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Background Purpose Methods Results Conclusions Low‐dose computed tomography (LDCT) can mitigate potential health risks to the public. However, the severe noise and artifacts in LDCT images can impede subsequent clinical diagnosis and analysis. Convolutional neural networks (CNNs) and Transformers stand out as the two most popular backbones in LDCT denoising. Nonetheless, CNNs suffer from a lack of long‐range modeling capabilities, while Transformers are hindered by high computational complexity.In this study, our main goal is to develop a simple and efficient model that can both focus on local spatial context and model long‐range dependencies with linear computational complexity for LDCT denoising.In this study, we make the first attempt to apply the State Space Model to LDCT denoising and propose a novel LDCT denoising model named Visual Mamba Encoder‐Decoder Network (ViMEDnet). To efficiently and effectively capture both the local and global features, we propose the Mixed State Space Module (MSSM), where the depth‐wise convolution, max‐pooling, and 2D Selective Scan Module (2DSSM) are coupled together through a partial channel splitting mechanism. 2DSSM is capable of capturing global information with linear computational complexity, while convolution and max‐pooling can effectively learn local signals to facilitate detail restoration. Furthermore, the network uses a weighted gradient‐sensitive hybrid loss function to facilitate the preservation of image details, improving the overall denoising performance.The performance of our proposed ViMEDnet is compared to five state‐of‐the‐art LDCT denoising methods, including an iterative algorithm, two CNN‐based methods, and two Transformer‐based methods. The comparative experimental results demonstrate that the proposed ViMEDnet can achieve better visual quality and quantitative assessment outcomes. In visual evaluation, ViMEDnet effectively removes noise and artifacts, while exhibiting superior performance in restoring fine structures and low‐contrast structural edges, resulting in minimal deviation of denoised images from NDCT. In quantitative assessment, ViMEDnet obtains the lowest RMSE and the highest PSNR, SSIM, and FSIM scores, further substantiating the superiority of ViMEDnet.The proposed ViMEDnet possesses excellent LDCT denoising performance and provides a new alternative to LDCT denoising models beyond the existing CNN and Transformer options. [ABSTRACT FROM AUTHOR]

Background: A variety of deep learning‐based and iterative approaches are available to predict Tracer Kinetic (TK) parameters from fully sampled or undersampled dynamic contrast‐enhanced (DCE) MRI data. However, both the methods offer distinct benefits and drawbacks. Purpose: To propose a hybrid algorithm (named as 'Greybox'), using both model‐ as well as DL‐based, for solving a multi‐parametric non‐linear inverse problem of directly estimating TK parameters from undersampled DCE MRI data, which is invariant to undersampling rate. Methods: The proposed algorithm was inspired by plug‐and‐play algorithms used for solving linear inverse imaging problems. This technique was tested for its effectiveness in solving the nonlinear ill‐posed inverse problem of generating 3D TK parameter maps from four‐dimensional (4D; Spatial + Temporal) retrospectively undersampled k‐space data. The algorithm learns a deep learning‐based prior using UNET to estimate the Ktrans$\mathbf {K_{trans}}$ and Vp$\mathbf {V_{p}}$ parameters based on the Patlak pharmacokinetic model, and this trained prior was utilized to estimate the TK parameter maps using an iterative gradient‐based optimization scheme. Unlike the existing DL models, this network is invariant to the undersampling rate of the input data. The proposed method was compared with the total variation‐based direct reconstruction technique on brain, breast, and prostate DCE‐MRI datasets for various undersampling rates using the Radial Golden Angle (RGA) scheme. For the breast dataset, an indirect estimation using the Fast Composite Splitting algorithm was utilized for comparison. Undersampling rates of 8×$\times$, 12×$\times$ and 20×$\times$ were used for the experiments, and the results were compared using the PSNR and SSIM as metrics. For the breast dataset of 10 patients, data from four patients were utilized for training (1032 samples), two for validation (752 samples), and the entire volume of four patients for testing. Similarly, for the prostate dataset of 18 patients, 10 patients were utilized for training (720 samples), five for validation (216 samples), and the whole volume of three patients for testing. For the brain dataset of nineteen patients, ten patients were used for training (3152 samples), five for validation (1168 samples), and the whole volume of four patients for testing. Statistical tests were also conducted to assess the significance of the improvement in performance. Results: The experiments showed that the proposed Greybox performs significantly better than other direct reconstruction methods. The proposed algorithm improved the estimated Ktrans$\mathbf {K_{trans}}$ and Vp$\mathbf {V_{p}}$ in terms of the peak signal‐to‐noise ratio by up to 3 dB compared to other standard reconstruction methods. Conclusion: The proposed hybrid reconstruction algorithm, Greybox, can provide state‐of‐the‐art performance in solving the nonlinear inverse problem of DCE‐MRI. This is also the first of its kind to utilize convolutional neural network‐based encodings as part of the plug‐and‐play priors to improve the performance of the reconstruction algorithm. [ABSTRACT FROM AUTHOR]

BACKGROUND: This study reports the results of a set of discrimination experiments using simulated images that represent the appearance of subtle lesions in low-dose computed tomography (CT) of the lungs. Noise in these images has a characteristic ramp-spectrum before apodization by noise control filters. We consider three specific diagnostic features that determine whether a lesion is considered malignant or benign, two system-resolution levels, and four apodization levels for a total of 24 experimental conditions. PURPOSE: The goal of the investigation is to better understand how well human observers perform subtle discrimination tasks like these, and the mechanisms of that performance. We use a forced-choice psychophysical paradigm to estimate observer efficiency and classification images. These measures quantify how effectively subjects can read the images, and how they use images to perform discrimination tasks across the different imaging conditions. MATERIALS AND METHODS: The simulated CT images used as stimuli in the psychophysical experiments are generated from high-resolution objects passed through a modulation transfer function (MTF) before down-sampling to the image-pixel grid. Acquisition noise is then added with a ramp noise-power spectrum (NPS), with subsequent smoothing through apodization filters. The features considered are lesion size, indistinct lesion boundary, and a nonuniform lesion interior. System resolution is implemented by an MTF with resolution (10% max.) of 0.47 or 0.58 cyc/mm. Apodization is implemented by a Shepp-Logan filter (Sinc profile) with various cutoffs. Six medically naïve subjects participated in the psychophysical studies, entailing training and testing components for each condition. Training consisted of staircase procedures to find the 80% correct threshold for each subject, and testing involved 2000 psychophysical trials at the threshold value for each subject. Human-observer performance is compared to the Ideal Observer to generate estimates of task efficiency. The significance of imaging factors is assessed using ANOVA. Classification images are used to estimate the linear template weights used by subjects to perform these tasks. Classification-image spectra are used to analyze subject weights in the spatial-frequency domain. RESULTS: Overall, average observer efficiency is relatively low in these experiments (10%-40%) relative to detection and localization studies reported previously. We find significant effects for feature type and apodization level on observer efficiency. Somewhat surprisingly, system resolution is not a significant factor. Efficiency effects of the different features appear to be well explained by the profile of the linear templates in the classification images. Increasingly strong apodization is found to both increase the classification-image weights and to increase the mean-frequency of the classification-image spectra. A secondary analysis of Unapodized classification images shows that this is largely due to observers undoing (inverting) the effects of apodization filters. CONCLUSIONS: These studies demonstrate that human observers can be relatively inefficient at feature-discrimination tasks in ramp-spectrum noise. Observers appear to be adapting to frequency suppression implemented in apodization filters, but there are residual effects that are not explained by spatial weighting patterns. The studies also suggest that the mechanisms for improving performance through the application of noise-control filters may require further investigation.

Background: In boron neutron capture therapy (BNCT)—a form of binary radiotherapy—the primary challenge in treatment planning systems for dose calculations arises from the time‐consuming nature of the Monte Carlo (MC) method. Recent progress, including the use of neural networks (NN), has been made to accelerate BNCT dose calculations. However, this approach may result in significant dose errors in both the tumor and the skin, with the latter being a critical organ in BNCT. Furthermore, owing to the lack of physical processes in purely NN‐based approaches, their reliability for clinical dose calculations in BNCT is questionable. Purpose: In this study, a physically constrained MC–NN (PCMC–NN) coupling algorithm is proposed to achieve fast and accurate computation of the BNCT three‐dimensional (3D) therapeutic dose distribution. This approach synergizes the high precision of the MC method with the speed of the NN and utilizes physical conservation laws to constrain the coupling process. It addresses the time‐consuming issue of the traditional MC method while reducing dose errors. Methods: Clinical data were collected from 113 glioblastoma patients. For each patient, the 3D dose distributions for both the coarse and detailed dose grids were calculated using the MC code PHITS. Among these patients, the data from 14 patients were allocated to the test set, 9 to the validation set, and the remaining to the training set. A neural network, 3D‐Unet, was built based on the coarse grid dose and patient CT information to enable fast and accurate computation of the 3D detailed grid dose distribution of BNCT. Results: Statistical evaluations, including relative deviation, dose deviation, mean absolute error (MAE), and mean absolute percentage error (MAPE) were conducted. Our findings suggested that the PCMC–NN algorithm substantially outperformed the traditional NN and interpolation methods. Furthermore, the proposed algorithm significantly reduced errors, particularly in the skin and GTV, and improved computational accuracy (hereinafter referred to simply as 'accuracy') with a MAPE range of 1.6%–4.0% and a maximum MAE of 0.3 Gy (IsoE) for different organs. The dose–volume histograms generated by the PCMC–NN aligned well with those obtained from the MC method, further validating its accuracy. Conclusions: The PCMC–NN algorithm enhanced the speed and accuracy of BNCT dose calculations by combining the MC method with the NN algorithm. This indicates the significant potential of the proposed algorithm for clinical applications in optimizing treatment planning. [ABSTRACT FROM AUTHOR]

Purpose: Deformable image registration (DIR) is a key enabling technology in many diagnostic and therapeutic tasks, but often does not meet the required robustness and accuracy for supporting clinical tasks. This is in large part due to a lack of high‐quality benchmark datasets by which new DIR algorithms can be evaluated. Our team was supported by the National Institute of Biomedical Imaging and Bioengineering to develop DIR benchmark dataset libraries for multiple anatomical sites, comprising of large numbers of highly accurate landmark pairs on matching blood vessel bifurcations. Here we introduce our lung CT DIR benchmark dataset library, which was developed to improve upon the number and distribution of landmark pairs in current public lung CT benchmark datasets. Acquisition and Validation Methods: Thirty CT image pairs were acquired from several publicly available repositories as well as authors' institution with IRB approval. The data processing workflow included multiple steps: (1) The images were denoised. (2) Lungs, airways, and blood vessels were automatically segmented. (3) Bifurcations were directly detected on the skeleton of the segmented vessel tree. (4) Falsely identified bifurcations were filtered out using manually defined rules. (5) A DIR was used to project landmarks detected on the first image onto the second image of the image pair to form landmark pairs. (6) Landmark pairs were manually verified. This workflow resulted in an average of 1262 landmark pairs per image pair. Estimates of the landmark pair target registration error (TRE) using digital phantoms were 0.4 mm ± 0.3 mm. Data Format and Usage Notes: The data is published in Zenodo at https://doi.org/10.5281/zenodo.8200423. Instructions for use can be found at https://github.com/deshanyang/Lung‐DIR‐QA. Potential Applications: The dataset library generated in this work is the largest of its kind to date and will provide researchers with a new and improved set of ground truth benchmarks for quantitatively validating DIR algorithms within the lung. [ABSTRACT FROM AUTHOR]

Background: CT reconstruction is of essential importance in medical imaging. In 2022, the American Association of Physicists in Medicine (AAPM) sponsored a Grand Challenge to investigate the challenging inverse problem of spectral CT reconstruction, with the aim of achieving the most accurate reconstruction results. The authors of this paper participated in the challenge and won as a runner‐up team. Purpose: This paper reports details of our PROSPECT algorithm (Prior‐based Restricted‐variable Optimization for SPEctral CT) and follow‐up studies regarding the algorithm's accuracy and enhancement of its convergence speed. Methods: We formulated the reconstruction task as an optimization problem. PROSPECT employed a one‐step backward iterative scheme to solve this optimization problem by allowing estimation of and correction for the difference between the actual polychromatic projection model and the monochromatic model used in the optimization problem. PROSPECT incorporated various forms of prior information derived by analyzing training data provided by the Grand Challenge to reduce the number of unknown variables. We investigated the impact of projection data precision on the resulting solution accuracy and improved convergence speed of the PROSPECT algorithm by incorporating a beam‐hardening correction (BHC) step in the iterative process. We also studied the algorithm's performance under noisy projection data. Results: Prior knowledge allowed a reduction of the number of unknown variables by 85.9%$85.9\%$. PROSPECT algorithm achieved the average root of mean square error (RMSE) of 3.3×10−6$3.3\,\times \,10^{-6}$ in the test data set provided by the Grand Challenge. Performing the reconstruction with the same algorithm but using double‐precision projection data reduced RMSE to 1.2×10−11$1.2\,\times \,10^{-11}$. Including the BHC step in the PROSPECT algorithm accelerated the iteration process with a 40% reduction in computation time. Conclusions: PROSPECT algorithm achieved a high degree of accuracy and computational efficiency. [ABSTRACT FROM AUTHOR]

BACKGROUND: Accurate detection and grading of atheromatous stenotic lesions within the cardiac, renal, and intracranial vasculature is imperative for early recognition of disease and guiding treatment strategies. PURPOSE: In this work, a stenotic lesion phantom was used to compare high resolution and normal resolution modes on the same CT scanner in terms of detection and size discrimination performance. MATERIALS AND METHODS: The phantom is comprised of three acrylic cylinders (each 15.0 cm in diameter and 1.3 cm thick) with a matching array of holes in each module. The outer two modules contain holes that are slightly larger than the corresponding hole in the central module to simulate stenotic narrowing in vasculature. The stack of modules was submerged in an iodine solution simulating contrast-enhanced stenotic lesions with a range of lumen diameters (1.32-10.08 mm) and stenosis severity (0%, 50%, 60%, 70%, and 80%). The phantom was imaged on the Canon Aquilion Precision high-resolution CT scanner in high-resolution (HR) mode (0.25 mm × 0.50 mm detector element size) and normal-resolution (NR) mode (0.50 mm × 0.50 mm) using 120 kV and two dose levels (14 and 21 mGy SSDE) with 30 repeat scans acquired for each combination. Filtered back-projection (FBP) and a hybrid-iterative reconstruction (AIDR) were used with the FC18 kernel, as well as a deep learning algorithm (AiCE) which is only available for HR. A non-prewhitening model observer with an eye filter was implemented to quantify performance for detection and size discrimination tasks in the axial plane. RESULTS: Detection performance improved with increasing diameter, dose, and for AIDR in comparison to FBP for a fixed resolution mode. Performance in the HR mode was generally higher than NR for the smaller lumen diameters (1-5 mm) with decreasing differences as the diameter increased. Performance in NR mode surpassed HR mode for lumen diameters greater than ∼4 mm and ∼5 mm for 14 mGy and 21 mGy, respectively. AiCE provided consistently higher detection performance compared with AIDR-FC18 (48% higher for a 6 mm lumen diameter). Discrimination performance increased with increasing nominal diameter, dose, and for larger differences in stenosis severity. When comparing discrimination performance in HR to NR modes, the largest relative differences occur at the smallest nominal diameters and smallest differences in stenosis severity. The AiCE reconstruction algorithm produced the highest overall discrimination performance values, and these were significantly higher than AIDR-FC18 for nominal diameters of 7.14 and 10.08 mm. CONCLUSIONS: HR mode outperforms NR for detection up to a specific diameter and the results improve with AiCE and for higher dose levels. For the task of size discrimination, HR mode consistently outperforms NR if AIDR-FC18 is used for dose levels of at least 21 mGy, and the results improve with AiCE and for the smallest differences in stenosis severity investigated (50% vs. 60%). High-resolution CT appears to be beneficial for detecting smaller simulated lumen diameters (

BackgroundMotion during data acquisition leads to artifacts in computed tomography (CT) reconstructions. In cases such as cardiac imaging, not only is motion unavoidable, but evaluating the motion of the object is of clinical interest. Reducing motion artifacts has typically been achieved by developing systems with faster gantry rotation or via algorithms which measure and/or estimate the displacement. However, these approaches have had limited success due to both physical constraints as well as the challenge of estimating non-rigid, temporally varying, and patient-specific motion fields.PurposeTo develop a novel reconstruction method which generates time-resolved, artifact-free images without estimation or explicit modeling of the motion.MethodsWe describe an analysis-by-synthesis approach which progressively regresses a solution consistent with the acquired sinogram. In our method, we focus on the movement of object boundaries. Not only are the boundaries the source of image artifacts, but object boundaries can simultaneously be used to represent both the object as well as its motion over time without the need for an explicit motion model. We represent the object boundaries via a signed distance function (SDF) which can be efficiently modeled using neural networks. As a result, optimization can be performed under spatial and temporal smoothness constraints without the need for explicit motion estimation.ResultsWe illustrate the utility of DiFiR-CT in three imaging scenarios with increasing motion complexity: translation of a small circle, heart-like change in an ellipse's diameter, and a complex topological deformation. Compared to filtered backprojection, DiFiR-CT provides high quality image reconstruction for all three motions without hyperparameter tuning or change to the architecture. We also evaluate DiFiR-CT's robustness to noise in the acquired sinogram and found its reconstruction to be accurate across a wide range of noise levels. Lastly, we demonstrate how the approach could be used for multi-intensity scenes and illustrate the importance of the initial segmentation providing a realistic initialization. Code and supplemental movies are available at https://kunalmgupta.github.io/projects/DiFiR-CT.html.ConclusionsProjection data can be used to accurately estimate a temporally-evolving scene without the need for explicit motion estimation using a neural implicit representation and analysis-by-synthesis approach.

BackgroundIn cone-beam computed tomography (CBCT)-guided radiotherapy, off-by-one vertebral-body misalignments are rare but serious errors that lead to wrong-site treatments.PurposeAn automatic error detection algorithm was developed that uses a three-branch convolutional neural network error detection model (EDM) to detect off-by-one vertebral-body misalignments using planning computed tomography (CT) images and setup CBCT images.MethodsAlgorithm training and test data consisted of planning CTs and CBCTs from 480 patients undergoing radiotherapy treatment in the thoracic and abdominal regions at two radiotherapy clinics. The clinically applied registration was used to derive true-negative (no error) data. The setup and planning images were then misaligned by one vertebral-body in both the superior and inferior directions, simulating the most likely misalignment scenarios. For each of the aligned and misaligned 3D image pairs, 2D slice pairs were automatically extracted in each anatomical plane about a point within the vertebral column. The three slice pairs obtained were then inputted to the EDM that returned a probability of vertebral misalignment. One model (EDM1 ) was trained solely on data from institution 1. EDM1 was further trained using a lower learning rate on a dataset from institution 2 to produce a fine-tuned model, EDM2 . Another model, EDM3 , was trained from scratch using a training dataset composed of data from both institutions. These three models were validated on a randomly selected and unseen dataset composed of images from both institutions, for a total of 303 image pairs. The model performances were quantified using a receiver operating characteristic analysis. Due to the rarity of vertebral-body misalignments in the clinic, a minimum threshold value yielding a specificity of at least 99% was selected. Using this threshold, the sensitivity was calculated for each model, on each institution's test set separately.ResultsWhen applied to the combined test set, EDM1 , EDM2 , and EDM3 resulted in an area under curve of 99.5%, 99.4%, and 99.5%, respectively. EDM1 achieved a sensitivity of 96% and 88% on Institution 1 and Institution 2 test set, respectively. EDM2 obtained a sensitivity of 95% on each institution's test set. EDM3 achieved a sensitivity of 95% and 88% on Institution 1 and Institution 2 test set, respectively.ConclusionThe proposed algorithm demonstrated accuracy in identifying off-by-one vertebral-body misalignments in CBCT-guided radiotherapy that was sufficiently high to allow for practical implementation. It was found that fine-tuning the model on a multi-facility dataset can further enhance the generalizability of the algorithm.

Background: Computed tomography (CT)‐based positron emission tomography (PET) attenuation correction (AC) is a commonly used method in PET AC. However, the CT truncation caused by the subject's limbs outside the CT field‐of‐view (FOV) leads to errors in PET AC. Purpose: In order to enhance the quantitative accuracy of PET imaging in the all‐digital DigitMI 930 PET/CT system, we assessed the impact of FOV truncation on its image quality and investigated the effectiveness of geometric shape‐based FOV extension algorithms in this system. Methods: We implemented two geometric shape‐based FOV extension algorithms. By setting the data from different numbers of detector channels on either side of the sinogram to zero, we simulated various levels of truncation. Specific regions of interest (ROI) were selected, and the mean values of these ROIs were calculated to visually compare the differences between truncated CT, CT extended using the FOV extension algorithms, and the original CT. Furthermore, we conducted statistical analyses on the mean and standard deviation of residual maps between truncated/extended CT and the original CT at different levels of truncation. Subsequently, similar data processing was applied to PET images corrected using original CT and those corrected using simulated truncated and extended CT images. This allowed us to evaluate the influence of FOV truncation on the images produced by the DigitMI 930 PET/CT system and assess the effectiveness of the FOV extension algorithms. Results: Truncation caused bright artifacts at the CT FOV edge and a slight increase in pixel values within the FOV. When using truncated CT data for PET AC, the PET activity outside the CT FOV decreased, while the extension algorithm effectively reduced these effects. Patient data showed that the activity within the CT FOV decreased by 60% in the truncated image compared to the base image, but this number could be reduced to at least 17.3% after extension. Conclusion: The two geometric shape‐based algorithms effectively eliminate CT truncation artifacts and restore the true distribution of CT shape and PET emission data outside the FOV in the all‐digital DigitMI 930 PET/CT system. These two algorithms can be used as basic solutions for CT FOV extension in all‐digital PET/CT systems. [ABSTRACT FROM AUTHOR]

PurposeStandard four-dimensional computed tomography (4DCT) cardiac reconstructions typically include spiraling artifacts that depend not only on the motion of the heart but also on the gantry angle range over which the data was acquired. We seek to reduce these motion artifacts and, thereby, improve the accuracy of left ventricular wall positions in 4DCT image series.MethodsWe use a motion artifact reduction approach (ResyncCT) that is based largely on conjugate pairs of partial angle reconstruction (PAR) images. After identifying the key locations where motion artifacts exist in the uncorrected images, paired subvolumes within the PAR images are analyzed with a modified cross-correlation function in order to estimate 3D velocity and acceleration vectors at these locations. A subsequent motion compensation process (also based on PAR images) includes the creation of a dense motion field, followed by a backproject-and-warp style compensation. The algorithm was tested on a 3D printed phantom, which represents the left ventricle (LV) and on challenging clinical cases corrupted by severe artifacts.ResultsThe results from our preliminary phantom test as well as from clinical cardiac scans show crisp endocardial edges and resolved double-wall artifacts. When viewed as a temporal series, the corrected images exhibit a much smoother motion of the LV endocardial boundary as compared to the uncorrected images. In addition, quantitative results from our phantom studies show that ResyncCT processing reduces endocardial surface distance errors from 0.9 ± 0.8 to 0.2 ± 0.1 mm.ConclusionsThe ResyncCT algorithm was shown to be effective in reducing motion artifacts and restoring accurate wall positions. Some perspectives on the use of conjugate-PAR images and on techniques for CT motion artifact reduction more generally are also given.

Background: The Dynamic Collimation System (DCS) has been shown to produce superior treatment plans to uncollimated pencil beam scanning (PBS) proton therapy using an in‐house treatment planning system (TPS) designed for research. Clinical implementation of the DCS requires the development and benchmarking of a rigorous dose calculation algorithm that accounts for pencil beam trimming, performs monitor unit calculations to produce deliverable plans at all beam energies, and is ideally implemented with a commercially available TPS. Purpose: To present an analytical Pencil bEam TRimming Algorithm (PETRA) for the DCS, with and without its range shifter, implemented in the Astroid TPS (.decimal, Sanford, Florida, USA). Materials: PETRA was derived by generalizing an existing pencil beam dose calculation model to account for the DCS‐specific effects of lateral penumbra blurring due to the nickel trimmers in two different planes, integral depth dose variation due to the trimming process, and the presence and absence of the range shifter. Tuning parameters were introduced to enable agreement between PETRA and a measurement‐validated Dynamic Collimation Monte Carlo (DCMC) model of the Miami Cancer Institute's IBA Proteus Plus system equipped with the DCS. Trimmer position, spot position, beam energy, and the presence or absence of a range shifter were all used as variables for the characterization of the model. The model was calibrated for pencil beam monitor unit calculations using procedures specified by International Atomic Energy Agency Technical Report Series 398 (IAEA TRS‐398). Results: The integral depth dose curves (IDDs) for energies between 70 MeV and 160 MeV among all simulated trimmer combinations, with and without the ranger shifter, agreed between PETRA and DCMC at the 1%/1 mm 1‐D gamma criteria for 99.99% of points. For lateral dose profiles, the median 2‐D gamma pass rate for all profiles at 1.5%/1.5 mm was 99.99% at the water phantom surface, plateau, and Bragg peak depths without the range shifter and at the surface and Bragg peak depths with the range shifter. The minimum 1.5%/1.5 mm gamma pass rates for the 2‐D profiles at the water phantom surface without and with the range shifter were 98.02% and 97.91%, respectively, and, at the Bragg peak, the minimum pass rates were 97.80% and 97.5%, respectively. Conclusion: The PETRA model for DCS dose calculations was successfully defined and benchmarked for use in a commercially available TPS. [ABSTRACT FROM AUTHOR]

PurposeDesigning and optimizing scintillator-based gamma detector using Monte Carlo simulation is of great importance in nuclear medicine and high energy physics. In scintillation detectors, understanding the light transport in the scintillator and the light collection by the photodetector plays a crucial role in achieving high performance. Thus, accurately modeling them is critical.MethodsIn previous works, we developed a model to compute crystal reflectance from the crystal 3D surface measurement and store it in look-up tables to be used in the Monte Carlo simulation software GATE. The relative light output comparison showed excellent agreement between simulations and experiments for both polished and rough surfaces in several configurations, that is, without and with reflector. However, when comparing them at the irradiation depth closest to the photodetector face, rough crystals with a reflector overestimated the predicted light output. Investigating the cause of this overestimation, we optimized the LUT algorithm to improve the reflectance computation accuracy, especially for rough surfaces. However, optical Monte Carlo simulations carried out with these newly generated LUTs still overestimate the light output. Based on previous observations, one probable cause is the erroneous assumption of perfect couplings between the reflector and crystal and between the crystal and photodetector, which likely results in an important overestimation of the light output compared to experimental values. In practice, several factors could degrade it. Here, we investigated possible suboptimal optical experimental configurations that could lead to a degraded light collection when using Teflon or ESR reflectors coupled to the crystal with air or grease. We generated look-up tables with a mixture of air and grease and showed the effect of three possible sources of light loss: the presence of a small gap between the crystal and the reflector edges close to the photodetector face, the infiltration of grease in the crystal-reflector coupling, and the presence of inhomogeneities in the photodetector-crystal interface.ResultsThe strongest effect is linked to the presence of a small gap of grease between the edges of the reflector material and the crystal (light loss of 10%-12% for 0.2 mm gap). The optical grease infiltrating the crystal-reflector air coupling decreases the light output, depending on the infiltration's extent and the amount of grease infiltrated. Five percent of air in the crystal-photodetector coupling can cause a light output decrease of 2% to 4%. The individual and combined effect of these advanced models can explain the discrepancy of the relative light output obtained with ESR in simulations and experiments. With Teflon, the study indicates that the light output loss strongly depends on the reflectance deterioration caused by grease absorption.ConclusionsOur results indicate that when studying scintillation detector performance with different finishes, performing simulations in ideal coupling conditions can lead to light output overestimation. To perform an accurate light output comparison and ultimately have a reliable detector performance estimation, all potential sources of practical limitations must be carefully considered. To broadly enable high-fidelity modeling, we developed an interface for users to compute their own LUTs, using their surface, scintillator, and reflector characteristics.

PurposeMissing or discrepant imaging volume is a common challenge in deformable image registration (DIR). To minimize the adverse impact, we train a neural network to synthesize cropped portions of head and neck CT's and then test its use in DIR.MethodsUsing a training dataset of 409 head and neck CT's, we trained a generative adversarial network to take in a cropped 3D image and output an image with synthesized anatomy in the cropped region. The network used a 3D U-Net generator along with Visual Geometry Group (VGG) deep feature losses. To test our technique, for each of the 53 test volumes, we used Elastix to deformably register combinations of a randomly cropped, full, and synthetically full volume to a single cropped, full, and synthetically full target volume. We additionally tested our method's robustness to crop extent by progressively increasing the amount of cropping, synthesizing the missing anatomy using our network, and then performing the same registration combinations. Registration performance was measured using 95% Hausdorff distance across 16 contours.ResultsWe successfully trained a network to synthesize missing anatomy in superiorly and inferiorly cropped images. The network can estimate large regions in an incomplete image, far from the cropping boundary. Registration using our estimated full images was not significantly different from registration using the original full images. The average contour matching error for full image registration was 9.9 mm, whereas our method was 11.6, 12.1, and 13.6 mm for synthesized-to-full, full-to-synthesized, and synthesized-to-synthesized registrations, respectively. In comparison, registration using the cropped images had errors of 31.7 mm and higher. Plotting the registered image contour error as a function of initial preregistered error shows that our method is robust to registration difficulty. Synthesized-to-full registration was statistically independent of cropping extent up to 18.7 cm superiorly cropped. Synthesized-to-synthesized registration was nearly independent, with a -0.04 mm of change in average contour error for every additional millimeter of cropping.ConclusionsDifferent or inadequate in scan extent is a major cause of DIR inaccuracies. We address this challenge by training a neural network to complete cropped 3D images. We show that with image completion, the source of DIR inaccuracy is eliminated, and the method is robust to varying crop extent.

Background: Artifact reduction or removal is a challenging task when the artifact creation physics are not well modeled mathematically. One of such situations is metal artifacts in x‐ray CT when the metallic material is unknown, and the x‐ray spectrum is wide. Purpose: A neural network is used to act as the objective function for iterative artifact reduction when the artifact model is unknown. Methods: A hypothetical unpredictable projection data distortion model is used to illustrate the proposed approach. The model is unpredictable, because it is controlled by a random variable. A convolutional neural network is trained to recognize the artifacts. The trained network is then used to compute the objective function for an iterative algorithm, which tries to reduce the artifacts in a computed tomography (CT) task. The objective function is evaluated in the image domain. The iterative algorithm for artifact reduction is in the projection domain. A gradient descent algorithm is used for the objective function optimization. The associated gradient is calculated with the chain rule. Results: The learning curves illustrate the decreasing treads of the objective function as the number of iterations increases. The images after the iterative treatment show the reduction of artifacts. A quantitative metric, the Sum Square Difference (SSD), also indicates the effectiveness of the proposed method. Conclusion: The methodology of using a neural network as an objective function has potential value for situations where a human developed model is difficult to describe the underlying physics. Real‐world applications are expected to be benefit from this methodology. [ABSTRACT FROM AUTHOR]

Background: In silico testing of novel image reconstruction and quantitative algorithms designed for interventional imaging requires realistic high‐resolution modeling of arterial trees with contrast dynamics. Furthermore, data synthesis for training of deep learning algorithms requires that an arterial tree generation algorithm be computationally efficient and sufficiently random. Purpose: The purpose of this paper is to provide a method for anatomically and physiologically motivated, computationally efficient, random hepatic arterial tree generation. Methods: The vessel generation algorithm uses a constrained constructive optimization approach with a volume minimization‐based cost function. The optimization is constrained by the Couinaud liver classification system to assure a main feeding artery to each Couinaud segment. An intersection check is included to guarantee non‐intersecting vasculature and cubic polynomial fits are used to optimize bifurcation angles and to generate smoothly curved segments. Furthermore, an approach to simulate contrast dynamics and respiratory and cardiac motion is also presented. Results: : The proposed algorithm can generate a synthetic hepatic arterial tree with 40 000 branches in 11 s. The high‐resolution arterial trees have realistic morphological features such as branching angles (MAD with Murray's law =1.2±1.2o$ = \;1.2 \pm {1.2^o}$), radii (median Murray deviation =0.08$ = \;0.08$), and smoothly curved, non‐intersecting vessels. Furthermore, the algorithm assures a main feeding artery to each Couinaud segment and is random (variability = 0.98 ± 0.01). Conclusions: This method facilitates the generation of large datasets of high‐resolution, unique hepatic angiograms for the training of deep learning algorithms and initial testing of novel 3D reconstruction and quantitative algorithms designed for interventional imaging. [ABSTRACT FROM AUTHOR]

Background: With the development of low‐dose computed tomography (CT), incomplete data reconstruction has been widely concerned. The total variation (TV) minimization algorithm can accurately reconstruct images from sparse or noisy data. Purpose: However, the traditional TV algorithm ignores the direction of structures in images, leading to the loss of edge information and block artifacts when the object is not piecewise constant. Since the anisotropic information can facilitate preserving the edge and detail information in images, we aim to improve the TV algorithm in terms of reconstruction accuracy via this approach. Methods: In this paper, we propose an adaptive‐weighted high order total variation (awHOTV) algorithm. We construct the second order TV‐norm using the second order gradient, adapt the anisotropic edge property between neighboring image pixels, adjust the local image‐intensity gradient to keep edge information, and design the corresponding Chambolle‐Pock (CP) solving algorithm. Implementing the proposed algorithm, comprehensive studies are conducted in the ideal projection data experiment where the Structural similarity (SSIM), Root Mean Square Error (RMSE), Contrast to noise ratio (CNR), and modulation transform function (MTF) curves are utilized to evaluate the quality of reconstructed images in statism, structure, spatial resolution, and contrast, respectively. In the noisy data experiment, we further use the noise power spectrum (NPS) curve to evaluate the reconstructed images and compare it with other three algorithms. Results: We use the 2D slice in the XCAT phantom, 2D slice in TCIA Challenge data and FORBILD phantom as simulation phantoms and use real bird data for real verification. The results show that, compared with the traditional TV and FBP algorithms, the awHOTV has better performance in terms of RMSE, SSIM, and Pearson correlation coefficient (PCC) under the projected data with different sparsity. In addition, the awHOTV algorithm is robust against the noise of different intensities. Conclusions: The proposed awHOTV method can reconstruct the images with high accuracy under sparse or noisy data. The awHOTV solves the strip artifacts caused by sparse data in the FBP method. Compared with the TV method, the awHOTV can effectively suppress block artifacts and has good detail protection ability. [ABSTRACT FROM AUTHOR]

The introduction of model‐based dose calculation algorithms (MBDCAs) in brachytherapy provides an opportunity for a more accurate dose calculation and opens the possibility for novel, innovative treatment modalities. The joint AAPM, ESTRO, and ABG Task Group 186 (TG‐186) report provided guidance to early adopters. However, the commissioning aspect of these algorithms was described only in general terms with no quantitative goals. This report, from the Working Group on Model‐Based Dose Calculation Algorithms in Brachytherapy, introduced a field‐tested approach to MBDCA commissioning. It is based on a set of well‐characterized test cases for which reference Monte Carlo (MC) and vendor‐specific MBDCA dose distributions are available in a Digital Imaging and Communications in Medicine—Radiotherapy (DICOM‐RT) format to the clinical users. The key elements of the TG‐186 commissioning workflow are now described in detail, and quantitative goals are provided. This approach leverages the well‐known Brachytherapy Source Registry jointly managed by the AAPM and the Imaging and Radiation Oncology Core (IROC) Houston Quality Assurance Center (with associated links at ESTRO) to provide open access to test cases as well as step‐by‐step user guides. While the current report is limited to the two most widely commercially available MBDCAs and only for 192Ir‐based afterloading brachytherapy at this time, this report establishes a general framework that can easily be extended to other brachytherapy MBDCAs and brachytherapy sources. The AAPM, ESTRO, ABG, and ABS recommend that clinical medical physicists implement the workflow presented in this report to validate both the basic and the advanced dose calculation features of their commercial MBDCAs. Recommendations are also given to vendors to integrate advanced analysis tools into their brachytherapy treatment planning system to facilitate extensive dose comparisons. The use of the test cases for research and educational purposes is further encouraged. [ABSTRACT FROM AUTHOR]

Background: With advanced x‐ray source and detector technologies being continuously developed, non‐traditional CT geometries have been widely explored. Generalized‐Equiangular Geometry CT (GEGCT) architecture, in which an x‐ray source might be positioned radially far away from the focus of arced detector array that is equiangularly spaced, is of importance in many novel CT systems and designs. Purpose: GEGCT, unfortunately, has no theoretically exact and shift‐invariant analytical image reconstruction algorithm in general. In this study, to obtain fast and accurate reconstruction from GEGCT and to promote its system design and optimization, an in‐depth investigation on a group of approximate Filtered Back‐Projection (FBP) algorithms with a variety of weighting strategies has been conducted. Methods: The architecture of GEGCT is first presented and characterized by using a normalized‐radial‐offset distance (NROD). Next, shift‐invariant weighted FBP‐type algorithms are derived in a unified framework, with pre‐filtering, filtering, and post‐filtering weights, for both fixed and dynamic NROD configurations. Three viable weighting strategies are then presented including a classic one developed by Besson in the literature and two new ones generated from a curvature fitting and from an empirical formula, where all of the three weights can be expressed as certain functions of NROD. After that, an analysis of reconstruction accuracy is conducted with a wide range of NROD. Finally, the weighted FBP algorithm for GEGCT is extended to a three‐dimensional form in the case of cone‐beam scan with a cylindrical detector array. Results: Theoretical analysis and numerical study show that weights in the shift‐invariant FBP algorithms can guarantee highly accurate reconstruction for GEGCT. A simulation of Shepp‐Logan phantom and a GEGCT scan of lung mimicked by using a clinical lung CT dataset both demonstrate that FBP reconstructions with Besson and polynomial weights can achieve excellent image quality, with Peak Signal to Noise Ratio and Structural Similarity being at the same level as that from the standard equiangular fan‐beam CT scan. Reconstruction of a cylinder object with multiple contrasts from simulated GEGCT scan with dynamic NROD is also highly consistent with fixed ones when using the Besson and polynomial weights, with root mean square error less than 7 hounsfield units, demonstrating the robustness and flexibility of the presented FBP algorithms. In terms of resolution, the direct FBP methods for GEGCT could achieve 1.35 lp/mm of spatial resolution at 10% modulation transfer functions point, higher than that of the rebinning method which can only reach 1.14 lp/mm. Moreover, 3D reconstructions of a disc phantom reveal that a greater value of NROD for GEGCT will bring less cone beam artifacts as expected. Conclusions: We propose the concept of GEGCT and investigate the feasibility of using shift‐invariant weighted FBP‐type algorithms for reconstruction from GEGCT data without rebinning. A comprehensive analysis and phantom studies have been conducted to validate the effectiveness of proposed weighting strategies in a wide range of NROD for GEGCT with fixed and dynamic NROD. [ABSTRACT FROM AUTHOR]

Background: Intensity‐modulated proton therapy (IMPT) is a well‐known delivery method of proton therapy. Besides higher plan quality, reducing the delivery time is also essential to IMPT plans. It can enhance patient comfort, reduce treatment costs, and improve delivery efficiency. From the perspective of treatment efficacy, it contributes to mitigating the intra‐fractional motions and improving the accuracy of radiotherapy, especially for moving tumors. Purpose: However, there is a tradeoff problem between the plan quality and delivery time. We consider the potential of a large momentum acceptance (LMA) beamline and apply the spots and energy layers reduction method to reduce the delivery time. Methods: The delivery time for each field consists of the energy layer switching time, spot traveling time, and dose delivery time. The larger momentum spread and higher intensity beam offered by the LMA beamline contribute to reducing the total delivery time compared to the conventional beamline. In addition to the dose fidelity term, an L1 and logarithm items were added to the objective function to increase the sparsity of the low‐weighted spots and energy layers. After that, the low‐weighted spots and layers were iteratively excluded in the reduced plan, which reduced the energy layer switching time and spot traveling time. We used the standard, reduced, and LMA‐reduced plans to validate the proposed method and tested it on prostate and nasopharyngeal cases. Then, we compared and evaluated the plan quality, treatment time, and plan robustness against delivery uncertainty. Results: Compared with the standard plans, the number of spots in the LMA‐reduced plans was on average reduced by 13 400 (95.6%) for prostate cases and by 48 300 (80.7%) for nasopharyngeal cases and the number of energy layers was on average reduced by 49 (61.3%) for prostate cases and by 97 (50.5%) for nasopharyngeal cases. And, the delivery time of the LMA‐reduced plans was shortened from 34.5 to 8.6 s for prostate cases and from 163.8 to 53.6 s for nasopharyngeal cases. The LMA‐reduced plans had comparable robustness to the spot monitor unit (MU) error compared with the standard plans, but the LMA‐reduced plans became more sensitive to spot position uncertainty. Conclusion: The delivery efficiency can be significantly improved using the LMA beamline and spots and energy layers reduction strategies. The method is promising to improve the efficiency of motion mitigation strategies for treating moving tumors. [ABSTRACT FROM AUTHOR]

Purpose: To propose a markerless beam's eye view (BEV) motion monitoring algorithm, which works with the inferior quality megavolt (MV) images with multi‐leaf collimator (MLC) occlusion‐compatible. Methods: A thorax phantom was used to verify the accuracy of the algorithm. Lung tumor quality assurance (QA) plans were generated for the phantom, and delivered 10 times on the linear accelerator with manually treatment offsets in various directions. The algorithm was used to register 753 electronic portal imaging device (EPID) images with the appropriate digitally reconstructed radiograph (DRR), calculating a registration offset that was compared with the actual offset to determine the monitoring errors. Image similarity measure was used as an independent check. Additionally, patient data of 21 lung tumor treatment plans were gathered. A total of 533 pairs of patient images were acquired for motion monitoring study, to offer quantifiable data of the tumor position change during treatment. Results: The monitoring algorithm can process various degrees (10%–80%) of image loss, and performs well when dealing with non‐rigid registration for partial data images. About 86.8% of the monitoring errors are less than 3 mm in the algorithm verification of the phantom study, and about 80% of the errors are under than 2 mm. Normalized Mutual Information (NMI) of phantom images changes from 1.182 ± 0.026 to 1.202 ± 0.027, with p < 0.005, and the Hausdorff‐Distance (HD) changes from 3.506 ± 0.417 mm to 3.466 ± 0.473 mm, with p < 0.005. Translation with a displacement range of ‐6.0 mm to 6.2 mm is the predominant change of the patient target during treatment. NMI of patient images changes from 1.216 ± 0.031 to 1.225 ± 0.031, with p < 0.005, and HD changes from 3.131 ± 0.876 mm to 3.118 ± 0.038 mm, with p < 0.005. The dice index of target before and after registration is 0.264 ± 0.336, indicating the presence of non‐negligible non‐rigid deformation. Conclusions: The study provides a robust markerless motion monitoring algorithm for multi‐modal, partial data and inferior quality image processing. [ABSTRACT FROM AUTHOR]

PurposeRecent studies have demonstrated a lack of reproducibility of radiomic features in response to variations in CT parameters. In addition, reproducibility of radiomic features has not been well established in clinical datasets. We aimed to investigate the effects of a wide range of CT acquisition and reconstruction parameters on radiomic features in a realistic setting using clinical low dose lung cancer screening cases. We performed univariable and multivariable explorations to consider the effects of individual parameters and the simultaneous interactions between three different acquisition/reconstruction parameters of radiation dose level, reconstructed slice thickness, and kernel.MethodA cohort of 89 lung cancer screening patients were collected that each had a solid lung nodule >4mm diameter. A computational pipeline was used to perform a simulation of dose reduction of the raw projection data, collected from patient scans. This was followed by reconstruction of raw data with weighted filter back projection (wFBP) algorithm and automatic lung nodule detection and segmentation using a computer-aided detection tool. For each patient, 36 different image datasets were created corresponding to dose levels of 100%, 50%, 25%, and 10% of the original dose level, three slice thicknesses of 0.6 mm, 1 mm, and 2 mm, as well as three reconstruction kernels of smooth, medium, and sharp. For each nodule, 226 well-known radiomic features were calculated at each image condition. The reproducibility of radiomic features was first evaluated by measuring the intercondition agreement of the feature values among the 36 image conditions. Then in a series of univariable analyses, the impact of individual CT parameters was assessed by selecting subsets of conditions with one varying and two constant CT parameters. In each subset, intraparameter agreements were assessed. Overall concordance correlation coefficient (OCCC) served as the measure of agreement. An OCCC ≥ 0.9 implied strong agreement and reproducibility of radiomic features in intercondition or intraparameter comparisons. Furthermore, the interaction of CT parameters in impacting radiomic feature values was investigated via ANOVA.ResultsAll included radiomic features lacked intercondition reproducibility (OCCC 50% of radiomic features.ConclusionWe systematically explored the multidimensional space of CT parameters in affecting lung nodule radiomic features. Univariable and multivariable analyses of this study not only showed the lack of reproducibility of the majority of radiomic features but also revealed existing interactions among CT parameters, meaning that the effect of individual CT parameters on radiomic features can be conditional upon other CT acquisition and reconstruction parameters. Our findings advise on careful radiomic feature selection and attention to the inclusion criteria for CT image acquisition protocols within the datasets of radiomic studies.

PurposeThe need for high-fidelity modeling of radiation detectors to perform reliable detector performance optimization using Monte Carlo simulations requires to accurately simulate the light transport in the scintillator and the light collection by the photodetector. In this work, we implement our well-validated crystal reflectance model computed from three-dimensional (3D) crystal surface measurement in a standalone open-source application to allow researchers to generate fully customized crystal reflectance look-up-tables (LUTs) to be used in optical Monte Carlo simulation.MethodsThe LUTDavisModel application can be installed in a few minutes on Windows, macOS, and Linux, using 26 MB of space. MATLAB Runtime is required and is automatically installed with the application. The core algorithm has been previously validated experimentally and implemented in GATE v8.0. The standalone is divided into five panels, each of which performing a specific task: generate LUTs from a combination of surface type, scintillator, and coupling medium available in the database (such as LSO or BGO) or custom; compute LUTs with the reflectors available and custom coupling thickness; create a mixture of coupling media to account for possible defects in the optical coupling; plot precomputed LUTs for visual comparison. Tooltips and errors/warnings facilitate the navigation. The reported computational times were obtained with an Intel Core i7 MacBook Pro.ResultsLUTs can be generated with computational time ranging from a few minutes to several hours depending on the selected surface, sampling, and computational power. A longer time is needed when using rough surfaces and thick coupling media (hundreds of μ m ) due to increased photon tracking.ConclusionsWe developed a user-friendly standalone application to generate LUTs that can be used inside GATE Monte Carlo simulations. It can be easily downloaded, installed, and used. Future optimizations will expand the database, decrease the computational time through greater parallelization, and include the generation of LUTs to study Cerenkov photons transport.

PurposeTo develop and evaluate the diagnostic performance of an algorithm for multi-marker radiomic-based classification of breast masses in dedicated breast computed tomography (bCT) images.MethodsOver 1000 radiomic descriptors aimed at quantifying mass and border heterogeneity, morphology, and margin sharpness were developed and implemented. These included well-established texture and shape feature descriptors, which were supplemented with additional approaches for contour irregularity quantification, spicule and lobe detection, characterization of degree of infiltration, and differences in peritumoral compartments. All descriptors were extracted from a training set of 202 bCT masses (133 benign and 69 malignant), and their individual diagnostic performance was investigated in terms of area under the receiver operating characteristics (ROC) curve (AUC) of single-feature-based linear discriminant analysis (LDA) classifiers. Subsequently, the most relevant descriptors were selected through a multiple-step feature selection process (including stability analysis, statistical significance, evaluation of feature interaction, and dimensionality reduction), and used to develop a final LDA radiomic model for classification of benign and malignant masses, which was then tested on an independent test set of 82 cases (45 benign and 37 malignant).ResultsThe majority of the individual radiomic descriptors showed, on the training set, an AUC value deriving from a linear decision boundary higher than 0.65, with the lower limit of the associated 95% confidence interval (C.I.) not overlapping with random chance (AUC = 0.5). The final LDA radiomic model resulted in a test set AUC of 0.90 (95% C.I. 0.80-0.96).ConclusionsThe proposed multi-marker radiomic approach achieved high diagnostic accuracy in bCT mass classification, using a radiomic signature based on different feature types. While future studies with larger datasets are needed to further validate these results, quantitative radiomics applied to bCT shows potential to improve the breast cancer diagnosis pipeline.

This document is a correction notice for an article titled "Generation and comparison of 3D dosimetric reference datasets for COMS eye plaque brachytherapy using model-based dose calculations." The supplementary materials referenced in the article, which contain dosimetric comparisons for all test cases, were not previously published alongside the paper but are now attached. The article establishes the first test cases for low-energy photon-emitting brachytherapy using model-based dose calculation algorithms. The test cases range from simple to complex scenarios involving a 16 mm COMS eye plaque loaded with 13 125I seeds. The calculations were performed using three Monte Carlo codes and a research version of the OncentraBrachy ACE treatment planning system. The figures presented in the article demonstrate the agreement between different codes and systems for the test cases. [Extracted from the article]

PurposeRadiation dose delivered to targets located near the upper abdomen or thorax are significantly affected by respiratory motion, necessitating large margins, limiting dose escalation. Surrogate motion management devices, such as the Real-time Position Management (RPM™) system (Varian Medical Systems, Palo Alto, CA), are commonly used to improve normal tissue sparing. Alternative to current solutions, we have developed and evaluated the feasibility of a real-time position management system that leverages the motion data from the onboard hardware of Apple iOS devices to provide patients with visual coaching with the potential to improve the reproducibility of breathing as well as improve patient compliance and reduce treatment delivery time.Methods and materialsThe iOS application, coined the Instant Respiratory Feedback (IRF) system, was developed in Swift (Apple Inc., Cupertino, CA) using the Core-Motion library and implemented on an Apple iPhone® devices. Operation requires an iPhone®, a three-dimensional printed arm, and a radiolucent projector screen system for feedback. Direct comparison between IRF, which leverages sensor fusion data from the iPhone®, and RPM™, an optical-based system, was performed on multiple respiratory motion phantoms and volunteers. The IRF system and RPM™ camera tracking marker were placed on the same location allowing for simultaneous data acquisition. The IRF surrogate measurement of displacement was compared to the signal trace acquired using RPM™ with univariate linear regressions and Bland-Altman analysis.ResultsPeriodic motion shows excellent agreement between both systems, and subject motion shows good agreement during regular and irregular breathing motion. Comparison of IRF and RPM™ show very similar signal traces that were significantly related across all phantoms, including those motion with different amplitude and frequency, and subjects' waveforms (all r > 0.9, P

Background: Methods described in the literature for designing shielding for treatment rooms for radiotherapy systems often involve assumptions that lead to overestimations of the wall thicknesses required to meet dose rate constraints outside the room. The Leksell Gamma Knife (LGK) has built‐in shielding that results in primarily scattered photons leaking into the room. The field of leakage radiation, therefore, has a wide spectrum of energies, up to the primary energies of cobalt‐60, and is highly anisotropic, making standard site planning methods difficult to adapt to the LGK. Monte Carlo (MC) simulations are an alternative for dose calculations but are computationally expensive. Purpose: The aim was to develop a dose calculation algorithm for fast estimations of dose rates outside the barriers of a treatment room. The algorithm could then be employed in iterative methods to optimize treatment room parameters such as wall thicknesses and position of the LKG unit. Methods: The algorithm uses pre‐calculated MC simulation data in two steps. First, it uses phase spaces that describe single photons in the radiation field around the LGK. Using each photon's position and direction, they are raytraced to the outside of the room. Based on their energy, angle of incidence to the barrier, and the barrier's thickness, a depth‐dose profile is sampled from a pre‐calculated library of profiles. The contribution from each photon is added to the space outside the barriers, giving the total dose distribution. The barrier thicknesses are optimized by iteratively running the algorithm and finding the minimum thickness required to keep the resulting maximum dose rate outside below a given upper limit. Results: Comparisons between dose distributions from the dose algorithm and full MC simulations of two typical rooms show good agreement, with the maximum dose rate outside each barrier being estimated within what represents a 2 cm error in concrete barrier thickness. The algorithm is successfully used in optimizing the barrier thicknesses and the results show potential decreases in most barriers' thickness, compared to a conventional method. Conclusions: The algorithm is a promising alternative to conventional barrier design methods, eliminating several shortcomings of the latter whilst being significantly faster than a full MC simulation. The flexibility of the algorithm would further enable solving more complex cases, such as optimizing the LGK position or barrier material to further reduce material use and cost. [ABSTRACT FROM AUTHOR]

PurposeFluence-modulated proton computed tomography (FMpCT) using pencil beam scanning aims at achieving task-specific image noise distributions by modulating the imaging proton fluence spot-by-spot based on an object-specific noise model. In this work, we present a method for fluence field optimization and investigate its performance in dose reduction for various phantoms and image variance targets.MethodsThe proposed method uses Monte Carlo simulations of a proton CT (pCT) prototype scanner to estimate expected variance levels at uniform fluence. Using an iterative approach, we calculate a stack of target variance projections that are required to achieve the prescribed image variance, assuming a reconstruction using filtered backprojection. By fitting a pencil beam model to the ratio of uniform fluence variance and target variance, relative weights for each pencil beam can be calculated. The quality of the resulting fluence modulations is evaluated by scoring imaging doses and comparing them to those at uniform fluence, as well as evaluating conformity of the achieved variance with the prescription. For three different phantoms, we prescribed constant image variance as well as two regions-of-interest (ROI) imaging tasks with inhomogeneous image variance. The shape of the ROIs followed typical beam profiles for proton therapy.ResultsPrescription of constant image variance resulted in a dose reduction of 8.9% for a homogeneous water phantom compared to a uniform fluence scan at equal peak variance level. For a more heterogeneous head phantom, dose reduction increased to 16.0% for the same task. Prescribing two different ROIs resulted in dose reductions between 25.7% and 40.5% outside of the ROI at equal peak variance levels inside the ROI. Imaging doses inside the ROI were increased by 9.2% to 19.2% compared to the uniform fluence scan, but can be neglected assuming that the ROI agrees with the therapeutic dose region. Agreement of resulting variance maps with the prescriptions was satisfactory.ConclusionsWe developed a method for fluence field optimization based on a noise model for a real scanner used in pCT. We demonstrated that it can achieve prescribed image variance targets. A uniform fluence field was shown not to be dose optimal and dose reductions achievable with the proposed method for FMpCT were considerable, opening an interesting perspective for image guidance and adaptive therapy.

PurposeProton dose distribution is sensitive to uncertainties in range estimation and patient positioning. Currently, the proton robustness is managed by worst-case scenario optimization methods, which are computationally inefficient. To overcome these challenges, we develop a novel intensity-modulated proton therapy (IMPT) optimization method that integrates dose fidelity with a sensitivity term that describes dose perturbation as the result of range and positioning uncertainties.MethodsIn the integrated optimization framework, the optimization cost function is formulated to include two terms: a dose fidelity term and a robustness term penalizing the inner product of the scanning spot sensitivity and intensity. The sensitivity of an IMPT scanning spot to perturbations is defined as the dose distribution variation induced by range and positioning errors. To evaluate the sensitivity, the spatial gradient of the dose distribution of a specific spot is first calculated. The spot sensitivity is then determined by the total absolute value of the directional gradients of all affected voxels. The fast iterative shrinkage-thresholding algorithm is used to solve the optimization problem. This method was tested on three skull base tumor (SBT) patients and three bilateral head-and-neck (H&N) patients. The proposed sensitivity-regularized method (SenR) was implemented on both clinic target volume (CTV) and planning target volume (PTV). They were compared with conventional PTV-based optimization method (Conv) and CTV-based voxel-wise worst-case scenario optimization approach (WC).ResultsUnder the nominal condition without uncertainties, the three methods achieved similar CTV dose coverage, while the CTV-based SenR approach better spared organs at risks (OARs) compared with the WC approach, with an average reduction of [Dmean, Dmax] of [4.72, 3.38]  GyRBE for the SBT cases and [2.54, 3.33] GyRBE for the H&N cases. The OAR sparing of the PTV-based SenR method was comparable with the WC method. The WC method, and SenR approaches all improved the plan robustness from the conventional PTV-based method. On average, under range uncertainties, the lowest [D95%, V95%, V100%] of CTV were increased from [93.75%, 88.47%, 47.37%] in the Conv method, to [99.28%, 99.51%, 86.64%] in the WC method, [97.71%, 97.85%, 81.65%] in the SenR-CTV method and [98.77%, 99.30%, 85.12%] in the SenR-PTV method, respectively. Under setup uncertainties, the average lowest [D95%, V95%, V100%] of CTV were increased from [95.35%, 94.92%, 65.12%] in the Conv method, to [99.43%, 99.63%, 87.12%] in the WC method, [96.97%, 97.13%, 77.86%] in the SenR-CTV method, and [98.21%, 98.34%, 83.88%] in the SenR-PTV method, respectively. The runtime of the SenR optimization is eight times shorter than that of the voxel-wise worst-case method.ConclusionWe developed a novel computationally efficient robust optimization method for IMPT. The robustness is calculated as the spot sensitivity to both range and shift perturbations. The dose fidelity term is then regularized by the sensitivity term for the flexibility and trade-off between the dosimetry and the robustness. In the stress test, SenR is more resilient to unexpected uncertainties. These advantages in combination with its fast computation time make it a viable candidate for clinical IMPT planning.

PurposeCT ventilation imaging (CTVI) is being used to achieve functional avoidance lung cancer radiation therapy in three clinical trials (NCT02528942, NCT02308709, NCT02843568). To address the need for common CTVI validation tools, we have built the Ventilation And Medical Pulmonary Image Registration Evaluation (VAMPIRE) Dataset, and present the results of the first VAMPIRE Challenge to compare relative ventilation distributions between different CTVI algorithms and other established ventilation imaging modalities.MethodsThe VAMPIRE Dataset includes 50 pairs of 4DCT scans and corresponding clinical or experimental ventilation scans, referred to as reference ventilation images (RefVIs). The dataset includes 25 humans imaged with Galligas 4DPET/CT, 21 humans imaged with DTPA-SPECT, and 4 sheep imaged with Xenon-CT. For the VAMPIRE Challenge, 16 subjects were allocated to a training group (with RefVI provided) and 34 subjects were allocated to a validation group (with RefVI blinded). Seven research groups downloaded the Challenge dataset and uploaded CTVIs based on deformable image registration (DIR) between the 4DCT inhale/exhale phases. Participants used DIR methods broadly classified into B-splines, Free-form, Diffeomorphisms, or Biomechanical modeling, with CT ventilation metrics based on the DIR evaluation of volume change, Hounsfield Unit change, or various hybrid approaches. All CTVIs were evaluated against the corresponding RefVI using the voxel-wise Spearman coefficient rS , and Dice similarity coefficients evaluated for low function lung ( DSClow ) and high function lung ( DSChigh ).ResultsA total of 37 unique combinations of DIR method and CT ventilation metric were either submitted by participants directly or derived from participant-submitted DIR motion fields using the in-house software, VESPIR. The rS and DSC results reveal a high degree of inter-algorithm and intersubject variability among the validation subjects, with algorithm rankings changing by up to ten positions depending on the choice of evaluation metric. The algorithm with the highest overall cross-modality correlations used a biomechanical model-based DIR with a hybrid ventilation metric, achieving a median (range) of 0.49 (0.27-0.73) for rS , 0.52 (0.36-0.67) for DSClow , and 0.45 (0.28-0.62) for DSChigh . All other algorithms exhibited at least one negative rS value, and/or one DSC value less than 0.5.ConclusionsThe VAMPIRE Challenge results demonstrate that the cross-modality correlation between CTVIs and the RefVIs varies not only with the choice of CTVI algorithm but also with the choice of RefVI modality, imaging subject, and the evaluation metric used to compare relative ventilation distributions. This variability may arise from the fact that each of the different CTVI algorithms and RefVI modalities provides a distinct physiologic measurement. Ultimately this variability, coupled with the lack of a "gold standard," highlights the ongoing importance of further validation studies before CTVI can be widely translated from academic centers to the clinic. It is hoped that the information gleaned from the VAMPIRE Challenge can help inform future validation efforts.

PurposeTime of flight (TOF) PET reconstruction is well known to statistically improve the image quality compared to non-TOF PET. Although TOF PET can improve the overall signal to noise ratio (SNR) of the image compared to non-TOF PET, the SNR disparity between separate regions in the reconstructed image using TOF data becomes higher than that using non-TOF data. Using the conventional ordered subset expectation maximization (OS-EM) method, the SNR in the low activity regions becomes significantly lower than in the high activity regions due to the different photon statistics of TOF bins. A uniform recovery across different SNR regions is preferred if it can yield an overall good image quality within small number of iterations in practice. To allow more uniform recovery of regions, a spatially variant update is necessary for different SNR regions.MethodsThis paper focuses on designing a spatially variant step size and proposes a TOF-PET reconstruction method that uses a nonuniform separable quadratic surrogates (NUSQS) algorithm, providing a straightforward control of spatially variant step size. To control the noise, a spatially invariant quadratic regularization is incorporated, which by itself does not theoretically affect the recovery uniformity. The Nesterov's momentum method with ordered subsets (OS) is also used to accelerate the reconstruction speed. To evaluate the proposed method, an XCAT simulation phantom and clinical data from a pancreas cancer patient with full (ground truth) and 6× downsampled counts were used, where a Poisson thinning process was employed for downsampling. We selected tumor and cold regions of interest (ROIs) and compared the proposed method with the TOF-based conventional OS-EM and OS-SQS algorithms with an early stopping criterion.ResultsIn computer simulation, without regularization, hot regions of OS-EM and OS-NUSQS converged similarly, but cold region of OS-EM was noisier than OS-NUSQS after 24 iterations. With regularization, although the overall speeds of OS-EM and OS-NUSQS were similar, recovery ratios of hot and cold regions reconstructed by the OS-NUSQS were more uniform compared to those of the conventional OS-SQS and OS-EM. The OS-NUSQS with Nesterov's momentum converged faster than others while preserving the uniform recovery. In the clinical example, we demonstrated that the OS-NUSQS with Nesterov's momentum provides more uniform recovery ratios of hot and cold ROIs compared to the OS-SQS and OS-EM. Although the cost function of all methods is equivalent, the proposed method has higher structural similarity (SSIM) values of hot and cold regions compared to other methods after 24 iterations. Furthermore, our computing time using graphics processing unit was 80× shorter than the time using quad-core CPUs.ConclusionThis paper proposes a TOF PET reconstruction method using the OS-NUSQS with Nesterov's momentum for uniform recovery of different SNR regions. In particular, the spatially nonuniform step size in the proposed method provides uniform recovery ratios of different SNR regions, and the Nesterov's momentum further accelerates overall convergence while preserving uniform recovery. The computer simulation and clinical example demonstrate that the proposed method converges uniformly across ROIs. In addition, tumor contrast and SSIM of the proposed method were higher than those of the conventional OS-EM and OS-SQS in early iterations.

Background: Due to the complex nature of digital breast tomosynthesis (DBT) in imaging techniques, reading times are longer than 2D mammograms. A robust computer‐aided diagnosis system in DBT could help radiologists reduce their workload and reading times. Purpose: The purpose of this study was to develop algorithms for detecting biopsy‐proven breast lesions on DBT using multi‐depth level convolutional models and leveraging non‐biopsied samples. As biopsied positive samples in a lesion dataset are limited, we hypothesized that false positive (FP) findings by detection algorithms from non‐biopsied benign lesions could improve detection algorithms by using them as data augmentation. Approach: We first extracted 2D slices from DBT volumes with biopsy‐proven breast lesions (cancer and benign), with non‐biopsied benign lesions (actionable), and for controls. Then, to provide lesion continuity along the z‐direction, we combined a lesion slice with its immediate adjacent slices to synthesize 2.5‐dimensional (2.5D) images of the lesion by assigning them into R, G, and B color channels. We used 224 biopsy‐proven lesions from 39 cancer and 62 benign patients from a DBTex challenge dataset of 1000 scans. We included the 2.5D images of immediate neighboring slices from the lesion's center to increase the number of training samples. For lesion detection, we used the YOLOv5 algorithm as our base network. We trained a baseline algorithm (medium‐depth level) using biopsied samples to detect actionable FPs in non‐biopsied images. Afterward, we fine‐tuned the baseline model on the augmented image set (actionable FPs added). For lesion inferencing, we processed the DBT volume slice‐by‐slice to estimate bounding boxes in each slice, and then combined them by connecting bounding boxes along the depth via volumetric morphological closing. We trained an additional model (large) with deeper‐depth levels by repeating the above process. Finally, we developed an ensemble algorithm by combining the medium and large detection models. We used the free‐response operating characteristic curve to evaluate our algorithms. We reported mean sensitivity per FPs per DBT volume only for biopsied views and sensitivity at 2‐false positives per image (2FPI) for all views. However, due to the limited accessibility to the truth of the challenge validation and test datasets, we used sensitivity at 2FPI for statistical evaluation. Results: For the DBTex independent validation set, the medium baseline model achieved a mean sensitivity of 0.627 FPs per DBT volume, and a sensitivity of 0.640 at 2FPI. After adding actionable FP lesions, the model had an improved 2FPI of 0.769 over the baseline (p‐value = 0.013). Our ensemble algorithm with multi‐depth levels (medium + large) achieved a mean sensitivity of 0.815 FPs per DBT volume and an improved sensitivity at 2FPI of 0.80 over the baseline (p‐value < 0.001) on the validation set. Finally, our ensemble model achieved a mean sensitivity of 0.786 FPs per DBT volume and a sensitivity of 0.743 at 2FPI on the DBTex independent test set. Conclusions: Our results show that actionable FP findings hold useful information for lesion detection algorithms, and our ensemble detection model with multi‐depth levels improves lesion detection performance. [ABSTRACT FROM AUTHOR]

Background: Deformable image registration is a crucial task in the field of medical image analysis. Functions of bounded deformation (BD) have been proved effective for modeling the displacement fields between medical images since they can capture the discontinuity of displacement fields along edges of organs and tissues. Purpose: However, we find that at the same time, BD functions‐based models tend to obtain discontinuous displacement fields inside the regions of organs and tissues due to image noises in some cases and the presented gradient descent algorithm is time‐consuming. To alleviate these problems, we propose a faster algorithm named SPA: splitting proximate algorithm. Methods: In the framework of variable‐splitting scheme, we incorporate a proximal term in the deformable registration energy based on functions of BD. Results: The proposed algorithm can efficiently solve the original model and obtain displacement fields, which look more natural and plausible. Numerical experiments show the effectiveness and stability of the proposed algorithm. Conclusions: The proposed SPA is able to drastically register the images with a plausible deformation field and not sensitive to noise. [ABSTRACT FROM AUTHOR]

Purpose: This work presents the clinical validation of RayStation's electron Monte Carlo code by the use of diodes and plane parallel radiation detectors in homogenous and heterogeneous tissues. Results are evaluated against internationally accepted criteria. Methods: The Monte Carlo–based electron beam dose calculation code was validated using diodes, air‐ and liquid‐filled parallel radiation detectors on an Elekta linac with beam energies of 4, 6, 8, 10, and 12 MeV. Treatment setups with varying source‐to‐skin distances, different applicators, various cutouts, and oblique beam incidences were addressed, together with dose prediction behind lung‐, air‐, and bone‐equivalent inserts. According to NCS (Netherlands Commission for Radiation Dosimetry) report 15 for nonstandard treatment setups, a dose agreement of 3% in the δ1 region (high‐dose region around Zref), a distance‐to‐agreement (DTA) of 3 mm or a dose agreement of 10% in the δ2 region (regions with high‐dose gradients), and 4% in the δ4 region (photon tail/low‐dose region) were applied. During validation, clinical routine settings of 2 × 2 × 2‐mm3 dose voxels and a statistically dose uncertainty of 0.6% (250 000 histories/cm2) were used. Results: RayStation's electron Monte Carlo code dose prediction was able to achieve the tolerances of NCS report 15. Output predictions as a function of the SSD improve with energy and applicator size. Cutout data revealed no field size or energy dependence on the accuracy of the dose prediction. Excellent agreement for the oblique incidence data was achieved and a maximum of one voxel difference was obtained for the DTA behind heterogeneous inserts. Conclusions: The accuracy of RayStation's Monte Carlo–based electron beam dose prediction for Elekta accelerators is confirmed for clinical treatment planning that is not only performed within an acceptable timeframe in terms of the number of histories but also addresses for homogenous and heterogeneous media. [ABSTRACT FROM AUTHOR]

Background: Spot-scanning Proton Arc (SPArc) has been of significant interest in recent years because of its superior plan quality. Currently, the primary focus of research and development is on deliverability and treatment efficiency., Purpose: To address the challenges in generating a deliverable and efficient SPArc plan for a proton therapy system with a massive gantry, we developed a novel SPArc optimization algorithm (SPArc DMPO ) by directly incorporating the machine-specific parameters such as gantry mechanical constraints and proton delivery sequence., Methods: SPArc delivery sequence model (DSM arc ) was built based on the machine-specific parameters of the prototype arc delivery system, IBA ProteusONE®, including mechanical constraint (maximum gantry speed, acceleration, and deceleration) and proton delivery sequence (energy and spot delivery sequence, and irradiation time). SPArc DMPO resamples and adjusts each control point's delivery speed based on the DSM arc calculation through the iterative approach. In SPArc DMPO, users could set a reasonable arc delivery time during the plan optimization, which aims to minimize the gantry momentum changes and improve the delivery efficiency. Ten cases were selected to test SPArc DMPO . Two kinds of SPArc plans were generated using the same planning objective functions: (1) original SPArc plan (SPArc original ); (2) SPArc DMPO plan with a user-pre-defined delivery time. Additionally, arc delivery sequence was simulated based on the DSM arc and was compared. Treatment delivery time was compared between SPArc original and SPArc DMPO . Dynamic arc delivery time, the static irradiation time, and its corresponding time differential (time differential = dynamic arc delivery time-static irradiation time) were analyzed, respectively. The total gantry velocity change was accumulated throughout the treatment delivery., Results: With a similar plan quality, objective value, number of energy layers, and spots, both SPArc original and SPArc DMPO plans could be delivered continuously within the ± 1 degree tolerance window. However, compared to the SPArc original , the strategy of SPArc DMPO is able to reduce the time differential from 30.55 ± 11.42%(90 ± 32 s) to 14.67 ± 6.97%(42 ± 20 s), p < 0.01. Furthermore, the corresponding total variations of gantry velocity during dynamic arc delivery are mitigated (SPArc original vs. SPArc DMPO ) from 14.73 ± 9.14 degree/s to 4.28 ± 2.42 degree/s, p < 0.01. Consequently, the SPArc DMPO plans could minimize the gantry momentum change based on the clinical user's input compared to the SPArc original plans , which could help relieve the mechanical challenge of accelerating or decelerating the massive proton gantry., Conclusions: For the first time, clinical users not only could generate a SPArc plan meeting the mechanical constraint of their proton system but also directly control the arc treatment speed and momentum changes of the gantry during the plan optimization process. This work paved the way for the routine clinical implementation of proton arc therapy in the treatment planning system., (© 2024 American Association of Physicists in Medicine.)

Background: The beam-hardening effect due to the polychromatic nature of the X-ray spectra results in two main artifacts in CT images: cupping in homogeneous areas and dark bands between dense parts in heterogeneous samples. Post-processing methods have been proposed in the literature to compensate for these artifacts, but these methods may introduce additional noise in low-dose acquisitions. Iterative methods are an alternative to compensate noise and beam-hardening artifacts simultaneously. However, they usually rely on the knowledge of the spectrum or the selection of empirical parameters., Purpose: We propose an iterative reconstruction method with beam hardening compensation for small animal scanners that is robust against low-dose acquisitions and that does not require knowledge of the spectrum, overcoming the limitations of current beam-hardening correction algorithms., Methods: The proposed method includes an empirical characterization of the beam-hardening function based on a simple phantom in a polychromatic statistical reconstruction method. Evaluation was carried out on simulated data with different noise levels and step angles and on limited-view rodent data acquired with the ARGUS/CT system., Results: Results in small animal studies showed a proper correction of the beam-hardening artifacts in the whole sample, independently of the quantity of bone present on each slice. The proposed approach also reduced noise in the low-dose acquisitions and reduced streaks in the limited-view acquisitions., Conclusions: Using an empirical model for the beam-hardening effect, obtained through calibration, in an iterative reconstruction method enables a robust correction of beam-hardening artifacts in low-dose small animal studies independently of the bone distribution., (© 2024 The Author(s). Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background: Golden angle (GA) radial trajectory is advantageous for dynamic magnetic resonance imaging (MRI). Recently, several advanced algorithms have been developed based on navigator-interleaved GA trajectory to realize free-running cardiac MRI. However, navigator-interleaved GA trajectory suffers from the eddy-current effect, which reduces the image quality., Purpose: This work aims to integrate the navigator-interleaved GA trajectory with clinical cardiac MRI acquisition, with the minimum eddy-current artifacts. The ultimate goal is to realize a high-quality free-running cardiac imaging technique., Methods: In this paper, we propose a new "swing golden angle" (swingGA) radial profile order. SwingGA samples the k-space by rotating back and forth at the generalized golden ratio interval, with smoothly interleaved navigator readouts. The sampling efficiency and angle increment distributions were investigated by numerical simulations. Static phantom imaging experiments were conducted to evaluate the eddy current effect, compared with cartesian, golden angle radial (GA), and tiny golden angle (tGA) trajectories. Furthermore, 12 heart-healthy subjects (aged 21-25 years) were recruited for free-running cardiac imaging with different sampling trajectories. Dynamic images were reconstructed by a low-rank subspace-constrained algorithm. The image quality was evaluated by signal-to-noise-ratio and spectrum analysis in the heart region, and compared with traditional clinical cardiac MRI images., Results: SwingGA pattern achieves the highest sampling efficiency (mSE > 0.925) and the minimum azimuthal angle increment (mAD < 1.05). SwingGA can effectively suppress eddy currents in static phantom images, with the lowest normalized root mean square error (nRMSE) values among radial trajectories. For the in-vivo cardiac images, swingGA enjoys the highest SNR both in the blood pool and myocardium, and contains the minimum level of high-frequency artifacts. The free-running cardiac images have good consistency with traditional clinical cardiac MRI, and the swingGA sampling pattern achieves the best image quality among all sampling patterns., Conclusions: The proposed swingGA sampling pattern can effectively improve the sampling efficiency and reduce the eddy currents for the navigator-interleaved GA sequence. SwingGA is a promising sampling pattern for free-running cardiac MRI., (© 2024 American Association of Physicists in Medicine.)

Background: With recent interest in patient-specific dosimetry for radiopharmaceutical therapy (RPT) and selective internal radiation therapy (SIRT), an increasing number of voxel-based algorithms are being evaluated. Monte Carlo (MC) radiation transport, generally considered to be the most accurate among different methods for voxel-level absorbed dose estimation, can be computationally inefficient for routine clinical use., Purpose: This work demonstrates a recently implemented grid-based linear Boltzmann transport equation (LBTE) solver for fast and accurate voxel-based dosimetry in RPT and SIRT and benchmarks it against MC., Methods: A deterministic LBTE solver (Acuros MRT) was implemented within a commercial RPT dosimetry package (Velocity 4.1). The LBTE is directly discretized using an adaptive mesh refined grid and then the coupled photon-electron radiation transport is iteratively solved inside specified volumes to estimate radiation doses from both photons and charged particles in heterogeneous media. To evaluate the performance of the LBTE solver for RPT and SIRT applications, 177 Lu SPECT/CT, 90 Y PET/CT, and 131 I SPECT/CT images of phantoms and patients were used. Multiple lesions (2-1052 mL) and normal organs were delineated for each study. Voxel dosimetry was performed with the LBTE solver, dose voxel kernel (DVK) convolution with density correction, and a validated in-house MC code using the same time-integrated activity and density maps as input to the different dose engines. The resulting dose maps, difference maps, and dose-volume-histogram (DVH) metrics were compared, to assess the voxel-level agreement. Evaluation of mean absorbed dose included comparison with structure-level estimates from OLINDA., Results: In the phantom inserts/compartments, the LBTE solver versus MC and DVK convolution demonstrated good agreement with mean absorbed dose and DVH metrics agreeing to within 5% except for the D90 and D70 metrics of a very low activity concentration insert of 90 Y where the agreement was within 15%. In the patient studies (five patients imaged after 177 Lu DOTATATE RPT, five after 90 Y SIRT, and two after 131 I radioimmunotherapy), in general, there was better agreement between the LBTE solver and MC than between LBTE solver and DVK convolution for mean absorbed dose and voxel-level evaluations. Across all patients for all three radionuclides, for soft tissue structures (kidney, liver, lesions), the mean absorbed dose estimates from the LBTE solver were in good agreement with those from MC (median difference < 1%, maximum 9%) and those from DVK (median difference < 5%, maximum 9%). The LBTE and OLINDA estimates for mean absorbed dose in kidneys and liver agreed to within 10%, but differences for lesions were larger with a maximum 14% for 177 Lu, 23% for 90 Y, and 26% for 131 I. For bone regions, the agreement in mean absorbed doses between LBTE and both MC and DVK were similar (median < 11%, max 11%) while for lung the agreement between LBTE and MC (median < 1%, max 8%) was substantially better than between LBTE and DVK (median < 16%, max 33%). Voxel level estimates for soft tissue structures also showed good agreement between the LBTE solver and both MC and DVK with a median difference < 5% (maximum < 13%) for the DVH metrics with all three radionuclides. The largest difference in DVH metrics was for the D90 and D70 metric in lung and bone where the uptake was low. Here, the difference between LBTE and MC had a median value < 14% (maximum 23%) for bone and < 4% (maximum 37%) for lung, while the corresponding differences between LBTE and DVK were < 23% (maximum 31%) and < 67% (maximum 313%), respectively. For a typical patient with a matrix size of 166 × 166 × 129 (voxel size 3 × 3 × 3 mm 3 ), voxel dosimetry using the LBTE solver was as fast as ∼2 min on a desktop computer., Conclusion: Having established good agreement between the LBTE solver and MC for RPT and SIRT applications, the LBTE solver is a viable option for voxel dosimetry that can be faster than MC. Further analysis is being performed to encompass the broad range of radionuclides and conditions encountered clinically., (© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background: Dynamic contrast-enhanced ultrasound (DCE-US) is highly susceptible to motion artifacts arising from patient movement, respiration, and operator handling and experience. Motion artifacts can be especially problematic in the context of perfusion quantification. In conventional 2D DCE-US, motion correction (MC) algorithms take advantage of accompanying side-by-side anatomical B-Mode images that contain time-stable features. However, current commercial models of 3D DCE-US do not provide side-by-side B-Mode images, which makes MC challenging., Purpose: This work introduces a novel MC algorithm for 3D DCE-US and assesses its efficacy when handling clinical data sets., Methods: In brief, the algorithm uses a pyramidal approach whereby short temporal windows consisting of three consecutive frames are created to perform local registrations, which are then registered to a master reference derived from a weighted average of all frames. We applied the algorithm to imaging studies from eight patients with metastatic lesions in the liver and assessed improvements in original versus motion corrected 3D DCE-US cine using: (i) frame-to-frame volumetric overlap of segmented lesions, (ii) normalized correlation coefficient (NCC) between frames (similarity analysis), and (iii) sum of squared errors (SSE), root-mean-squared error (RMSE), and r-squared (R 2 ) quality-of-fit from fitted time-intensity curves (TIC) extracted from a segmented lesion., Results: We noted improvements in frame-to-frame lesion overlap across all patients, from 68% ± 13% without correction to 83% ± 3% with MC (p = 0.023). Frame-to-frame similarity as assessed by NCC also improved on two different sets of time points from 0.694 ± 0.057 (original cine) to 0.862 ± 0.049 (corresponding MC cine) and 0.723 ± 0.066 to 0.886 ± 0.036 (p ≤ 0.001 for both). TIC analysis displayed a significant decrease in RMSE (p = 0.018) and a significant increase in R 2 goodness-of-fit (p = 0.029) for the patient cohort., Conclusions: Overall, results suggest decreases in 3D DCE-US motion after applying the proposed algorithm., (© 2024 American Association of Physicists in Medicine.)

Background: While minimizing plan delivery time is beneficial for proton therapy in terms of motion management, patient comfort, and treatment throughput, it often poses a tradeoff with optimizing plan quality. A key component of plan delivery time is the energy switching time, which is approximately proportional to the number of energy layers, that is, the cardinality., Purpose: This work aims to develop a novel optimization method that can efficiently compute the pareto surface between plan quality and energy layer cardinality, for the planner to navigate through this quality-and-efficiency tradeoff and select the appropriate plan of a balanced tradeoff., Methods: A new IMPT method CARD is proposed that (1) explicitly incorporates the minimization of energy layer cardinality as an optimization objective, and (2) automatically generates a set of plans sequentially with a descending order in number of energy layers. The energy layer cardinality is penalized through the l 1,0 -norm regularization with an upper bound, and the upper bound is monotonically decreased to compute a series of treatment plans with gradually decreased energy layer cardinality on the quality-and-efficiency pareto surface. For any given treatment plan, the plan optimality is enforced using dose-volume planning objectives and the plan deliverability is imposed through minimum-monitor-unit (MMU) constraints, with optimization solution algorithm based on iterative convex relaxation., Results: The new method CARD was validated in comparison with the benchmark plan of all energy layers (P0), and a state-of-the-art method called MMSEL, using prostate, head-and-neck (HN), lung, pancreas, liver and brain cases. While labor-intensive and time-consuming manual parameter tuning was needed for MMSEL to generate plans of predefined energy layer cardinality, CARD automatically and efficiently computed all plans with sequentially decreasing predefined energy layer cardinality all at once. With the acceptable plan quality (i.e., no more than 110% of total optimization objective value from P0), CARD achieved the reduction of number of energy layers to 52% (from 77 to 40), 48% (from 135 to 65), 59% (from 85 to 50), 67% (from 52 to 35), 80% (from 50 to 40), and 30% (from 66 to 20), for prostate, HN, lung, pancreas, liver, and brain cases, respectively, compared to P0, with overall better plan quality than MMSEL. Moreover, due to the nonconvexity of the MMU constraint, CARD provided the similar or even smaller optimization objective than P0, at the same time with fewer number of energy layers, that is, 55 versus 77, 85 versus 135, 45 versus 52, and 25 versus 66 for prostate, HN, pancreas, and brain cases, respectively., Conclusions: We have developed a novel optimization algorithm CARD that can efficiently and automatically compute a series of treatment plans of any given energy layer sequentially, which allows the planner to navigate through the plan-quality and energy-layer-cardinality tradeoff and select the appropriate plan of a balanced tradeoff., (© 2024 American Association of Physicists in Medicine.)

Background: Conventional methods for estimating the noise power spectrum (NPS) often necessitate multiple computed tomography (CT) data acquisitions and are required to satisfy stringent stationarity and ergodicity conditions, which prove challenging in CT imaging systems., Purpose: The aim was to revisit the conventional NPS estimation method, leading to a new framework that estimates local NPS without relying on stationarity or ergodicity, thus facilitating experimental NPS estimations., Methods: The scientific foundation of the conventional CT NPS measurement method, based on the Wiener-Khintchine theorem, was reexamined, emphasizing the critical conditions of stationarity and ergodicity. This work proposes an alternative framework, characterized by its independence from stationarity and ergodicity, and its ability to facilitate local NPS estimations. A spatial average of local NPS over a Region of Interest (ROI) yields the conventional NPS for that ROI. The connections and differences between the proposed alternative method and the conventional method are discussed. Experimental studies were conducted to validate the new method., Results: (1) The NPS estimated using the conventional method was demonstrated to correspond to the spatial average of pointwise NPS from the proposed NPS estimation framework. (2) The NPS estimated over an ROI with the conventional method was shown to be the sum of the NPS estimated from the proposed method and a contribution from measurement uncertainty. (3) Local NPS estimations from the proposed method in this work elucidate the impact of surrounding image content on local NPS variations., Conclusion: The NPS estimation method proposed in this work allows for the estimation of local NPS without relying on stationarity and ergodicity conditions, offering local NPS estimations with significantly improved precision., (© 2024 The Authors. Medical Physics published by Wiley Periodicals LLC on behalf of American Association of Physicists in Medicine.)

Background: Motion induced image artifacts have been the focus of many investigations for x-ray computed tomography (CT). Methodologies of combating patient motion include the use of gating devices to optimize the data acquisition, reduction in patient scan time via faster gantry rotation and large detector coverage, and the development of advanced reconstruction and post-processing algorithms to minimize motion artifacts., Purpose: Previously proposed approaches are generally "global" in nature in that motion is characterized for the entire image. It is well known, however, that the presence of motion artifact in a CT image is highly nonuniform. When there is a lack of automated and quantitative local measure indicating the presence and the severity of motion artifacts in a local region, the quality of the reconstructed images depends heavily on the CT operator's rigor and experience. Even when an operator is informed of the presence of motion, little information is provided about the nature of the motion artifact to understand its relevance to the clinical task at hand. In this paper, we propose an image-space spatial- and temporal-consistency metric (CM) to detect and characterize the local motion., Method: In a non-rigid human organ, such as the lung, there are many small and rigid objects (target objects), such as blood vessels and nodules, distributed throughout the organ. If motion can be characterized for these target objects, we obtain a complete motion map for the organ. To accomplish this, a preliminary image reconstruction is carried out to identify the target objects and establish region-of-interests for consistency-metric calculation. The CM is then obtained based on the backprojected intensity difference between the object region and its circular background. For a stationary object, the accumulation of this quantity over views is linear. When a target object moves, nonlinear behavior exhibits and a quantitative measure of linearity indicates the severity of motion., Results: Extensive computer simulation was utilized to confirm the validity of the theory. These tests stress the sensitivity of the proposed CM to the target object size, object shape, in-plane motion, cross-plane motion, cone-beam effect, and complex background. Results confirm that the proposed approach is robust under different testing conditions. The proposed CM is further validated using a cardiac scan of a swine, and the proposed CM correlates well with the visual inspection of the artifact in the reconstructed images., Conclusions: In this paper, we have demonstrated the efficacy of the proposed CM for motion detection. Unlike previously proposed approaches where the consistency condition is derived for the entire image or the entire imaging volume, the proposed metric is well localized so that different zones in a patient anatomy can be individually characterized. In addition, the proposed CM provides a quantitative measure on a view-by-view basis so that the severity of motion is consistently estimated over time. Such information can be used to optimize the image reconstruction process and minimize the motion artifact., (© 2024 American Association of Physicists in Medicine.)

Background: Proton arc therapy (PAT) has emerged as a promising approach for improving dose distribution, but also enabling simpler and faster treatment delivery in comparison to conventional proton treatments. However, the delivery speed achievable in proton arc relies on dedicated algorithms, which currently do not generate plans with a clear speed-up and sometimes even result in increased delivery time., Purpose: This study aims to address the challenge of minimizing delivery time through a hybrid method combining a fast geometry-based energy layer (EL) pre-selection with a dose-based EL filtering, and comparing its performance to a baseline approach without filtering., Methods: Three methods of EL filtering were developed: unrestricted, switch-up (SU), and switch-up gap (SU gap) filtering. The unrestricted method filters the lowest weighted EL while the SU gap filtering removes the EL around a new SU to minimize the gantry rotation braking. The SU filtering removes the lowest weighted group of EL that includes a SU. These filters were combined with the RayStation dynamic proton arc optimization framework energy layer selection and spot assignment (ELSA). Four bilateral oropharyngeal and four lung cancer patients' data were used for evaluation. Objective function values, target coverage robustness, organ-at-risk doses and normal tissue complication probability evaluations, as well as comparisons to intensity-modulated proton therapy (IMPT) plans, were used to assess plan quality., Results: The SU gap filtering algorithm performed best in five out of the eight cases, maintaining plan quality within tolerance while reducing beam delivery time, in particular for the oropharyngeal cohort. It achieved up to approximately 22% and 15% reduction in delivery time for oropharyngeal and lung treatment sites, respectively. The unrestricted filtering algorithm followed closely. In contrast, the SU filtering showed limited improvement, suppressing one or two SU without substantial delivery time shortening. Robust target coverage was kept within 1% of variation compared to the PAT baseline plan while organs-at-risk doses slightly decreased or kept about the same for all patients., Conclusions: This study provides insights to accelerate PAT delivery without compromising plan quality. These advancements could enhance treatment efficiency and patient throughput., (© 2024 American Association of Physicists in Medicine.)

Purpose: Most commercially available treatment planning systems (TPSs) approximate the continuous delivery of volumetric modulated arc therapy (VMAT) plans with a series of discretized static beams for treatment planning, which can make VMAT dose computation extremely inefficient. In this study, we developed a polar‐coordinate‐based pencil beam (PB) algorithm for efficient VMAT dose computation with high‐resolution gantry angle sampling that can improve the computational efficiency and reduce the dose discrepancy due to the angular under‐sampling effect. Methods and Materials: 6 MV 1×1mm2$1 \times 1{\rm{\;m}}{{\rm{m}}^2}$ pencil beams were simulated on a uniform cylindrical phantom under an EGSnrc Monte Carlo (MC) environment. The MC‐generated PB kernels were collected in the polar coordinate system for each bixel on a 40×40cm2$40 \times 40{\rm{\;c}}{{\rm{m}}^2}$ fluence map and subsequently fitted via a series of Gaussians. The fluence was calculated using a detectors' eye view with off‐axis and MLC transmission factors corrected. Doses of VMAT arc on the phantom were computed by summing the convolution results between the corresponding PB kernels and fluence for each bixel in the polar coordinate system. The convolution was performed using fast Fourier transform to expedite the computing speed. The calculated doses were converted to the Cartesian coordinate system and compared with the reference dose computed by a collapsed cone convolution (CCC) algorithm of the TPS. A heterogeneous phantom was created to study the heterogeneity corrections using the proposed algorithm. Ten VMAT arcs were included to evaluate the algorithm performance. Gamma analysis and computation complexity theory were used to measure the dosimetric accuracy and computational efficiency, respectively. Results: The dosimetric comparisons on the homogeneous phantom between the proposed PB algorithm and the CCC algorithm for 10 VMAT arcs demonstrate that the proposed algorithm can achieve a dosimetric accuracy comparable to that of the CCC algorithm with average gamma passing rates of 96% (2%/2mm) and 98% (3%/3mm). In addition, the proposed algorithm can provide better computational efficiency for VMAT dose computation using a PC equipped with a 4‐core processor, compared to the CCC algorithm utilizing a dual 10‐core server. Moreover, the computation complexity theory reveals that the proposed algorithm has a great advantage with regard to computational efficiency for VMAT dose computation on homogeneous medium, especially when a fine angular sampling rate is applied. This can support a reduction in dose errors from the angular under‐sampling effect by using a finer angular sampling rate, while still preserving a practical computing speed. For dose calculation on the heterogeneous phantom, the proposed algorithm with heterogeneity corrections can still offer a reasonable dosimetric accuracy with comparable computational efficiency to that of the CCC algorithm. Conclusions: We proposed a novel polar‐coordinate‐based pencil beam algorithm for VMAT dose computation that enables a better computational efficiency while maintaining clinically acceptable dosimetric accuracy and reducing dose error caused by the angular under‐sampling effect. It also provides a flexible VMAT dose computation structure that allows adjustable sampling rates and direct dose computation in regions of interest, which makes the algorithm potentially useful for clinical applications such as independent dose verification for VMAT patient‐specific QA. [ABSTRACT FROM AUTHOR]

Purpose: This study developed and evaluated a fully convolutional network (FCN) for pediatric CT organ segmentation and investigated the generalizability of the FCN across image heterogeneities such as CT scanner model protocols and patient age. We also evaluated the autosegmentation models as part of a software tool for patient‐specific CT dose estimation. Methods: A collection of 359 pediatric CT datasets with expert organ contours were used for model development and evaluation. Autosegmentation models were trained for each organ using a modified FCN 3D V‐Net. An independent test set of 60 patients was withheld for testing. To evaluate the impact of CT scanner model protocol and patient age heterogeneities, separate models were trained using a subset of scanner model protocols and pediatric age groups. Train and test sets were split to answer questions about the generalizability of pediatric FCN autosegmentation models to unseen age groups and scanner model protocols, as well as the merit of scanner model protocol or age‐group‐specific models. Finally, the organ contours resulting from the autosegmentation models were applied to patient‐specific dose maps to evaluate the impact of segmentation errors on organ dose estimation. Results: Results demonstrate that the autosegmentation models generalize to CT scanner acquisition and reconstruction methods which were not present in the training dataset. While models are not equally generalizable across age groups, age‐group‐specific models do not hold any advantage over combining heterogeneous age groups into a single training set. Dice similarity coefficient (DSC) and mean surface distance results are presented for 19 organ structures, for example, median DSC of 0.52 (duodenum), 0.74 (pancreas), 0.92 (stomach), and 0.96 (heart). The FCN models achieve a mean dose error within 5% of expert segmentations for all 19 organs except for the spinal canal, where the mean error was 6.31%. Conclusions: Overall, these results are promising for the adoption of FCN autosegmentation models for pediatric CT, including applications for patient‐specific CT dose estimation. [ABSTRACT FROM AUTHOR]

Purpose: Provide a projection‐based algorithm to solve the class of optimization problems encountered in intensity modulated proton therapy (IMPT). The algorithm can handle percentage dose–volume constraints (DVCs) that are usually found in such problems. Methods: To seek a feasible solution, the automatic relaxation method was used to project the spot weight vector onto the interval defined by lower and upper bound target dose constraints. The obtained solution was optimized separately based on the objective of each organ at risk (OAR) in addition to maximizing the minimum target dose using the bisection search method using a stopping criterion of 10 cGy. The combined weight was used in the CQ algorithm to solve the split feasibility problem but with a special projection technique due to the nonconvexity of DVCs. The algorithm was applied to four clinical IMPT cases (meningioma, prostate, tongue, and oropharynx) and compared to the corresponding treatment plans optimized in Eclipse. Results: The treatment plans obtained, for the four cases, using the BCQ‐ARM algorithm have dosimetric endpoints that are similar to their counterparts generated from Eclipse. The algorithm worked equally well with all cases, including the complex head and neck ones. The stopping criterion of 10 cGy results in making the generated plans slightly less optimal (ε$\epsilon$–optimal) rather than optimal, but with the advantage of the possibility of generating a database of plans. Conclusions: The application of the BCQ‐ARM algorithm to different cases of IMPT plans with DVCs was demonstrated. The algorithm is successful in generating plans that are dosimetrically equivalent to their corresponding Eclipse plans. Thus, it is suitable to generate optimized treatment plans in a clinically reasonable time frame. [ABSTRACT FROM AUTHOR]

PurposeCoronary x-ray computed tomography angiography (CCTA) is used to non-invasively assess coronary artery geometry and has, combined with computational modeling, demonstrated the potential to identify physiologically significant lesions. These measurements require robust and accurate coronary imaging and delineation of vessels despite the presence of small motion. This simulation study characterizes the impact of small, uncorrected vessel drifts during data acquisition on the assessment of vessel intensity, diameter, and shape.MethodsWe developed a digital phantom and simulated projection data for a clinical scanner geometry for a range of vessel drifts that can occur during relative vessel stasis (0 to 2 mm per 360° gantry rotation) for vessels between 0.2 and 3.0 mm in diameter (covering 0% through 93% stenosis of a 3 mm vessel). In addition to the impact of vessel drift, we evaluated the performance of half-scan acquisitions (relative to full-scans) over a range of gantry positions. The performance of FDK reconstructions was compared to an iterative technique and potential improvement in sampling from focal spot deflection and quarter detector offset was compared.ResultsAt rest, vessel intensity and diameter were accurately obtained in vessels greater than 1.5 mm with all vessels appearing circular in shape (major-to-minor axis ratio ~1). Vessels between 1.5 and 0.2 mm in diameter demonstrated a rapid decrease in signal intensity with full width half maximum (FWHM) vessel diameters remaining above 0.75 mm as true vessel diameter decreased. Uncorrected vessel motion resulted in decreased vessel intensity, increased vessel diameter, and distortion of vessel shape. The extent of these changes depended on both the position of the gantry as well as the reconstruction approach (half- vs. full-scan). FDK reconstruction results depended on choice of filter with Ram-Lak results yielding comparable performance to an unconstrained iterative reconstruction. Focal spot deflection and quarter detector offset did not result in large changes in performance, likely due to the high sampling density near the isocenter.ConclusionsDespite improvement in gantry speed and acquisition of coronary images during cardiac phases that have relatively stationary vessels, small coronary drifts (0-2 mm per 360° rotation) have been reported and if uncorrected, can present challenges to visual grading and computational modeling of stenoses because vessels will appear dimmer, larger, and more ellipsoidal in shape. The impact of a particular motion depends on the gantry position, the use of half vs. full-scan acquisitions, and the reconstruction technique.

PurposeThe purpose of this study is to determine the optimal representative reconstruction and quantitative image feature set for a computer-aided diagnosis (CADx) scheme for dedicated breast computer tomography (bCT).MethodWe used 93 bCT scans that contain 102 breast lesions (62 malignant, 40 benign). Using an iterative image reconstruction (IIR) algorithm, we created 37 reconstructions with different image appearances for each case. In addition, we added a clinical reconstruction for comparison purposes. We used image sharpness, determined by the gradient of gray value in a parenchymal portion of the reconstructed breast, as a surrogate measure of the image qualities/appearances for the 38 reconstructions. After segmentation of the breast lesion, we extracted 23 quantitative image features. Using leave-one-out-cross-validation (LOOCV), we conducted the feature selection, classifier training, and testing. For this study, we used the linear discriminant analysis classifier. Then, we selected the representative reconstruction and feature set for the classifier with the best diagnostic performance among all reconstructions and feature sets. Then, we conducted an observer study with six radiologists using a subset of breast lesions (N = 50). Using 1000 bootstrap samples, we compared the diagnostic performance of the trained classifier to those of the radiologists.ResultThe diagnostic performance of the trained classifier increased as the image sharpness of a given reconstruction increased. Among combinations of reconstructions and quantitative image feature sets, we selected one of the sharp reconstructions and three quantitative image feature sets with the first three highest diagnostic performances under LOOCV as the representative reconstruction and feature set for the classifier. The classifier on the representative reconstruction and feature set achieved better diagnostic performance with an area under the ROC curve (AUC) of 0.94 (95% CI = [0.81, 0.98]) than those of the radiologists, where their maximum AUC was 0.78 (95% CI = [0.63, 0.90]). Moreover, the partial AUC, at 90% sensitivity or higher, of the classifier (pAUC = 0.085 with 95% CI = [0.063, 0.094]) was statistically better (P-value

PurposeLung cancer screening with low-dose CT has recently been approved for reimbursement, heralding the arrival of such screening services worldwide. Computer-aided detection (CAD) tools offer the potential to assist radiologists in detecting nodules in these screening exams. In lung screening, as in all CT exams, there is interest in further reducing radiation dose. However, the effects of continued dose reduction on CAD performance are not fully understood. In this work, we investigated the effect of reducing radiation dose on CAD lung nodule detection performance in a screening population.MethodsThe raw projection data files were collected from 481 patients who underwent low-dose screening CT exams at our institution as part of the National Lung Screening Trial (NLST). All scans were performed on a multidetector scanner (Sensation 64, Siemens Healthcare, Forchheim Germany) according to the NLST protocol, which called for a fixed tube current scan of 25 effective mAs for standard-sized patients and 40 effective mAs for larger patients. The raw projection data were input to a reduced-dose simulation software to create simulated reduced-dose scans corresponding to 50% and 25% of the original protocols. All raw data files were reconstructed at the scanner with 1 mm slice thickness and B50 kernel. The lungs were segmented semi-automatically, and all images and segmentations were input to an in-house CAD algorithm trained on higher dose scans (75-300 mAs). CAD findings were compared to a reference standard generated by an experienced reader. Nodule- and patient-level sensitivities were calculated along with false positives per scan, all of which were evaluated in terms of the relative change with respect to dose. Nodules were subdivided based on size and solidity into categories analogous to the LungRADS assessment categories, and sub-analyses were performed.ResultsFrom the 481 patients in this study, 82 had at least one nodule (prevalence of 17%) and 399 did not (83%). A total of 118 nodules were identified. Twenty-seven nodules (23%) corresponded to LungRADS category 4 based on size and composition, while 18 (15%) corresponded to LungRADS category 3 and 73 (61%) corresponded to LungRADS category 2. For solid nodules ≥8 mm, patient-level median sensitivities were 100% at all three dose levels, and mean sensitivities were 72%, 63%, and 63% at original, 50%, and 25% dose, respectively. Overall mean patient-level sensitivities for nodules ranging from 3 to 45 mm were 38%, 37%, and 38% at original, 50%, and 25% dose due to the prevalence of smaller nodules and nonsolid nodules in our reference standard. The mean false-positive rates were 3, 5, and 13 per case.ConclusionsCAD sensitivity decreased very slightly for larger nodules as dose was reduced, indicating that reducing the dose to 50% of original levels may be investigated further for use in CT screening. However, the effect of dose was small relative to the effect of the nodule size and solidity characteristics. The number of false positives per scan increased substantially at 25% dose, illustrating the importance of tuning CAD algorithms to very challenging, high-noise screening exams.

PurposeProton computed tomography (pCT) is a promising imaging technique to substitute or at least complement x-ray CT for more accurate proton therapy treatment planning as it allows calculating directly proton relative stopping power from proton energy loss measurements. A proton CT scanner with a silicon-based particle tracking system and a five-stage scintillating energy detector has been completed. In parallel a modular software platform was developed to characterize the performance of the proposed pCT.MethodThe modular pCT software platform consists of (1) a Geant4-based simulation modeling the Loma Linda proton therapy beam line and the prototype proton CT scanner, (2) water equivalent path length (WEPL) calibration of the scintillating energy detector, and (3) image reconstruction algorithm for the reconstruction of the relative stopping power (RSP) of the scanned object. In this work, each component of the modular pCT software platform is described and validated with respect to experimental data and benchmarked against theoretical predictions. In particular, the RSP reconstruction was validated with both experimental scans, water column measurements, and theoretical calculations.ResultsThe results show that the pCT software platform accurately reproduces the performance of the existing prototype pCT scanner with a RSP agreement between experimental and simulated values to better than 1.5%.ConclusionsThe validated platform is a versatile tool for clinical proton CT performance and application studies in a virtual setting. The platform is flexible and can be modified to simulate not yet existing versions of pCT scanners and higher proton energies than those currently clinically available.