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Background: Volumetric reconstruction of magnetic resonance imaging (MRI) from sparse samples is desirable for 3D motion tracking and promises to improve magnetic resonance (MR)‐guided radiation treatment precision. Data‐driven sparse MRI reconstruction, however, requires large‐scale training datasets for prior learning, which is time‐consuming and challenging to acquire in clinical settings. Purpose: To investigate volumetric reconstruction of MRI from sparse samples of two orthogonal slices aided by sparse priors of two static 3D MRI through implicit neural representation (NeRP) learning, in support of 3D motion tracking during MR‐guided radiotherapy. Methods: A multi‐layer perceptron network was trained to parameterize the NeRP model of a patient‐specific MRI dataset, where the network takes 4D data coordinates of voxel locations and motion states as inputs and outputs corresponding voxel intensities. By first training the network to learn the NeRP of two static 3D MRI with different breathing motion states, prior information of patient breathing motion was embedded into network weights through optimization. The prior information was then augmented from two motion states to 31 motion states by querying the optimized network at interpolated and extrapolated motion state coordinates. Starting from the prior‐augmented NeRP model as an initialization point, we further trained the network to fit sparse samples of two orthogonal MRI slices and the final volumetric reconstruction was obtained by querying the trained network at 3D spatial locations. We evaluated the proposed method using 5‐min volumetric MRI time series with 340 ms temporal resolution for seven abdominal patients with hepatocellular carcinoma, acquired using golden‐angle radial MRI sequence and reconstructed through retrospective sorting. Two volumetric MRI with inhale and exhale states respectively were selected from the first 30 s of the time series for prior embedding and augmentation. The remaining 4.5‐min time series was used for volumetric reconstruction evaluation, where we retrospectively subsampled each MRI to two orthogonal slices and compared model‐reconstructed images to ground truth images in terms of image quality and the capability of supporting 3D target motion tracking. Results: Across the seven patients evaluated, the peak signal‐to‐noise‐ratio between model‐reconstructed and ground truth MR images was 38.02 ± 2.60 dB and the structure similarity index measure was 0.98 ± 0.01. Throughout the 4.5‐min time period, gross tumor volume (GTV) motion estimated by deforming a reference state MRI to model‐reconstructed and ground truth MRI showed good consistency. The 95‐percentile Hausdorff distance between GTV contours was 2.41 ± 0.77 mm, which is less than the voxel dimension. The mean GTV centroid position difference between ground truth and model estimation was less than 1 mm in all three orthogonal directions. Conclusion: A prior‐augmented NeRP model has been developed to reconstruct volumetric MRI from sparse samples of orthogonal cine slices. Only one exhale and one inhale 3D MRI were needed to train the model to learn prior information of patient breathing motion for sparse image reconstruction. The proposed model has the potential of supporting 3D motion tracking during MR‐guided radiotherapy for improved treatment precision and promises a major simplification of the workflow by eliminating the need for large‐scale training datasets. [ABSTRACT FROM AUTHOR]

Objective. Precision radiation therapy requires managing motions of organs at risk that occur during treatment. While methods have been developed for real-time respiratory motion tracking, non-breathing intra-fractional variations (including gastric contractile motion) have seen little attention to date. The purpose of this study is to develop a cyclic gastric contractile motion prediction model to support real-time management during radiotherapy. Approach. The observed short-term reproducibility of gastric contractile motion permitted development of a prediction model that (1) extracts gastric contraction motion phases from few minutes of golden angle stack of stars scanning (at patient positioning), (2) estimate gastric phase of real-time sampled data acquired during treatment delivery to these reconstructed phases and (3) predicting future gastric phase by linear extrapolation using estimation results from step 2 to account for processing and system latency times. Model was evaluated on three parameters including training time window for step 1, number of spokes for real-time sampling data in step 2 and future prediction time. Main results. The model was tested on a population of 20 min data samples from 25 scans from 15 patients. The mean prediction error with 10 spokes and 2 min training was 0.3 ± 0.1 mm (0.1–0.7 mm) with 5.1 s future time, slowly rising to 0.6 ± 0.2 mm (0.2–1.1 mm) for 6.8 s future time and then increasing rapidly for longer forward predictions, for an average 3.6 ± 0.5 mm (2.8–4.7 mm) HD95 of gastric motion. Results showed that reducing of train time window (5–2 min) does not influence the prediction performance, while using 5 spokes increased prediction errors. Significance. The proposed gastric motion prediction model has sufficiently accurate prediction performance to allow for sub-millimeter accuracy while allowing sufficient time for data processing and machine interaction and shows the potential for clinical implementation to support stomach motion tracking during radiotherapy. [ABSTRACT FROM AUTHOR]

Objective. Precision radiation therapy requires managing motions of organs at risk that occur during treatment. While methods have been developed for real-time respiratory motion tracking, non-breathing intra-fractional variations (including gastric contractile motion) have seen little attention to date. The purpose of this study is to develop a cyclic gastric contractile motion prediction model to support real-time management during radiotherapy. Approach. The observed short-term reproducibility of gastric contractile motion permitted development of a prediction model that (1) extracts gastric contraction motion phases from few minutes of golden angle stack of stars scanning (at patient positioning), (2) estimate gastric phase of real-time sampled data acquired during treatment delivery to these reconstructed phases and (3) predicting future gastric phase by linear extrapolation using estimation results from step 2 to account for processing and system latency times. Model was evaluated on three parameters including training time window for step 1, number of spokes for real-time sampling data in step 2 and future prediction time. Main results. The model was tested on a population of 20 min data samples from 25 scans from 15 patients. The mean prediction error with 10 spokes and 2 min training was 0.3 ± 0.1 mm (0.1–0.7 mm) with 5.1 s future time, slowly rising to 0.6 ± 0.2 mm (0.2–1.1 mm) for 6.8 s future time and then increasing rapidly for longer forward predictions, for an average 3.6 ± 0.5 mm (2.8–4.7 mm) HD95 of gastric motion. Results showed that reducing of train time window (5–2 min) does not influence the prediction performance, while using 5 spokes increased prediction errors. Significance. The proposed gastric motion prediction model has sufficiently accurate prediction performance to allow for sub-millimeter accuracy while allowing sufficient time for data processing and machine interaction and shows the potential for clinical implementation to support stomach motion tracking during radiotherapy. [ABSTRACT FROM AUTHOR]

Radiation therapy is a major component of cancer treatment pathways worldwide. The main aim of this treatment is to achieve tumor control through the delivery of ionizing radiation while preserving healthy tissues for minimal radiation toxicity. Because radiation therapy relies on accurate localization of the target and surrounding tissues, imaging plays a crucial role throughout the treatment chain. In the treatment planning phase, radiological images are essential for defining target volumes and organs‐at‐risk, as well as providing elemental composition (e.g., electron density) information for radiation dose calculations. At treatment, onboard imaging informs patient setup and could be used to guide radiation dose placement for sites affected by motion. Imaging is also an important tool for treatment response assessment and treatment plan adaptation. MRI, with its excellent soft tissue contrast and capacity to probe functional tissue properties, holds great untapped potential for transforming treatment paradigms in radiation therapy. The MR in Radiation Therapy ISMRM Study Group was established to provide a forum within the MR community to discuss the unmet needs and fuel opportunities for further advancement of MRI for radiation therapy applications. During the summer of 2021, the study group organized its first virtual workshop, attended by a diverse international group of clinicians, scientists, and clinical physicists, to explore our predictions for the future of MRI in radiation therapy for the next 25 years. This article reviews the main findings from the event and considers the opportunities and challenges of reaching our vision for the future in this expanding field. [ABSTRACT FROM AUTHOR]

Purpose: To develop a geometry‐informed deep learning framework for volumetric MRI with sub‐second acquisition time in support of 3D motion tracking, which is highly desirable for improved radiotherapy precision but hindered by the long image acquisition time. Methods: A 2D–3D deep learning network with an explicitly defined geometry module that embeds geometric priors of the k‐space encoding pattern was investigated, where a 2D generation network first augmented the sparsely sampled image dataset by generating new 2D representations of the underlying 3D subject. A geometry module then unfolded the 2D representations to the volumetric space. Finally, a 3D refinement network took the unfolded 3D data and outputted high‐resolution volumetric images. Patient‐specific models were trained for seven abdominal patients to reconstruct volumetric MRI from both orthogonal cine slices and sparse radial samples. To evaluate the robustness of the proposed method to longitudinal patient anatomy and position changes, we tested the trained model on separate datasets acquired more than one month later and evaluated 3D target motion tracking accuracy using the model‐reconstructed images by deforming a reference MRI with gross tumor volume (GTV) contours to a 5‐min time series of both ground truth and model‐reconstructed volumetric images with a temporal resolution of 340 ms. Results: Across the seven patients evaluated, the median distances between model‐predicted and ground truth GTV centroids in the superior‐inferior direction were 0.4 ± 0.3 mm and 0.5 ± 0.4 mm for cine and radial acquisitions, respectively. The 95‐percentile Hausdorff distances between model‐predicted and ground truth GTV contours were 4.7 ± 1.1 mm and 3.2 ± 1.5 mm for cine and radial acquisitions, which are of the same scale as cross‐plane image resolution. Conclusion: Incorporating geometric priors into deep learning model enables volumetric imaging with high spatial and temporal resolution, which is particularly valuable for 3D motion tracking and has the potential of greatly improving MRI‐guided radiotherapy precision. [ABSTRACT FROM AUTHOR]

Purpose: Medical physics residents (MPRs) will define and shape the future of physics in medicine. We sought to better understand the residency experience, as related to resilience and well‐being, through the lens of current MPRs and medical physicists (MPs) working with residents. Methods and materials: From February–May 2019, we conducted 32, 1‐h, confidential, semi‐structured interviews with MPs either currently enrolled in an accredited residency (n = 16) or currently employed by a department with an accredited residency (n = 16). Interviews centered on the topics of mentorship, work/life integration, and discrimination. Qualitative analysis methods were used to derive key themes from the interview transcripts. Results: With regard to the medical physics residency experience, four key themes emerged during qualitative analysis: the demanding nature of medical physics residencies, the negative impacts of residency on MPRs during training and beyond, strategies MPRs use to cope with residency stress, and the role of professional societies in addressing residency‐related change. Conclusions: Residency training is a stress‐inducing time in the path to becoming a board‐certified MP. By uncovering several sources of this stress, we have identified opportunities to support the resiliency and well‐being of MPs in training through recommendations by professional societies, programmatic changes, and interventions at the department and residency program director level for residency programs, as well as strategies that MPRs themselves can use to support well‐being on their career journey. [ABSTRACT FROM AUTHOR]

Abdominal organ motions introduce geometric uncertainties to radiotherapy. This study investigates a multi-temporal resolution 3D motion prediction scheme that accounts for both breathing and slow drifting motion in the abdomen in support of MRI-guided radiotherapy. Ten-minute MRI scans were acquired for 8 patients using a volumetric golden-angle stack-of-stars sequence. The first five-minutes was used for patient-specific motion modeling. Fast breathing motion was modeled from high temporal resolution radial k-space samples, which served as a navigator signal to sort k-space data into different bins for high spatial resolution reconstruction of breathing motion states. Slow drifting motion was modeled from a lower temporal resolution image time series which was reconstructed by sequentially combining a large number of breathing-corrected k-space samples. Principal components analysis (PCA) was performed on deformation fields between different motion states. Gaussian kernel regression and linear extrapolation were used to predict PCA coefficients of future motion states for breathing motion (340 ms ahead of acquisition) and slow drifting motion (8.5 s ahead of acquisition) respectively. k-space data from the remaining five-minutes was used to compare ground truth motions states obtained from retrospective reconstruction/deformation with predictions. Median distances between predicted and ground truth centroid positions of gross tumor volume (GTV) and organs at risk (OARs) were less than 1 mm on average. 95- percentile Hausdorff distances between predicted and ground truth GTV contours of various breathing motions states were 2 mm on average, which was smaller than the imaging resolution and 95-percentile Hausdorff distances between predicted and ground truth OAR contours of different slow drifting motion states were less than 0.2 mm. These results suggest that multi-temporal resolution motion models are capable of volumetric predictions of breathing and slow drifting motion with sufficient accuracy and temporal resolution for MRI-based tracking, and thus have potential for supporting MRI-guided abdominal radiotherapy. [ABSTRACT FROM AUTHOR]

The past decade has seen the increasing integration of magnetic resonance (MR) imaging into radiation therapy (RT). This growth can be contributed to multiple factors, including hardware and software advances that have allowed the acquisition of high‐resolution volumetric data of RT patients in their treatment position (also known as MR simulation) and the development of methods to image and quantify tissue function and response to therapy. More recently, the advent of MR‐guided radiation therapy (MRgRT) ‐ achieved through the integration of MR imaging systems and linear accelerators ‐ has further accelerated this trend. As MR imaging in RT techniques and technologies, such as MRgRT, gain regulatory approval worldwide, these systems will begin to propagate beyond tertiary care academic medical centers and into more community‐based health systems and hospitals, creating new opportunities to provide advanced treatment options to a broader patient population. Accompanying these opportunities are unique challenges related to their adaptation, adoption, and use including modification of hardware and software to meet the unique and distinct demands of MR imaging in RT, the need for standardization of imaging techniques and protocols, education of the broader RT community (particularly in regards to MR safety) as well as the need to continue and support research, and development in this space. In response to this, an ad hoc committee of the American Association of Physicists in Medicine (AAPM) was formed to identify the unmet needs, roadblocks, and opportunities within this space. The purpose of this document is to report on the major findings and recommendations identified. Importantly, the provided recommendations represent the consensus opinions of the committee's membership, which were submitted in the committee's report to the AAPM Board of Directors. In addition, AAPM ad hoc committee reports differ from AAPM task group reports in that ad hoc committee reports are neither reviewed nor ultimately approved by the committee's parent groups, including at the council and executive committee level. Thus, the recommendations given in this summary should not be construed as being endorsed by or official recommendations from the AAPM. [ABSTRACT FROM AUTHOR]

Purpose: Gastrointestinal motion patterns such as peristalsis and segmental contractions can alter the shape and position of the stomach and intestines with respect to other irradiated organs during radiation therapy. Unfortunately, these deformations are concealed by conventional four‐dimensional (4D)‐MRI techniques, which were developed to visualize respiratory motion by binning acquired data into respiratory motion states without considering the phases of GI motion. We present a method to reconstruct breathing‐compensated images showing the phases of periodic gastric motion and study the effect of this motion on regional anatomical structures. Methods: Sixty‐seven DCE‐MRI examinations were performed on patients undergoing MRI simulation for hepatocellular carcinoma using a golden‐angle stack‐of‐stars sequence that collected 2000 radial spokes over 5 min. The collected data were reconstructed using a method with integrated respiratory motion correction into a time series of 3D image volumes without visible breathing motion. From this series, a gastric motion signal was extracted by temporal filtering of time–intensity curves in the stomach. Using this motion signal, breathing‐corrected back‐projection images were sorted according to the gastric phase and reconstructed into 21 gastric motion state images showing the phases of gastric motion. Results: Reconstructed image volumes showed gastric motion states clearly with no visible breathing motion or related artifacts. The mean frequency of the gastric motion signal was 3 cycles/min with a standard deviation of 0.27 cycles/min. Conclusions: Periodic gastrointestinal motion can be visualized without confounding respiratory motion using the presented GI 4D MRI technique. GI 4D MRIs may help define internal target volumes for treatment planning, aid in planning organ at risk volume definition, or support motion model development for gastrointestinal motion tracking algorithms for real‐time MR‐guided radiation therapy. [ABSTRACT FROM AUTHOR]

Abdominal organ motions introduce geometric uncertainties to gastrointestinal radiotherapy. This study investigated slow drifting motion induced by changes of internal anatomic organ arrangements using a 3D radial MRI sequence with a scan length of 20 min. Breathing motion and cyclic GI motion were first removed through multi-temporal resolution image reconstruction. Slow drifting motion analysis was performed using an image time series consisting of 72 image volumes with a temporal sampling rate of 17 s. B-spline deformable registration was performed to align image volumes of the time series to a reference volume. The resulting deformation fields were used for motion velocity evaluation and patient-specific motion model construction through principal component analysis (PCA). Geometric uncertainties introduced by slow drifting motion were assessed by Hausdorff distances between unions of organs at risk (OARs) at different motion states and reference OAR contours as well as probabilistic distributions of OARs predicted using the PCA model. Thirteen examinations from 11 patients were included in this study. The averaged motion velocities ranged from 0.8 to 1.9 mm min−1, 0.7 to 1.6 mm min−1, 0.6 to 2.0 mm min−1 and 0.7 to 1.4 mm min−1 for the small bowel, colon, duodenum and stomach respectively; the averaged Hausdorff distances were 5.6 mm, 5.3 mm, 5.1 mm and 4.6 mm. On average, a margin larger than 4.5 mm was needed to cover a space with OAR occupancy probability higher than 55%. Temporal variations of geometric uncertainties were evaluated by comparing across four 5 min sub-scans extracted from the full scan. Standard deviations of Hausdorff distances across sub-scans were less than 1 mm for most examinations, indicating stability of relative margin estimates from separate time windows. These results suggested slow drifting motion of GI organs is significant and geometric uncertainties introduced by such motion should be accounted for during radiotherapy planning and delivery. [ABSTRACT FROM AUTHOR]

Abdominal organs are subject to a variety of physiological forces that superimpose their effects to influence local motion and configuration. These forces not only include breathing, but can also arise from cyclic antral contractions and a range of slow configuration changes. To elucidate each individual motion pattern as well as their combined effects, a hierarchical motion model was built for characterization of these 3 motion modes (characterized as deformation maps between states) using golden angle radial MR signals. Breathing motions are characterized first. Antral contraction states are then reconstructed after breathing motion-induced deformation are corrected; slow configuration change states are further extracted from breathing motion-corrected image reconstructions. The hierarchical model is established based on these multimodal states, which can be either individually shown or combined to demonstrate any arbitrary composited motion patterns. The model was evaluated using 20 MR scans acquired from 9 subjects. Poor reproducibility of breathing motions both within as well as between scan sessions was observed, with an average intra-subject difference of 1.6 cycles min−1 for average breathing frequencies of 12.0 cycles min−1. Antral contraction frequency distributions were more stable than breathing, but also presented poor reproducibility between scans with an average difference of 0.3 cycles min−1 for average frequencies of 3.2 cycles min−1. The magnitudes of motions beyond breathing were found to be significant, with 14.4 and 33.8 mm maximal motions measured from antral contraction and slow configuration changes, respectively. Hierarchical motion models have potential in multiple applications in radiotherapy, including improving the accuracy of dose delivery estimation, providing guidance for margin creation, and supporting advanced decisions and strategies for immobilization, treatment monitoring and gating. [ABSTRACT FROM AUTHOR]

Purpose: Most existing computed tomography (CT)‐ventilation imaging techniques are based on deformable image registration (DIR) of different respiratory phases of a four‐dimensonal CT (4DCT) scan of the lung, followed by the quantification of local breathing‐induced changes in Hounsfield Units (HU) or volume. To date, only moderate correlations have been reported between these CT‐ventilation metrics and standard ventilation imaging modalities for adaptive lung radiation therapy. This study evaluates the use of stress maps derived from biomechanical model‐based DIR as an alternative CT‐ventilation metric. Materials and methods: Six patients treated for lung cancer with conventional radiation therapy were retrospectively analyzed. For each patient, a 4DCT scan and Tc‐99m SPECT‐V image acquired for treatment planning were collected. Biomechanical model‐based DIR was applied between the inhale and exhale phase of the 4DCT scans and stress maps were calculated. The voxel‐wise correlation between the reference SPECT‐V image and map of the maximum principal stress was measured with a Spearman correlation coefficient. The overlap between high (above the 75th percentile) and low (below the 25th percentile) functioning volumes extracted from the reference SPECT‐V and the stress maps was measured with Dice similarity coefficients (DSC). The results were compared to those obtained when using two classical CT‐ventilation metrics: the change in HU and Jacobian determinant. Results: The mean Spearman correlation coefficients were: 0.37 ± 18 and 0.39 ± 13 and 0.59 ± 0.13 considering the changes in HU, Jacobian and maximum principal stress, respectively. The corresponding mean DSC coefficients were 0.38 ± 0.09, 0.37 ± 0.07 and 0.52 ± 0.07 for the high ventilation function volumes and 0.48 ± 0.13, 0.44 ± 0.09 and 0.52 ± 0.07 for the low ventilation function volumes. Conclusion: For presenting a significantly stronger and more consistent correlation with standard SPECT‐V images than previously proposed CT‐ventilation metrics, stress maps derived with the proposed method appear to be a promising tool for incorporation into functional lung avoidance strategies. [ABSTRACT FROM AUTHOR]

Purpose: T1‐weighted dynamic contrast‐enhanced Magnetic Resonance Imaging (DCE‐MRI) is typically quantified by least squares (LS) fitting to a pharmacokinetic (PK) model to yield parameters of microvasculature and perfusion in normal and disease tissues. Such fitting is both time‐consuming as well as subject to inaccuracy and instability in parameter estimates. Here, we propose a novel neural network approach to estimate the PK parameters by extracting long and short time‐dependent features in DCE‐MRI. Methods: A Long Short‐Term Memory (LSTM) network, widely used for processing sequence data, was employed to map DCE‐MRI time‐series accompanied with an arterial input function to parameters of the extended Tofts model. Head and neck DCE‐MRI from 103 patients were used for training and testing the LSTM model. Arterial input functions (AIFs) from 78 patients were used to generate synthetic DCE‐MRI time‐series for training, during which data augmentation was used to overcome the limited size of in vivo data. The model was tested on independent synthesized DCE data using AIFs from 25 patients. The LSTM performance was optimized for the numbers of layers and hidden state features. The performance of the LSTM was tested for different temporal resolution, total acquisition time, and contrast‐to‐noise ratio (CNR), and compared to the conventional LS fitting and a CNN‐based method. Results: Compared to LS fitting, the LSTM model had comparable accuracy in PK parameter estimations from fully temporal‐sampled DCE‐MRI data (~3 s per frame), but much better accuracy for the data with temporally subsampling (4s or greater per frame), total acquisition time truncation by 48%‐16%, or low CNR (5 and 10). The LSTM reduced normalized root mean squared error by 40.4%, 46.9%, and 53.0% for sampling intervals of 4s, 5s, and 6s, respectively, compared to LS fitting. Compared to the CNN model, the LSTM model reduced the error in the parameter estimates up to 55.2%. Also, the LSTM improved the inference time by ~ 14 times on CPU compared to LS fitting. Conclusion: Our study suggests that the LSTM model could achieve improved robustness and computation speed for PK parameter estimation compared to LS fitting and the CNN based network, particularly for suboptimal data. [ABSTRACT FROM AUTHOR]

Magnetic resonance imaging (MRI) is gaining popularity in guiding radiation treatment for intrahepatic cancers due to its superior soft tissue contrast and potential of monitoring individual motion and liver function. This study investigates a deep learning-based method that generates synthetic CT volumes from T1-weighted MR Dixon images in support of MRI-based intrahepatic radiotherapy treatment planning. Training deep neutral networks for this purpose has been challenged by mismatches between CT and MR images due to motion and different organ filling status. This work proposes to resolve such challenge by generating 'semi-synthetic' CT images from rigidly aligned CT and MR image pairs. Contrasts within skeletal elements of the 'semi-synthetic' CT images were determined from CT images, while contrasts of soft tissue and air volumes were determined from voxel-wise intensity classification results on MR images. The resulting 'semi-synthetic' CT images were paired with their corresponding MR images and used to train a simple U-net model without adversarial components. MR and CT scans of 46 patients were investigated and the proposed method was evaluated for 31 patients with clinical radiotherapy plans, using 3-fold cross validation. The averaged mean absolute errors between synthetic CT and CT images across patients were 24.10 HU for liver, 28.62 HU for spleen, 47.05 HU for kidneys, 29.79 HU for spinal cord, 105.68 HU for lungs and 110.09 HU for vertebral bodies. VMAT and IMRT plans were optimized using CT-derived electron densities, and doses were recalculated using corresponding synthetic CT-derived density grids. Resulting dose differences to planning target volumes and various organs at risk were small, with the average difference less than 0.15 Gy for all dose metrics evaluated. The similarities in both image intensity and radiation dose distributions between CT and synthetic CT volumes demonstrate the accuracy of the method and its potential in supporting MRI-only radiotherapy treatment planning. [ABSTRACT FROM AUTHOR]

A novel deep learning architecture was explored to create synthetic CT (MRCT) images that preserve soft tissue contrast necessary for support of patient positioning in Radiation therapy. A U-Net architecture was applied to learn the correspondence between input T1-weighted MRI and spatially aligned corresponding CT images. The network was trained on sagittal images, taking advantage of the left-right symmetry of the brain to increase the amount of training data for similar anatomic positions. The output CT images were divided into three channels, representing Hounsfield Unit (HU) ranges of voxels containing air, soft tissue, and bone, respectively, and simultaneously trained using a combined Mean Absolute Error (MAE) and Mean Squared Error (MSE) loss function equally weighted for each channel. Training on 9192 image pairs yielded resulting synthetic CT images on 13 test patients with MAE of 17.6+/−3.4 HU (range 14–26.5 HU) in soft tissue. Varying the amount of training data demonstrated a general decrease in MAE values with more data, with the lack of a plateau indicating that additional training data could further improve correspondence between MRCT and CT tissue intensities. Treatment plans optimized on MRCT-derived density grids using this network for 7 radiosurgical targets had doses recalculated using the corresponding CT-derived density grids, yielding a systematic mean target dose difference of 2.3% due to the lack of the immobilization mask on the MRCT images, and a standard deviation of 0.1%, indicating the consistency of this correctable difference. Alignment of MRCT and cone beam CT (CBCT) images used for patient positioning demonstrated excellent preservation of dominant soft tissue features, and alignment comparisons of treatment planning CT scans to CBCT images vs. MRCT to CBCT alignment demonstrated differences of −0.1 (σ 0.2) mm, −0.1 (σ 0.3) mm, and −0.2 (σ 0.3) mm about the left-right, anterior-posterior and cranial-caudal axes, respectively. [ABSTRACT FROM AUTHOR]

Purpose : Abdominal dynamic contrast‐enhanced (DCE) MRI suffers from motion‐induced artifacts that can blur images and distort contrast‐agent uptake curves. For liver perfusion analysis, image reconstruction with rigid‐body motion correction (RMC) can restore distorted portal‐venous input functions (PVIF) to higher peak amplitudes. However, RMC cannot correct for liver deformation during breathing. We present a reconstruction algorithm with deformable motion correction (DMC) that enables correction of breathing‐induced deformation in the whole abdomen. Methods: Raw data from a golden‐angle stack‐of‐stars gradient‐echo sequence were collected for 54 DCE‐MRI examinations of 31 patients. For each examination, a respiratory motion signal was extracted from the data and used to reconstruct 21 breathing states from inhale to exhale. The states were aligned with deformable image registration to the end‐exhale state. Resulting deformation fields were used to correct back‐projection images before reconstruction with view sharing. Images with DMC were compared to uncorrected images and images with RMC. Results: DMC significantly increased the PVIF peak amplitude compared to uncorrected images (P << 0.01, mean increase: 8%) but not compared to RMC. The increased PVIF peak amplitude significantly decreased estimated portal‐venous perfusion in the liver (P << 0.01, mean decrease: 8 ml/(100 ml·min)). DMC also removed artifacts in perfusion maps at the liver edge and reduced blurring of liver tumors for some patients. Conclusions: DCE‐MRI reconstruction with DMC can restore motion‐distorted uptake curves in the abdomen and remove motion artifacts from reconstructed images and parameter maps but does not significantly improve perfusion quantification in the liver compared to RMC. [ABSTRACT FROM AUTHOR]

Magnetic resonance (MR) images often provide superior anatomic and functional information over computed tomography (CT) images, but generally are not used alone without CT images for radiotherapy treatment planning and image guidance. This study aims to investigate the potential of probabilistic classification of voxels from multiple MRI contrasts to generate synthetic CT (‘MRCT’) images. The method consists of (1) acquiring multiple MRI volumes: T1-weighted, T2-weighted, two echoes from a ultra-short echo time (UTE) sequence, and calculated fat and water image volumes using a Dixon method, (2) classifying tissues using fuzzy c-means clustering with a spatial constraint, (3) assigning attenuation properties with weights based on the probability of individual tissue classes being present in each voxel, and (4) generating a MRCT image volume from the sum of attenuation properties in each voxel. The capability of each MRI contrast to differentiate tissues of interest was investigated based on a retrospective analysis of ten patients. For one prospective patient, the correlation of skull intensities between CT and MR was investigated, the discriminatory power of MRI in separating air from bone was evaluated, and the generated MRCT image volume was qualitatively evaluated. Our analyses showed that one MRI volume was not sufficient to separate all tissue types, and T2-weighted images was more sensitive to bone density variation compared to other MRI image types. The short echo UTE image showed significant improvement in contrasting air versus bone, but could not completely separate air from bone without false labeling. Generated MRCT and CT images showed similar contrast between bone and soft/solid tissues. These results demonstrate the potential of the presented method to generate synthetic CT images to support the workflow of radiation oncology treatment planning and image guidance. [ABSTRACT FROM AUTHOR]

Purpose: Arteriovenous malformations are often treated with a combination of embolization and stereotactic radiosurgery. Concern has been expressed in the past regarding the dosimetric properties of materials used in embolization and the effects that the introduction of these materials into the brain may have on the quality of the radiosurgery plan. To quantify these effects, the authors have taken large volumes of Onyx 34 and Onyx 18 (ethylene-vinyl alcohol copolymer doped with tantalum) and measured the attenuation and interface effects of these embolization materials. Methods: The manufacturer provided large cured volumes (∼28 cc) of both Onyx materials. These samples were 8.5 cm in diameter with a nominal thickness of 5 mm. The samples were placed on a block tray above a stack of solid water with an Attix chamber at a depth of 5 cm within the stack. The Attix chamber was used to measure the attenuation. These measurements were made for both 6 and 16 MV beams. Placing the sample directly on the solid water stack and varying the thickness of solid water between the sample and the Attix chamber measured the interface effects. The computed tomography (CT) numbers for bulk material were measured in a phantom using a wide bore CT scanner. Results: The transmission through the Onyx materials relative to solid water was approximately 98% and 97% for 16 and 6 MV beams, respectively. The interface effect shows an enhancement of approximately 2% and 1% downstream for 16 and 6 MV beams. CT numbers of approximately 2600-3000 were measured for both materials, which corresponded to an apparent relative electron density (RED) ρew to water of approximately 2.7-2.9 if calculated from the commissioning data of the CT scanner. Conclusions: We performed direct measurements of attenuation and interface effects of Onyx 34 and Onyx 18 embolization materials with large samples. The introduction of embolization materials affects the dose distribution of a MV therapeutic beam, but should be of negligible consequence for effective thicknesses of less than 8 mm. The measured interface effects are also small, particularly at 6 MV. Large areas of high-density artifacts and low-density artifacts can cause errors in dose calculations and need to be identified and resolved during planning. [ABSTRACT FROM AUTHOR]

The purpose of this work is to determine appropriate radiation therapy beam margins to account for intrafraction prostate translations for use with real-time electromagnetic position monitoring and correction strategies. Motion was measured continuously in 35 patients over 1157 fractions at 5 institutions. This data was studied using van Herk's formula of (aS + ?s') for situations ranging from no electromagnetic guidance to automated real-time corrections. Without electromagnetic guidance, margins of over 10mm are necessary to ensure 95% dosimetric coverage while automated electromagnetic guidance allows the margins necessary for intrafraction translations to be reduced to submillimeter levels. Factors such as prostate deformation and rotation, which are not included in this analysis, will become the dominant concerns as margins are reduced. Continuous electromagnetic monitoring and automated correction have the potential to reduce prostate margins to 2-3mm, while ensuring that a higher percentage of patients (99% versus 90%) receive a greater percentage (99% versus 95%) of the prescription dose. [ABSTRACT FROM AUTHOR]

Iterative methods for 3D image reconstruction have the potential to improve image quality over conventional filtered back projection (FBP) in X-ray computed tomography (CT). However, the computation burden of 3D cone-beam forward and back-projectors is one of the greatest challenges facing practical adoption of iterative methods for X-ray CT. Moreover, projector accuracy is also important for iterative methods. This paper describes two new separable footprint (SF) projector methods that approximate the voxel footprint functions as 2D separable functions. Because of the separability of these footprint functions, calculating their integrals over a detector cell is greatly simplified and can be implemented efficiently. The SF-TR projector uses trapezoid functions in the transaxial direction and rectangular functions in the axial direction, whereas the SF-TT projector uses trapezoid functions in both directions. Simulations and experiments showed that both SF projector methods are more accurate than the distance-driven (DD) projector, which is a current state-of-the-art method in the field. The SF-TT projector is more accurate than the SF-TR projector for rays associated with large cone angles. The SF-TR projector has similar computation speed with the DD projector and the SF-TT projector is about two times slower. [ABSTRACT FROM PUBLISHER]

Purpose: Estimating motion and deformation parameters from a series of projection radiographs acquired during arc therapy using a reference CT volume has become a promising technique for targeting treatment. The purpose of this work is to investigate the influence of rotational arc length on maximum achievable accuracy of motion estimation. Methods: The projection-to-volume alignment procedure used a nonrigid model to describe motion in thorax area, a cost function consisting of a least-squared error metric and a simple regularizer that encourages local invertibility, and a four-level multiresolution scheme with a conjugate gradient method to optimize the cost function. The authors tested both small and large scale deformations typically found in the thorax of a radiotherapy patient at different breathing states and limited-angle scans of six angular widths (12°, 18°, 24°, 36°, 60°, and 90°) centered at three angles (0°, 45°, and 90°). Results: The experiments illustrate the potential accuracy of limited-angle projection-to-volume alignment. Registration accuracy can be sensitive to angular center, tends to be lower along direction of the projection set, and tends to decrease away from the rotation center. The studies of small as well as large but realistically scaled deformations show similar dependencies. These trends appear to have fairly low sensitivity to quantum noise effects. Conclusions: There is potentially sufficient information present in a small spread of projections to monitor the configuration of reasonably high contrast tumors without implanted markers. [ABSTRACT FROM AUTHOR]

Physicists have and continue to play a major role in the creation and introduction of novel technology into medical care. This review covers some of the highlights of contributions of medical physicists to the field of radiation oncology during the history of the AAPM. While not comprehensive, the broad scope of developments and their impact hints at the importance of the medical physicist in advancing the field in the past, present, and future. [ABSTRACT FROM AUTHOR]

The looming potential of deformable alignment tools to play an integral role in adaptive radiotherapy suggests a need for objective assessment of these complex algorithms. Previous studies in this area are based on the ability of alignment to reproduce analytically generated deformations applied to sample image data, or use of contours or bifurcations as ground truth for evaluation of alignment accuracy. In this study, a deformable phantom was embedded with 48 small plastic markers, placed in regions varying from high contrast to roughly uniform regional intensity, and small to large regional discontinuities in movement. CT volumes of this phantom were acquired at different deformation states. After manual localization of marker coordinates, images were edited to remove the markers. The resulting image volumes were sent to five collaborating institutions, each of which has developed previously published deformable alignment tools routinely in use. Alignments were done, and applied to the list of reference coordinates at the inhale state. The transformed coordinates were compared to the actual marker locations at exhale. A total of eight alignment techniques were tested from the six institutions. All algorithms performed generally well, as compared to previous publications. Average errors in predicted location ranged from 1.5 to 3.9 mm, depending on technique. No algorithm was uniformly accurate across all regions of the phantom, with maximum errors ranging from 5.1 to 15.4 mm. Larger errors were seen in regions near significant shape changes, as well as areas with uniform contrast but large local motion discontinuity. Although reasonable accuracy was achieved overall, the variation of error in different regions suggests caution in globally accepting the results from deformable alignment. [ABSTRACT FROM AUTHOR]

Current four-dimensional (4D) computed tomography (CT) imaging techniques using multislice CT scanners require retrospective sorting of the reconstructed two-dimensional (2D) CT images. Most existing sorting methods depend on externally monitored breathing signals recorded by extra instruments. External signals may not always accurately capture the breathing status and may lead to severe discontinuity artifacts in the sorted CT volumes. This article describes a method to find the temporal correspondences for the free-breathing multislice CT images acquired at different table positions based on internal anatomy movement. The algorithm iteratively sorts the CT images using estimated internal motion indices. It starts from two imperfect reference volumes obtained from the unsorted CT images; then, in each iteration, thorax motion is estimated from the reference volumes and the free-breathing CT images. Based on the estimated motion, the breathing indices as well as the reference volumes are refined and fed into the next iteration. The algorithm terminates when two successive iterations attain the same sorted reference volumes. In three out of five patient studies, our method attained comparable image quality with that using external breathing signals. For the other two patient studies, where the external signals poorly reflected the internal motion, the proposed method significantly improved the sorted 4D CT volumes, albeit with greater computation time. [ABSTRACT FROM AUTHOR]

Modeling and predicting tumor motion caused by respiration is challenging due to temporal variations in breathing patterns. Treatment approaches such as gating or adaptive bed adjustment/alignment may not require full knowledge of instantaneous position, but might benefit from tracking the general trend of the motion. One simple method for tracking mean tumor position is to apply moving average filters with window sizes corresponding to the breathing periods. Yet respiratory motion is only semiperiodic, so such methods require reliable phase estimation, which is difficult in the presence of noise. This article describes a robust method to track the mean position of respiratory motion without explicitly estimating instantaneous phase. We form a state vector from the respiration signal values at the current instant and at a previous time, and fit an ellipse model to training data. Ellipse eccentricity and orientation potentially capture hysteresis in respiratory motion. Furthermore, we provide two recursive online algorithms for real time mean position tracking: a windowed version with an adaptive window size and another one with temporal discounting. We test the proposed method with simulated breathing traces, as well as with real time-displacement (RPM, Varian) signals. Estimation traces are compared with retrospectively generated moving average results to illustrate the performance of the proposed approach. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the feasibility of a simple deformable phantom as a QA tool for testing and validation of deformable image registration algorithms. A diagnostic thoracic imaging phantom with a deformable foam insert was used in this study. Small plastic markers were distributed through the foam to create a lattice with a measurable deformation as the ground truth data for all comparisons. The foam was compressed in the superior-inferior direction using a one-dimensional drive stage pushing a flat “diaphragm” to create deformations similar to those from inhale and exhale states. Images were acquired at different compressions of the foam and the location of every marker was manually identified on each image volume to establish a known deformation field with a known accuracy. The markers were removed digitally from corresponding images prior to registration. Different image registration algorithms were tested using this method. Repeat measurement of marker positions showed an accuracy of better than 1 mm in identification of the reference marks. Testing the method on several image registration algorithms showed that the system is capable of evaluating errors quantitatively. This phantom is able to quantitatively assess the accuracy of deformable image registration, using a measure of accuracy that is independent of the signals that drive the deformation parameters. [ABSTRACT FROM AUTHOR]

We have shown that high dose conformal radiation combined with chemotherapy appears to prolong the survival of patients with unresectable intrahepatic cancers. The ability to safely deliver higher doses is primarily limited by the development of radiation-induced liver disease, characterized by venous occlusion. In this study, we investigated whether portal venous perfusion measured prior to the end of radiation therapy (RT) together with dose could predict liver venous perfusion dysfunction after treatment. Ten patients with unresectable intrahepatic cancer participated in an IRB-approved computer tomography (CT) perfusion study. Hepatic arterial and portal vein perfusion distributions were estimated by using dynamic contrast enhanced CT and the single compartmental model. Scans were obtained at four time points: prior to treatment, after 15 and 30 fractions of 1.5 Gy treatments, and one month following the completion of RT. Multivariant linear regression was used to determine covariances among the first three time point measurements plus dose for prediction of the post RT measurement. The reduction in the regional venous perfusion one month following RT was predicted by the local accumulated dose and the change in the regional venous perfusion after ∼30 fractions (F=90.6,p<0.000 01). Each Gy produced an approximately 1.2% of reduction in the venous perfusion. This local dose and venous perfusion model has the potential to predict individual sensitivity to radiation. This is the first step toward developing a method to deliver higher and potentially more curative radiation doses to the patients who can safely receive these higher doses. [ABSTRACT FROM AUTHOR]

Respiratory motion remains a significant source of errors in treatment planning for the thorax and upper abdomen. Recently, we proposed a method to estimate two-dimensional (2-D) object motion from a sequence of slowly rotating X-ray projection views, which we called deformation from orbiting views (DOVs). In this method, we model the motion as a time varying deformation of a static prior of the anatomy. We then optimize the parameters of the motion model by maximizing the similarity between the modeled and actual projection views. This paper extends the method to full three-dimensional (3-D) motion and cone-beam projection views. We address several practical issues for using a cone-beam computed tomography (CBCT) scanner that is integrated in a radiotherapy system, such as the effects of Compton scatter and the limited gantry rotation for one breathing cycle. We also present simulation and phantom results to illustrate the performance of this method. [ABSTRACT FROM AUTHOR]

Variations in target volume position between and during treatment fractions can lead to measurable differences in the dose distribution delivered to each patient. Current methods to estimate the ongoing cumulative delivered dose distribution make idealized assumptions about individual patient motion based on average motions observed in a population of patients. In the delivery of intensity modulated radiation therapy (IMRT) with a multi-leaf collimator (MLC), errors are introduced in both the implementation and delivery processes. In addition, target motion and MLC motion can lead to dosimetric errors from interplay effects. All of these effects may be of clinical importance. Here we present a method to compute delivered dose distributions for each treatment beam and fraction, which explicitly incorporates synchronized real-time patient motion data and real-time fluence and machine configuration data. This synchronized dynamic dose reconstruction method properly accounts for the two primary classes of errors that arise from delivering IMRT with an MLC: (a) Interplay errors between target volume motion and MLC motion, and (b) Implementation errors, such as dropped segments, dose over/under shoot, faulty leaf motors, tongue-and-groove effect, rounded leaf ends, and communications delays. These reconstructed dose fractions can then be combined to produce high-quality determinations of the dose distribution actually received to date, from which individualized adaptive treatment strategies can be determined. [ABSTRACT FROM AUTHOR]

The purpose of this study was to investigate the number of intermediate states required to adequately approximate the clinically relevant cumulative dose to deforming/moving thoracic anatomy in four-dimensional (4D) conformal radiotherapy that uses 6 MV photons to target tumors. Four patients were involved in this study. For the first three patients, computed tomography images acquired at exhale and inhale were available; they were registered using B-spline deformation model and the computed transformation was further used to simulate intermediate states between exhale and inhale. For the fourth patient, 4D-acquired, phase-sorted datasets were available and each dataset was registered with the exhale dataset. The exhale-inhale transformation was also used to simulate intermediate states in order to compare the cumulative doses computed using the actual and the simulated datasets. Doses to each state were calculated using the Dose Planning Method (DPM) Monte Carlo code and dose was accumulated for scoring on the exhale anatomy via the transformation matrices for each state and time weighting factors. Cumulative doses were estimated using increasing numbers of intermediate states and compared to simpler scenarios such as a “2-state” model which used only the exhale and inhale datasets or the dose received during the average phase of the breathing cycle. Dose distributions for each modeled state as well as the cumulative doses were assessed using dose volume histograms and several treatment evaluation metrics such as mean lung dose, normal tissue complication probability, and generalized uniform dose. Although significant “point dose” differences can exist between each breathing state, the differences decrease when cumulative doses are considered, and can become less significant yet in terms of evaluation metrics depending upon the clinical end point. This study suggests that for certain “clinical” end points of importance for lung cancer, satisfactory predictions of accumulated total dose to be received by the distorting anatomy can be achieved by calculating the dose to but a few (or even simply the average) phases of the breathing cycle. [ABSTRACT FROM AUTHOR]

We propose a method of using a relatively low degree of freedom (DOF) warping to accurately measure the interval change of lesions having homogeneous contrast. The setting presented here presupposes the use of interval computed tomography (CT) liver exams. After a 3 × 24 DOF warping of the later examination to match the liver's pose in the earlier exam of the interval pair is performed, the lesion's volume change is estimated using the computed difference volume of the two data sets via a novel method that counts partial volume contributions and is insensitive to slight misregistration. A mathematically generated phantom is used to quantify accuracy in the presence of noise. We also quantify the accuracy of our CT liver registrations using microcoils implanted for chemotherapy. A probabilistic liver atlas is used to support automatic masking and liver-focused registration. [ABSTRACT FROM AUTHOR]