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Purpose: To develop a radiomics filtering technique for characterizing spatial‐encoded regional pulmonary ventilation information on lung computed tomography (CT). Methods: The lung volume was segmented on 46 CT images, and a 3D sliding window kernel was implemented across the lung volume to capture the spatial‐encoded image information. Fifty‐three radiomic features were extracted within the kernel, resulting in a fourth‐order tensor object. As such, each voxel coordinate of the original lung was represented as a 53‐dimensional feature vector, such that radiomic features could be viewed as feature maps within the lungs. To test the technique as a potential pulmonary ventilation biomarker, the radiomic feature maps were compared to paired functional images (Galligas PET or DTPA‐SPECT) based on the Spearman correlation (ρ) analysis. Results: The radiomic feature maps GLRLM‐based Run‐Length Non‐Uniformity and GLCOM‐based Sum Average are found to be highly correlated with the functional imaging. The achieved ρ (median [range]) for the two features are 0.46 [0.05, 0.67] and 0.45 [0.21, 0.65] across 46 patients and 2 functional imaging modalities, respectively. Conclusions: The results provide evidence that local regions of sparsely encoded heterogeneous lung parenchyma on CT are associated with diminished radiotracer uptake and measured lung ventilation defects on PET/SPECT imaging. These findings demonstrate the potential of radiomics to serve as a complementary tool to the current lung quantification techniques and provide hypothesis‐generating data for future studies. [ABSTRACT FROM AUTHOR]

Objective: Despite its prevalence, cone beam computed tomography (CBCT) has poor soft‐tissue contrast, making it challenging to localize liver tumors. We propose a patient‐specific deep learning model to generate synthetic magnetic resonance imaging (MRI) from CBCT to improve tumor localization. Methods: A key innovation is using patient‐specific CBCT‐MRI image pairs to train a deep learning model to generate synthetic MRI from CBCT. Specifically, patient planning CT was deformably registered to prior MRI, and then used to simulate CBCT with simulated projections and Feldkamp, Davis, and Kress reconstruction. These CBCT‐MRI images were augmented using translations and rotations to generate enough patient‐specific training data. A U‐Net‐based deep learning model was developed and trained to generate synthetic MRI from CBCT in the liver, and then tested on a different CBCT dataset. Synthetic MRIs were quantitatively evaluated against ground‐truth MRI. Results: The synthetic MRI demonstrated superb soft‐tissue contrast with clear tumor visualization. On average, the synthetic MRI achieved 28.01, 0.025, and 0.929 for peak signal‐to‐noise ratio, mean square error, and structural similarity index, respectively, outperforming CBCT images. The model performance was consistent across all three patients tested. Conclusion: Our study demonstrated the feasibility of a patient‐specific model to generate synthetic MRI from CBCT for liver tumor localization, opening up a potential to democratize MRI guidance in clinics with conventional LINACs. [ABSTRACT FROM AUTHOR]

Purpose. Although deep learning (DL) technique has been successfully used for computed tomography (CT) reconstruction, its implementation on cone-beam CT (CBCT) reconstruction is extremely challenging due to memory limitations. In this study, a novel DL technique is developed to resolve the memory issue, and its feasibility is demonstrated for CBCT reconstruction from sparsely sampled projection data. Methods. The novel geometry-guided deep learning (GDL) technique is composed of a GDL reconstruction module and a post-processing module. The GDL reconstruction module learns and performs projection-to-image domain transformation by replacing the traditional single fully connected layer with an array of small fully connected layers in the network architecture based on the projection geometry. The DL post-processing module further improves image quality after reconstruction. We demonstrated the feasibility and advantage of the model by comparing ground truth CBCT with CBCT images reconstructed using (1) GDL reconstruction module only, (2) GDL reconstruction module with DL post-processing module, (3) Feldkamp, Davis, and Kress (FDK) only, (4) FDK with DL post-processing module, (5) ray-tracing only, and (6) ray-tracing with DL post-processing module. The differences are quantified by peak-signal-to-noise ratio (PSNR), structural similarity index measure (SSIM), and root-mean-square error (RMSE). Results. CBCT images reconstructed with GDL show improvements in quantitative scores of PSNR, SSIM, and RMSE. Reconstruction time per image for all reconstruction methods are comparable. Compared to current DL methods using large fully connected layers, the estimated memory requirement using GDL is four orders of magnitude less, making DL CBCT reconstruction feasible. Conclusion. With much lower memory requirement compared to other existing networks, the GDL technique is demonstrated to be the first DL technique that can rapidly and accurately reconstruct CBCT images from sparsely sampled data. [ABSTRACT FROM AUTHOR]

Purpose: To develop an artificial intelligence (AI) agent for fully automated rapid head‐and‐neck intensity‐modulated radiation therapy (IMRT) plan generation without time‐consuming dose‐volume‐based inverse planning. Methods: This AI agent was trained via implementing a conditional generative adversarial network (cGAN) architecture. The generator, PyraNet, is a novel deep learning network that implements 28 classic ResNet blocks in pyramid‐like concatenations. The discriminator is a customized four‐layer DenseNet. The AI agent first generates multiple customized two‐dimensional projections at nine template beam angles from a patient's three‐dimensional computed tomography (CT) volume and structures. These projections are then stacked as four‐dimensional inputs of PyraNet, from which nine radiation fluence maps of the corresponding template beam angles are generated simultaneously. Finally, the predicted fluence maps are automatically postprocessed by Gaussian deconvolution operations and imported into a commercial treatment planning system (TPS) for plan integrity check and visualization. The AI agent was built and tested upon 231 oropharyngeal IMRT plans from a TPS plan library. 200/16/15 plans were assigned for training/validation/testing, respectively. Only the primary plans in the sequential boost regime were studied. All plans were normalized to 44 Gy prescription (2 Gy/fx). A customized Harr wavelet loss was adopted for fluence map comparison during the training of the PyraNet. For test cases, isodose distributions in AI plans and TPS plans were qualitatively evaluated for overall dose distributions. Key dosimetric metrics were compared by Wilcoxon signed‐rank tests with a significance level of 0.05. Results: All 15 AI plans were successfully generated. Isodose gradients outside of PTV in AI plans were comparable to those of the TPS plans. After PTV coverage normalization, Dmean of left parotid (DAI = 23.1 ± 2.4 Gy; DTPS = 23.1 ± 2.0 Gy), right parotid (DAI = 23.8 ± 3.0 Gy; DTPS = 23.9 ± 2.3 Gy), and oral cavity (DAI = 24.7 ± 6.0 Gy; DTPS = 23.9 ± 4.3 Gy) in the AI plans and the TPS plans were comparable without statistical significance. AI plans achieved comparable results for maximum dose at 0.01cc of brainstem (DAI = 15.0 ± 2.1 Gy; DTPS = 15.5 ± 2.7 Gy) and cord + 5mm (DAI = 27.5 ± 2.3 Gy; DTPS = 25.8 ± 1.9 Gy) without clinically relevant differences, but body Dmax results (DAI = 121.1 ± 3.9 Gy; DTPS = 109.0 ± 0.9 Gy) were higher than the TPS plan results. The AI agent needed ~3 s for predicting fluence maps of an IMRT plan. Conclusions: With rapid and fully automated execution, the developed AI agent can generate complex head‐and‐neck IMRT plans with acceptable dosimetry quality. This approach holds great potential for clinical applications in preplanning decision‐making and real‐time planning. [ABSTRACT FROM AUTHOR]

Develop a machine learning-based method to generate multi-contrast anatomical textures in the 4D extended cardiac-torso (XCAT) phantom for more realistic imaging simulations. As a pilot study, we synthesize CT and CBCT textures in the chest region. For training purposes, major organs and gross tumor volumes (GTVs) in chest region were segmented from real patient images and assigned to different HU values to generate organ maps, which resemble the XCAT images. A dual-discriminator conditional-generative adversarial network (D-CGAN) was developed to synthesize anatomical textures in the corresponding organ maps. The D-CGAN was uniquely designed with two discriminators, one trained for the body and the other for the tumor. Various XCAT phantoms were input to the D-CGAN to generate textured XCAT phantoms. The D-CGAN model was trained separately using 62 CT and 63 CBCT images from lung SBRT patients to generate multi-contrast textured XCAT (MT-XCAT). The MT-XCAT phantoms were evaluated by comparing the intensity histograms and radiomic features with those from real patient images using Wilcoxon rank-sum test. The visual examination demonstrated that the MT-XCAT phantoms presented similar general contrast and anatomical textures as CT and CBCT images. The mean HU of the MT-XCAT-CT and MT-XCAT-CBCT were and , compared with that of real CT () and CBCT (). The majority of radiomic features from the MT-XCAT phantoms followed the same distribution as the real images according to the Wilcoxon rank-sum test, except for limited second-order features. The study demonstrated the feasibility of generating realistic MT-XCAT phantoms using D-CGAN. The MT-XCAT phantoms can be further expanded to include other modalities (MRI, PET, ultrasound, etc) under the same scheme. This crucial development greatly enhances the value of the phantom for various clinical applications, including testing and optimizing novel imaging techniques, validation of radiomics analysis methods, and virtual clinical trials. [ABSTRACT FROM AUTHOR]

Edges tend to be over-smoothed in total variation (TV) regularized under-sampled images. In this paper, symmetric residual convolutional neural network (SR-CNN), a deep learning based model, was proposed to enhance the sharpness of edges and detailed anatomical structures in under-sampled cone-beam computed tomography (CBCT). For training, CBCT images were reconstructed using TV-based method from limited projections simulated from the ground truth CT, and were fed into SR-CNN, which was trained to learn a restoring pattern from under-sampled images to the ground truth. For testing, under-sampled CBCT was reconstructed using TV regularization and was then augmented by SR-CNN. Performance of SR-CNN was evaluated using phantom and patient images of various disease sites acquired at different institutions both qualitatively and quantitatively using structure similarity (SSIM) and peak signal-to-noise ratio (PSNR). SR-CNN substantially enhanced image details in the TV-based CBCT across all experiments. In the patient study using real projections, SR-CNN augmented CBCT images reconstructed from as low as 120 half-fan projections to image quality comparable to the reference fully-sampled FDK reconstruction using 900 projections. In the tumor localization study, improvements in the tumor localization accuracy were made by the SR-CNN augmented images compared with the conventional FDK and TV-based images. SR-CNN demonstrated robustness against noise levels and projection number reductions and generalization for various disease sites and datasets from different institutions. Overall, the SR-CNN-based image augmentation technique was efficient and effective in considerably enhancing edges and anatomical structures in under-sampled 3D/4D-CBCT, which can be very valuable for image-guided radiotherapy. [ABSTRACT FROM AUTHOR]

The purpose of this work was to investigate the potential relationship between radiomic features extracted from pre-treatment x-ray CT images and clinical outcomes following stereotactic body radiation therapy (SBRT) for non-small-cell lung cancer (NSCLC). Seventy patients who received SBRT for stage-1 NSCLC were retrospectively identified. The tumor was contoured on pre-treatment free-breathing CT images, from which 43 quantitative radiomic features were extracted to collectively capture tumor morphology, intensity, fine-texture, and coarse-texture. Treatment failure was defined based on cancer recurrence, local cancer recurrence, and non-local cancer recurrence following SBRT. The univariate association between each radiomic feature and each clinical endpoint was analyzed using Welch's t-test, and p-values were corrected for multiple hypothesis testing. Multivariate associations were based on regularized logistic regression with a singular value decomposition to reduce the dimensionality of the radiomics data. Two features demonstrated a statistically significant association with local failure: Homogeneity2 (p = 0.022) and Long-Run-High-Gray-Level-Emphasis (p = 0.048). These results indicate that relatively dense tumors with a homogenous coarse texture might be linked to higher rates of local recurrence. Multivariable logistic regression models produced maximum values of , and , for the recurrence, local recurrence, and non-local recurrence endpoints, respectively. The CT-based radiomic features used in this study may be more associated with local failure than non-local failure following SBRT for stage I NSCLC. This finding is supported by both univariate and multivariate analyses. [ABSTRACT FROM AUTHOR]

The four-dimensional (4-D) eXtended CArdiac-Torso (XCAT) series of phantoms was developed to provide accurate computerized models of the human anatomy and physiology. The XCAT series encompasses a vast population of phantoms of varying ages from newborn to adult, each including parameterized models for the cardiac and respiratory motions. With great flexibility in the XCAT’s design, any number of body sizes, different anatomies, cardiac or respiratory motions or patterns, patient positions and orientations, and spatial resolutions can be simulated. As such, the XCAT phantoms are gaining a wide use in biomedical imaging research. There they can provide a virtual patient base from which to quantitatively evaluate and improve imaging instrumentation, data acquisition, techniques, and image reconstruction and processing methods which can lead to improved image quality and more accurate clinical diagnoses. The phantoms have also found great use in radiation dosimetry, radiation therapy, medical device design, and even the security and defense industry. This review paper highlights some specific areas in which the XCAT phantoms have found use within biomedical imaging and other fields. From these examples, we illustrate the increasingly important role that computerized phantoms and computer simulation are playing in the research community. [ABSTRACT FROM PUBLISHER]

Purpose: Current approaches using deformable vector field (DVF) for motion-compensated 4D-cone beam CT (CBCT) reconstruction typically utilize an isotropically smoothed DVF between different respiration phases. Such isotropically smoothed DVF does not work well if sliding motion exists between neighboring organs. This study investigated an anisotropic motion modeling scheme by extracting organ boundary local motions (e.g., sliding) and incorporated them into 4D-CBCT reconstruction to optimize the motion modeling and reconstruction methods. Methods: Initially, a modified simultaneous algebraic reconstruction technique (mSART) was applied to reconstruct high quality reference phase CBCT using all phase projections. The initial DVFs were precalculated and subsequently updated to achieve the optimized solution. During the DVF update, sliding motion estimation was performed by matching the measured projections to the forward projection of the deformed reference phase CBCT. In this process, each moving organ boundary was first segmented. The normal vectors of the boundary DVF were then extracted and incorporated for further DVF optimization. The regularization term in the objective function adaptively regularizes the DVF by (1) isotopically smoothing the DVF within each organ; (2) smoothing the DVF at boundary along the normal direction; and (3) leaving the tangent direction of boundary DVF unsmoothed (i.e., allowing for sliding motion). A nonlinear conjugate gradient optimizer was used. The algorithm was validated on a digital cubic tube phantom with sliding motion, nonuniform rotational B-spline based cardiac-torso (NCAT) phantom, and two anonymized patient data. The relative reconstruction error (RE), the motion trajectory's root mean square error (RMSE) together with its maximum error (MaxE), and the Dice coefficient of the lung boundary were calculated to evaluate the algorithm performance. Results: For the cubic tube and NCAT phantom tests, the REs are 10.2% and 7.4% with sliding motion compensation, compared to 13.4% and 8.9% without sliding modeling. The motion trajectory's RMSE and MaxE for NCAT phantom tests are 0.5 and 0.8 mm with sliding motion constraint compared to 3.5 and 7.3 mm without sliding motion modeling. The Dice coefficients for both NCAT phantom and the patients show a consistent trend that sliding motion constraint achieves better similarity for segmented lung boundary compared with the ground truth or patient reference. Conclusions: A sliding motion-compensated 4D-CBCT reconstruction and the motion modeling scheme was developed. Both phantom and patient study demonstrated the improved accuracy and motion modeling accuracy in reconstructed 4D-CBCT. [ABSTRACT FROM AUTHOR]

Purpose: Lung cancer treatment is susceptible to treatment errors caused by interfractional anatomical and respirational variations of the patient. On-board treatment dose verification is especially critical for the lung stereotactic body radiation therapy due to its high fractional dose. This study investigates the feasibility of using cone-beam (CB)CT images estimated by a motion modeling and free-form deformation (MM-FD) technique for on-board dose verification. Methods: Both digital and physical phantom studies were performed. Various interfractional variations featuring patient motion pattern change, tumor size change, and tumor average position change were simulated from planning CT to on-board images. The doses calculated on the planning CT (planned doses), the on-board CBCT estimated by MM-FD (MM-FD doses), and the on-board CBCT reconstructed by the conventional Feldkamp-Davis-Kress (FDK) algorithm (FDK doses) were compared to the on-board dose calculated on the "gold-standard" on-board images (gold-standard doses). The absolute deviations of minimum dose (?Dmin), maximum dose (?Dmax), and mean dose (?Dmean), and the absolute deviations of prescription dose coverage (?V100%) were evaluated for the planning target volume (PTV). In addition, 4D on-board treatment dose accumulations were performed using 4D-CBCT images estimated by MM-FD in the physical phantom study. The accumulated doses were compared to those measured using optically stimulated luminescence (OSL) detectors and radiochromic films. Results: Compared with the planned doses and the FDK doses, the MM-FD doses matched much better with the gold-standard doses. For the digital phantom study, the average (± standard deviation) ?Dmin, ?Dmax, ?Dmean, and ?V100% (values normalized by the prescription dose or the total PTV) between the planned and the gold-standard PTV doses were 32.9% (±28.6%), 3.0% (±2.9%), 3.8% (±4.0%), and 15.4% (±12.4%), respectively. The corresponding values of FDK PTV doses were 1.6% (±1.9%), 1.2% (±0.6%), 2.2% (±0.8%), and 17.4% (±15.3%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.3% (±0.2%), 0.9% (±0.6%), 0.6% (±0.4%), and 1.0% (±0.8%), respectively. Similarly, for the physical phantom study, the average ?Dmin, ?Dmax, ?Dmean, and ?V100% of planned PTV doses were 38.1% (±30.8%), 3.5% (±5.1%), 3.0% (±2.6%), and 8.8% (±8.0%), respectively. The corresponding values of FDK PTV doses were 5.8% (±4.5%), 1.6% (±1.6%), 2.0% (±0.9%), and 9.3% (±10.5%), respectively. In contrast, the corresponding values of MM-FD PTV doses were 0.4% (±0.8%), 0.8% (±1.0%), 0.5% (±0.4%), and 0.8% (±0.8%), respectively. For the 4D dose accumulation study, the average (± standard deviation) absolute dose deviation (normalized by local doses) between the accumulated doses and the OSL measured doses was 3.3% (±2.7%). The average gamma index (3%/3 mm) between the accumulated doses and the radiochromic film measured doses was 94.5% (±2.5%). Conclusions: MM-FD estimated 4D-CBCT enables accurate on-board dose calculation and accumulation for lung radiation therapy. It can potentially be valuable for treatment quality assessment and adaptive radiation therapy. [ABSTRACT FROM AUTHOR]

Purpose: To construct a robotic SPECT system and to demonstrate its capability to image a thorax phantom on a radiation therapy flat-top couch, as a step toward onboard functional and molecular imaging in radiation therapy. Methods: A robotic SPECT imaging system was constructed utilizing a gamma camera detector (Digirad 2020tc) and a robot (KUKA KR150 L110 robot). An imaging study was performed with a phantom (PET CT Phantom™), which includes five spheres of 10, 13, 17, 22, and 28 mm diameters. The phantom was placed on a flat-top couch. SPECT projections were acquired either with a parallel-hole collimator or a single-pinhole collimator, both without background in the phantom and with background at 1/10th the sphere activity concentration. The imaging trajectories of parallel-hole and pinhole collimated detectors spanned 180° and 228°, respectively. The pinhole detector viewed an off-centered spherical common volume which encompassed the 28 and 22 mm spheres. The common volume for parallel-hole system was centered at the phantom which encompassed all five spheres in the phantom. The maneuverability of the robotic system was tested by navigating the detector to trace the phantom and flat-top table while avoiding collision and maintaining the closest possible proximity to the common volume. The robot base and tool coordinates were used for image reconstruction. Results: The robotic SPECT system was able to maneuver parallel-hole and pinhole collimated SPECT detectors in close proximity to the phantom, minimizing impact of the flat-top couch on detector radius of rotation. Without background, all five spheres were visible in the reconstructed parallel-hole image, while four spheres, all except the smallest one, were visible in the reconstructed pinhole image. With background, three spheres of 17,22, and 28 mm diameters were readily observed with the parallel-hole imaging, and the targeted spheres (22 and 28 mm diameters) were readily observed in the pinhole region-of-interest imaging. Conclusions: Onboard SPECT could be achieved by a robot maneuvering a SPECT detector about patients in position for radiation therapy on a flat-top couch. The robot inherent coordinate frames could be an effective means to estimate detector pose for use in SPECT image reconstruction. [ABSTRACT FROM AUTHOR]

Purpose: The authors have recently developed a novel 4D-MRI technique for imaging organ respiratory motion employing cine acquisition in the axial plane and using body area (BA) as a respiratory surrogate. A potential disadvantage associated with axial image acquisition is the space-dependent phase shift in the superior-inferior (SI) direction, i.e., different axial slice positions reach the respiratory peak at different respiratory phases. Since respiratory motion occurs mostly in the SI and anterior-posterior (AP) directions, sagittal image acquisition, which embeds motion information in these two directions, is expected to be more robust and less affected by phase-shift than axial image acquisition. This study aims to develop and evaluate a 4D-MRI technique using sagittal image acquisition. Methods: The authors evaluated axial BA and sagittal BA using both 4D-CT images (11 cancer patients) and cine MR images (6 healthy volunteers and 1 cancer patient) by comparing their corresponding space-dependent phase-shift in the SI direction (δSI SPS ) and in the lateral direction (δLAT SPS ), respectively. To evaluate sagittal BA 4D-MRI method, a motion phantom study and a digital phantom study were performed. Additionally, six patients who had cancer(s) in the liver were prospectively enrolled in this study. For each patient, multislice sagittal MR images were acquired for 4D-MRI reconstruction. 4D retrospective sorting was performed based on respiratory phases. Single-slice cine MRI was also acquired in the axial, coronal, and sagittal planes across the tumor center from which tumor motion trajectories in the SI, AP, and medial-lateral (ML) directions were extracted and used as references from comparison. All MR images were acquired in a 1.5 T scanner using a steady-state precession sequence (frame rate ~3 frames/s). Results: 4D-CT scans showed that δSI SPS was significantly greater than δLAT SPS (p-value: 0.012); the median phase-shift was 16.9% and 7.7%, respectively. Body surface motion measurement from axial and sagittal MR cines also showed δSI SPS was significantly greater than δLAT SPS . The median δSI SPS and δLAT SPS was 11.0% and 9.2% (p-value = 0.008), respectively. Tumor motion trajectories from 4D-MRI matched with those from single-slice cine MRI: the mean (±SD) absolute differences in tumor motion amplitude between the two were 1.5 ± 1.6 mm, 2.1 ± 1.9 mm, and 1.1 ± 1.0 mm in the SI, ML, and AP directions from this patient study. Conclusions: Space-dependent phase shift is less problematic for sagittal acquisition than for axial acquisition. 4D-MRI using sagittal acquisition was successfully carried out in patients with hepatic tumors. [ABSTRACT FROM AUTHOR]

Purpose: To investigate the feasibility of and challenges yet to be addressed to measure dose from low energy (effective energy <50 keV) brachytherapy sources (Pd-103, Cs-131, and I-125) using polyurethane based 3D dosimeters with optical CT. Methods: The authors' evaluation used the following sources: models 200 (Pd-103), CS-1 Rev2 (Cs- 131), and 6711 (I-125). The authors used the Monte Carlo radiation transport code MCNP5, simulations with the ScanSim optical tomography simulation software, and experimental measurements with PRESAGE ® dosimeters/optical CT to investigate the following: (1) the water equivalency of conventional (density = 1.065 g/cm³) and deformable (density = 1.02 g/cm³) formulations of polyurethane dosimeters, (2) the scatter conditions necessary to achieve accurate dosimetry for low energy photon seeds, (3) the change in photon energy spectrum within the dosimeter as a function of distance from the source in order to determine potential energy sensitivity effects, (4) the optimal delivered dose to balance optical transmission (per projection) with signal to noise ratio in the reconstructed dose distribution, and (5) the magnitude and characteristics of artifacts due to the presence of a channel in the dosimeter. Monte Carlo simulations were performed using both conventional and deformable dosimeter formulations. For verification, 2.8 Gy at 1 cm was delivered in 92 h using an I-125 source to a PRESAGE ® dosimeter with conventional formulation and a central channel with 0.0425 cm radius for source placement. The dose distribution was reconstructed with 0.02 and 0.04 cm³ voxel size using the Duke midsized optical CT scanner (DMOS). Results: While the conventional formulation overattenuates dose from all three sources compared to water, the current deformable formulation has nearly water equivalent attenuation properties for Cs-131 and I-125, while underattenuating for Pd-103. The energy spectrum of each source is relatively stable within the first 5 cm especially for I-125. The inherent assumption of radial symmetry in the TG43 geometry leads to a linear increase in sample points within the 3D dosimeter as a function of distance away from the source, which partially offsets the decreasing signal. Simulations of dose reconstruction using optical CT showed the feasibility of reconstructing dose out to a radius of 10 cm without saturating projection images using an optimal dose and high dynamic range scanning; the simulations also predicted that reconstruction artifacts at the channel surface due to a small discrepancy in refractive index should be negligible. Agreement of the measured with calculated radial dose function for I-125 was within 5% between 0.3 and 2.5 cm from the source, and the median difference of measured from calculated anisotropy function was within 5% between 0.3 and 2.0 cm from the source. Conclusions: 3D dosimetry using polyurethane dosimeters with optical CT looks to be a promising application to verify dosimetric distributions surrounding low energy brachytherapy sources. [ABSTRACT FROM AUTHOR]

Purpose: Currently, no 3D or 4D volumetric x-ray imaging techniques are available for intrafraction verification of target position during actual treatment delivery or in-between treatment beams, which is critical for stereotactic radiosurgery (SRS) and stereotactic body radiation therapy (SBRT) treatments. This study aims to develop a limited-angle intrafraction verification (LIVE) system to use prior information, deformation models, and limited angle kV-MV projections to verify target position intrafractionally. Methods: The LIVE system acquires limited-angle kV projections simultaneously during arc treatment delivery or in-between static 3D/IMRT treatment beams as the gantry moves from one beam to the next. Orthogonal limited-angle MV projections are acquired from the beamˈs eye view (BEV) exit fluence of arc treatment beam or in-between static beams to provide additional anatomical information. MV projections are converted to kV projections using a linear conversion function. Patient prior planning CT at one phase is used as the prior information, and the on-board patient volume is considered as a deformation of the prior images. The deformation field is solved using the data fidelity constraint, a breathing motion model extracted from the planning 4D-CT based on principal component analysis (PCA) and a free-form deformation (FD) model. LIVE was evaluated using a 4D digital extended cardiac torso phantom (XCAT) and a CIRS 008A dynamic thoracic phantom. In the XCAT study, patient breathing pattern and tumor size changes were simulated from CT to treatment position. In the CIRS phantom study, the artificial target in the lung region experienced both size change and position shift from CT to treatment position. Varian Truebeam research mode was used to acquire kV and MV projections simultaneously during the delivery of a dynamic conformal arc plan. The reconstruction accuracy was evaluated by calculating the 3D volume percentage difference (VPD) and the center of mass (COM) difference of the tumor in the true on-board images and reconstructed images. Results: In both simulation and phantom studies, LIVE achieved substantially better reconstruction accuracy than reconstruction using PCA or FD deformation model alone. In the XCAT study, the average VPD and COM differences among different patient scenarios for LIVE system using orthogonal 30° scan angles were 4.3% and 0.3 mm when using kV+BEV MV. Reducing scan angle to 15° increased the average VPD and COM differences to 15.1% and 1.7 mm. In the CIRS phantom study, the VPD and COM differences for the LIVE system using orthogonal 30° scan angles were 6.4% and 1.4 mm. Reducing scan angle to 15° increased the VPD and COM differences to 51.9% and 3.8 mm. Conclusions: The LIVE system has the potential to substantially improve intrafraction target localization accuracy by providing volumetric verification of tumor position simultaneously during arc treatment delivery or in-between static treatment beams. With this improvement, LIVE opens up a new avenue for margin reduction and dose escalation in both fractionated treatments and SRS and SBRT treatments. [ABSTRACT FROM AUTHOR]

Contemporary medical imaging is becoming increasingly more quantitative. The emerging field of radiomics is a leading example. By translating unstructured data (i.e., images) into structured data (i.e., imaging features), radiomics can potentially characterize clinically useful imaging phenotypes. In this paper, an exploratory radiomics approach is used to investigate the potential association between quantitative imaging features and pulmonary function in CT images. Thirty-nine radiomic features were extracted from the lungs of 64 patients as potential imaging biomarkers for pulmonary function. Collectively, these features capture the morphology of the lungs, as well as intensity variations, fine-texture, and coarse-texture of the pulmonary tissue. The extracted lung radiomics data was compared to conventional pulmonary function tests. In general, patients with larger lungs of homogeneous, low attenuating pulmonary tissue (as measured via radiomics) were found to be associated with poor spirometry performance and a lower diffusing capacity for carbon monoxide. Unsupervised dynamic data clustering revealed subsets of patients with similar lung radiomic patterns that were found to be associated with similar forced expiratory volume in one second (FEV1) measurements. This implies that patients with similar radiomic feature vectors also presented with comparable spirometry performance, and were separable by varying degrees of pulmonary function as measured by imaging. [ABSTRACT FROM AUTHOR]

Purpose: To develop a technique to estimate onboard 4D-CBCT using prior information and limited-angle projections for potential 4D target verification of lung radiotherapy. Methods: Each phase of onboard 4D-CBCT is considered as a deformation from one selected phase (prior volume) of the planning 4D-CT. The deformation field maps (DFMs) are solved using a motion modeling and free-form deformation (MM-FD) technique. In the MM-FD technique, the DFMs are estimated using a motion model which is extracted from planning 4D-CT based on principal component analysis (PCA). The motion model parameters are optimized by matching the digitally reconstructed radiographs of the deformed volumes to the limited-angle onboard projections (data fidelity constraint). Afterward, the estimated DFMs are fine-tuned using a FD model based on data fidelity constraint and deformation energy minimization. The 4D digital extended-cardiac-torso phantom was used to evaluate the MM-FD technique. A lung patient with a 30 mm diameter lesion was simulated with various anatomical and respirational changes from planning 4D-CT to onboard volume, including changes of respiration amplitude, lesion size and lesion average-position, and phase shift between lesion and body respiratory cycle. The lesions were contoured in both the estimated and 'ground-truth' onboard 4D-CBCT for comparison. 3D volume percentage-difference (VPD) and center-of-mass shift (COMS) were calculated to evaluate the estimation accuracy of three techniques: MM-FD, MM-only, and FD-only. Different onboard projection acquisition scenarios and projection noise levels were simulated to investigate their effects on the estimation accuracy. Results: For all simulated patient and projection acquisition scenarios, the mean VPD (±S.D.)/COMS (±S.D.) between lesions in prior images and 'ground-truth' onboard images were 136.11% (±42.76%)/15.5 mm (±3.9 mm). Using orthogonal-view 15°-each scan angle, the mean VPD/COMS between the lesion in estimated and 'ground-truth' onboard images for MM-only, FD-only, and MM-FD techniques were 60.10% (±27.17%)/4.9 mm (±3.0 mm), 96.07% (±31.48%)/12.1 mm (±3.9 mm) and 11.45% (±9.37%)/1.3 mm (±1.3 mm), respectively. For orthogonal-view 30°-each scan angle, the corresponding results were 59.16% (±26.66%)/4.9 mm (±3.0 mm), 75.98% (±27.21%)/9.9 mm (±4.0 mm), and 5.22% (±2.12%)/0.5 mm (±0.4 mm). For single-view scan angles of 3°, 30°, and 60°, the results for MM-FD technique were 32.77% (±17.87%)/3.2 mm (±2.2 mm), 24.57% (±18.18%)/2.9 mm (±2.0 mm), and 10.48% (±9.50%)/1.1 mm (±1.3 mm), respectively. For projection angular-sampling-intervals of 0.6°, 1.2°, and 2.5° with the orthogonal-view 30°-each scan angle, the MM-FD technique generated similar VPD (maximum deviation 2.91%) and COMS (maximum deviation 0.6 mm), while sparser sampling yielded larger VPD/COMS. With equal number of projections, the estimation results using scattered 360° scan angle were slightly better than those using orthogonal-view 30°-each scan angle. The estimation accuracy of MM-FD technique declined as noise level increased. Conclusions: The MM-FD technique substantially improves the estimation accuracy for onboard 4D-CBCT using prior planning 4D-CT and limited-angle projections, compared to the MM-only and FD-only techniques. It can potentially be used for the inter/intrafractional 4D-localization verification. [ABSTRACT FROM AUTHOR]

Purpose: To develop a novel on-board imaging technique which allows generation of virtual monochromatic (VM) cone-beam CT (CBCT) with a selected energy from combined kilovoltage (kV)/megavoltage (MV) beam projections. Methods: With the current orthogonal kV/MV imaging hardware equipped in modern linear accelerators, both MV projections (from gantry angle of 0°-100°) and kV projections (90°-200°) were acquired as gantry rotated a total of 110°. A selected range of overlap projections between 90° to 100° were then decomposed into two material projections using experimentally determined parameters from orthogonally stacked aluminum and acrylic step-wedges. Given attenuation coefficients of aluminum and acrylic at a predetermined energy, one set of VM projections could be synthesized from two corresponding sets of decomposed projections. Two linear functions were generated using projection information at overlap angles to convert kV and MV projections at nonoverlap angles to approximate VM projections for CBCT reconstruction. The contrast-to-noise ratios (CNRs) were calculated for different inserts in VM CBCTs of a CatPhan phantom with various selected energies and compared with those in kV and MV CBCTs. The effect of overlap projection number on CNR was evaluated. Additionally, the effect of beam orientation was studied by scanning the CatPhan sandwiched with two 5 cm solid-water phantoms on both lateral sides and an electronic density phantom with two metal bolt inserts. Results: Proper selection of VM energy [30 and 40 keV for low-density polyethylene (LDPE), polymethylpentene, 2 MeV for Delrin] provided comparable or even better CNR results as compared with kV or MV CBCT. An increased number of overlap kV and MV projection demonstrated only marginal improvements of CNR for different inserts (with the exception of LDPE) and therefore one projection overlap was found to be sufficient for the CatPhan study. It was also evident that the optimal CBCT image quality was achieved when MV beams penetrated through the heavy attenuation direction of the object. Conclusions: A novel technique was developed to generate VM CBCTs from kV/MV projections. This technique has the potential to improve CNR at selected VM energies and to suppress artifacts at appropriate beam orientations. [ABSTRACT FROM AUTHOR]

Purpose: In order to achieve functional and molecular imaging as patients are in position for radiation therapy, a robotic multipinhole SPECT system is being developed. Alignment of the SPECT system-to the linear accelerator (LINAC) coordinate frame and to the coordinate frames of other on-board imaging systems such as cone-beam CT (CBCT)-is essential for target localization and image reconstruction. An alignment method that utilizes line sources and one pinhole projection is proposed and investigated to achieve this goal. Potentially, this method could also be applied to the calibration of the other pinhole SPECT systems. Methods: An alignment model consisting of multiple alignment parameters was developed which maps line sources in three-dimensional (3D) space to their two-dimensional (2D) projections on the SPECT detector. In a computer-simulation study, 3D coordinates of line-sources were defined in a reference room coordinate frame, such as the LINAC coordinate frame. Corresponding 2D line-source projections were generated by computer simulation that included SPECT blurring and noise effects. The Radon transform was utilized to detect angles (α) and offsets (ρ) of the line-source projections. Alignment parameters were then estimated by a nonlinear least squares method, based on the α and ρ values and the alignment model. Alignment performance was evaluated as a function of number of line sources, Radon transform accuracy, finite line-source width, intrinsic camera resolution, Poisson noise, and acquisition geometry. Experimental evaluations were performed using a physical line-source phantom and a pinhole-collimated gamma camera attached to a robot. Results: In computer-simulation studies, when there was no error in determining angles (α) and offsets (ρ) of the measured projections, six alignment parameters (three translational and three rotational) were estimated perfectly using three line sources. When angles (α) and offsets (ρ) were provided by the Radon transform, estimation accuracy was reduced. The estimation error was associated with rounding errors of Radon transform, finite line-source width, Poisson noise, number of line sources, intrinsic camera resolution, and detector acquisition geometry. Statistically, the estimation accuracy was significantly improved by using four line sources rather than three and by thinner line-source projections (obtained by better intrinsic detector resolution). With five line sources, median errors were 0.2 mm for the detector translations, 0.7 mm for the detector radius of rotation, and less than 0.5° for detector rotation, tilt, and twist. In experimental evaluations, average errors relative to a different, independent registration technique were about 1.8 mm for detector translations, 1.1 mm for the detector radius of rotation (ROR), 0.5° and 0.4° for detector rotation and tilt, respectively, and 1.2° for detector twist. Conclusions: Alignment parameters can be estimated using one pinhole projection of line sources. Alignment errors are largely associated with limited accuracy of the Radon transform in determining angles (α) and offsets (ρ) of the line-source projections. This alignment method may be important for multipinhole SPECT, where relative pinhole alignment may vary during rotation. For pinhole and multipinhole SPECT imaging on-board radiation therapy machines, the method could provide alignment of SPECT coordinates with those of CBCT and the LINAC. [ABSTRACT FROM AUTHOR]

Purpose: The purpose of this study is to evaluate the performance of a recently developed benchtop dual cone-beam computed tomography (CBCT) system with two orthogonally placed tube/detector sets. Methods: The benchtop dual CBCT system consists of two orthogonally placed 40 × 30 cm flat-panel detectors and two conventional x-ray tubes with two individual high-voltage generators sharing the same rotational axis. The x-ray source to detector distance is 150 cm and x-ray source to rotational axis distance is 100 cm for both subsystems. The objects are scanned through 200° of rotation. The dual CBCT system utilized 110° of projection data from one detector and 90° from the other while the two individual single CBCTs utilized 200° data from each detector. The system performance was characterized in terms of uniformity, contrast, spatial resolution, noise power spectrum, and CT number linearity. The uniformities, within the axial slice and along the longitudinal direction, and noise power spectrum were assessed by scanning a water bucket; the contrast and CT number linearity were measured using the Catphan phantom; and the spatial resolution was evaluated using a tungsten wire phantom. A skull phantom and a ham were also scanned to provide qualitative evaluation of high- and low-contrast resolution. Each measurement was compared between dual and single CBCT systems. Results: Compared to single CBCT, the dual CBCT presented: (1) a decrease in uniformity by 1.9% in axial view and 1.1% in the longitudinal view, as averaged for four energies (80, 100, 125, and 150 kVp); (2) comparable or slightly better contrast (0∼25 HU) for low-contrast objects and comparable contrast for high-contrast objects; (3) comparable spatial resolution; (4) comparable CT number linearity with R2 ≥ 0.99 for all four tested energies; (5) lower noise power spectrum in magnitude. Dual CBCT images of the skull phantom and the ham demonstrated both high-contrast resolution and good soft-tissue contrast. Conclusions: The performance of a benchtop dual CBCT imaging system has been characterized and is comparable to that of a single CBCT. [ABSTRACT FROM AUTHOR]

Purpose: To provide a benchmark for seminal vesicle (SV) margin selection to account for intrafractional motion and to investigate the effectiveness of 2 motion surrogates in predicting intrafractional SV coverage.Methods and Materials: Fifteen prostate patients were studied. Each patient had 5 pairs (1 patient had 4 pairs) of pretreatment and posttreatment cone beam CTs (CBCTs). Each pair of CBCTs was registered on the basis of prostate fiducial markers. All pretreatment SVs were expanded with 1-, 2-, 3-, 4-, 5-, and 8-mm isotropic margins to form a series of planning target volumes, and their intrafractional coverage to the posttreatment SV determined the "ground truth" for exact coverage. Two motion surrogates, the center of mass (COM) and the border of contour, were evaluated by the use of Pearson product-moment correlation coefficient and exponential fitting for predicting SV underdosage. Action threshold of each surrogate was calculated. The margin for each surrogate was calculated according to a traditional margin recipe.Results: Ninety-five percent posttreatment SV coverage was achieved in 9%, 53%, 73%, 86%, 95%, and 97% of fractions with 1-, 2-, 3-, 4-, 5-, and 8-mm margins, respectively. The 5-mm margins provided 95% intrafractional SV coverage in over 90% of fractions. The correlation between the COM and border was weak, moderate, and strong in the left-right (L-R), anterior-posterior (A-P), and superior-inferior (S-I) directions, respectively. Exponential fitting gave the underdosage threshold of 4.5 and 7.0 mm for the COM and border. The Van Herk margin recipe recommended 0-, 0.5-, and 0.8-mm margins in the L-R, A-P, and S-I directions based on the COM, and 1.2-, 3.9-, and 2.5-mm margins based on the border.Conclusions: Five-millimeter isotropic margins for the SV constitute the minimum required to mitigate the intrafractional motion. Both the COM and the border are acceptable predictors for SV underdosage with 4.5- and 7.0-mm action threshold. Traditional margin based on the COM or border underestimates the margin. [ABSTRACT FROM AUTHOR]

Purpose: To evaluate the feasibility of a retrospective binning technique for 4-dimensional magnetic resonance imaging (4D-MRI) using body area (BA) as a respiratory surrogate. Methods and Materials: Seven patients with hepatocellular carcinoma (4 of 7) or liver metastases (3 of 7) were enrolled in an institutional review board-approved prospective study. All patients were simulated with both computed tomography (CT) and MRI to acquire 3-dimensinal and 4D images for treatment planning. Multiple-slice multiple-phase cine-MR images were acquired in the axial plane for 4D-MRI reconstruction. Image acquisition time per slice was set to 10-15 seconds. Single-slice 2-dimensinal cine-MR images were also acquired across the center of the tumor in orthogonal planes. Tumor motion trajectories from 4D-MRI, cine-MRI, and 4D-CT were analyzed in the superior–inferior (SI), anterior–posterior (AP), and medial–lateral (ML) directions, respectively. Their correlation coefficients (CC) and differences in tumor motion amplitude were determined. Tumor-to-liver contrast-to-noise ratio (CNR) was measured and compared between 4D-CT, 4D-MRI, and conventional T2-weighted fast spin echo MRI. Results: The means (±standard deviations) of CC comparing 4D-MRI with cine-MRI were 0.97 ± 0.03, 0.97 ± 0.02, and 0.99 ± 0.04 in SI, AP, and ML directions, respectively. The mean differences were 0.61 ± 0.17 mm, 0.32 ± 0.17 mm, and 0.14 ± 0.06 mm in SI, AP, and ML directions, respectively. The means of CC comparing 4D-MRI and 4D-CT were 0.95 ± 0.02, 0.94 ± 0.02, and 0.96 ± 0.02 in SI, AP, and ML directions, respectively. The mean differences were 0.74 ± 0.02 mm, 0.33 ± 0.13 mm, and 0.18 ± 0.07 mm in SI, AP, and ML directions, respectively. The mean tumor-to-tissue CNRs were 2.94 ± 1.51, 19.44 ± 14.63, and 39.47 ± 20.81 in 4D-CT, 4D-MRI, and T2-weighted MRI, respectively. Conclusions: The preliminary evaluation of our 4D-MRI technique results in oncologic patients demonstrates its potential usefulness to accurately measure tumor respiratory motion with improved tumor CNR compared with 4D-CT. [ABSTRACT FROM AUTHOR]

Purpose: To quantify uncertainties in delineating an internal target volume (ITV) and to understand how these uncertainties may be individually minimized for stereotactic body radiation therapy (SBRT) of early stage non-small cell lung cancer (NSCLC). Methods and Materials: Twenty patients with NSCLC who were undergoing SBRT were imaged with free-breathing 3-dimensional computed tomography (3DCT) and 10-phase 4-dimensional CT (4DCT) for delineating gross tumor volume (GTV)(3D) and ITV(10Phase) (ITV3). The maximum intensity projection (MIP) CT was also calculated from 10-phase 4DCT for contouring ITV(MIP) (ITV1). Then, ITV(COMB) (ITV2), ITV(10Phase+GTV3D) (ITV4), and ITV(10Phase+ITVCOMB) (ITV5) were generated by combining ITV(MIP) and GTV(3D), ITV(10phase) and GTV(3D), and ITV(10phase) and ITV(COMB), respectively. All 6 volumes (GTV(3D) and ITV1 to ITV5) were delineated in the same lung window by the same radiation oncologist. The percentage of volume difference (PVD) between any 2 different volumes was determined and was correlated to effective tumor diameter (ETD), tumor motion ranges, R(3D), and the amplitude variability of the recorded breathing signal (v) to assess their volume variations. Results: The mean (range) tumor motion (R(SI), R(AP), R(ML), and R(3D)) and breathing variability (v) were 7.6 mm (2-18 mm), 4.0 mm (2-8 mm), 3.3 mm (0-7.5 mm), 9.9 mm (4.1-18.7 mm), and 0.17 (0.07-0.37), respectively. The trend of volume variation was GTV(3D) Conclusion: Uncertainties in individualized ITVs for SBRT of early stage NSCLC could effectively be minimized by combining information from 3DCT, 4DCT, and MIP. If these images cannot be efficiently contoured, a combination of ITV(MIP) and GTV(3D) could be an effective alternative. [ABSTRACT FROM AUTHOR]

Purpose: In this study, we investigate a technique of matching internal target volumes (ITVs) in four-dimensional (4D) simulation computed tomography (CT) to the composite target volume in free-breathing on-board cone-beam (CB) CT. The technique is illustrated by using both phantom and patient cases. Methods and Materials: A dynamic phantom with a target ball simulating respiratory motion with various amplitude and cycle times was used to verify localization accuracy. The dynamic phantom was scanned using simulation CT with a phase-based retrospective sorting technique. The ITV was then determined based on 10 sets of sorted images. The size and epicenter of the ITV identified from 4D simulation CT images and the composite target volume identified from on-board CBCT images were compared to assess localization accuracy. Similarly, for two clinical cases of patients with lung cancer, ITVs defined from 4D simulation CT images and CBCT images were compared. Results: For the phantom, localization accuracy between the ITV in 4D simulation CT and the composite target volume in CBCT was within 1 mm, and ITV was within 8.7%. For patient cases, ITVs on simulation CT and CBCT were within 8.0%. Conclusion: This study shows that CBCT is a useful tool to localize ITV for targets affected by respiratory motion. Verification of the ITV from 4D simulation CT using on-board free-breathing CBCT is feasible for the target localization of lung tumors. [ABSTRACT FROM AUTHOR]

Purpose: High-precision intensity-modulated radiotherapy demands high patient positioning accuracy. On-board digital tomosynthesis (DTS) provides three-dimensional (3D) image guidance for daily positioning with a lower imaging dose, faster acquisition, and more geometric flexibility than 3D cone-beam computed tomography (CBCT). This clinical study evaluated DTS as a daily imaging technique for patient positioning and compared the results with 3D CBCT and two-dimensional (2D) radiography. Methods and Materials: Head and neck cancer patients undergoing intensity-modulated radiotherapy were studied. For each session, the patient was positioned using laser marks. On-board imaging data sets, including 2D kilovoltage radiographs, DTS, and CBCT, were obtained to measure the daily patient positioning variations. The mean and standard deviations of the positioning variations in the translational and rotational directions were calculated. The positioning differences among 2D radiography, DTS, and CBCT were analyzed. Results: Image data sets were collected from 65 treatment fractions for 10 patients. The systematic patient positioning variation was <0.10 cm and 1.0 degrees one dimensionally. The random variations were 0.27-0.34 cm in the translational and 0.93 degrees -1.99 degrees in the rotational direction. The mean vector isocenter variation was 0.48 cm. DTS with 40 degrees and 20 degrees scan angles in the coronal or sagittal directions yielded the same results for patient positioning. DTS performance was comparable to that of CBCT, with positioning differences of <0.1 cm and 0.5 degrees . The positioning difference between 2D radiography and DTS was approximately 0.1 cm and 0.2 cm in the vertical/longitudinal and lateral directions. Conclusion: Our results have demonstrated that DTS is a comparable 3D imaging technique to CBCT for daily patient positioning of head-and-neck patients as determined by manual registration of bony anatomy. [ABSTRACT FROM AUTHOR]