The future of MRI in radiation therapy: Challenges and opportunities for the MR community.

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.

A qualitative investigation of resilience and well-being among medical physics residents.

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.

Clinical implementation of MR-guided vaginal cylinder brachytherapy.

We present an institutional experience on the clinical implementation of magnetic resonance (MR)-guided vaginal brachytherapy using commercially available solid applicator models. To test the fidelity of solid applicator models to digitize vaginal cylinder applicators, three datasets were evaluated. The first included 15 patients who were simulated with CT alone. Next, a water phantom was used to evaluate vaginal cylinders ranging from 20 to 35 mm in diameter with CT and MR. Finally, three patients undergoing HDR brachytherapy with vaginal cylinders that were simulated with both CT and MR were evaluated. In these assessments, the solid applicator models were aligned based on the outline of the applicators on the corresponding volumetric image, and deviations between the central source positions defined based on X-ray markers (on CT) and solid applicator models (on CT and MR), and the percent dose difference between select reference points were calculated. The mean central source deviation defined based on X-ray markers (on CT) and solid applicator models (on CT and MR) for the 15-patient cohort, the phantom, and the 3-patient cohort is 0.6 mm, 0.6 mm, and 1.2 mm, respectively. The average absolute percent dose difference for the bladder, rectum, prescription, and inferior reference points were 2.2%, 2.3%, 2.2%, and 2.4%, respectively, for the 15 patient cohort. For the phantom study, the average, absolute percent dose difference for the prescription and inferior reference points are 2.0% and 2.1% for the CT, 2.3% and 2.2% for the T1W, and 2.8% and 3.0% for the T2W images. For the three patient cohort, the average absolute percent dose difference for the bladder, rectum, prescription, and inferior reference points are 2.9%, 2.6%, 3.0%, and 4.2% for the CT, 6.5%, 1.6%, 2.5%, and 4.7% for the T1W, and 6.0%, 7.4%, 2.6, and 2.0% for the T2W images. Based on the current study, aligning the applicator model to MR images provides a practical, efficient approach to perform MR-based brachytherapy planning.

Volumetric MRI with sparse sampling for MR-guided 3D motion tracking via sparse prior-augmented implicit neural representation learning.

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.

Extended nnU-Net for Brain Metastasis Detection and Segmentation in Contrast-Enhanced Magnetic Resonance Imaging With a Large Multi-Institutional Data Set.

PURPOSE: The purpose of this study was to investigate an extended self-adapting nnU-Net framework for detecting and segmenting brain metastases (BM) on magnetic resonance imaging (MRI). METHODS AND MATERIALS: Six different nnU-Net systems with adaptive data sampling, adaptive Dice loss, or different patch/batch sizes were trained and tested for detecting and segmenting intraparenchymal BM with a size ≥2 mm on 3 Dimensional (3D) post-Gd T1-weighted MRI volumes using 2092 patients from 7 institutions (1712, 195, and 185 patients for training, validation, and testing, respectively). Gross tumor volumes of BM delineated by physicians for stereotactic radiosurgery were collected retrospectively and curated at each institute. Additional centralized data curation was carried out to create gross tumor volumes of uncontoured BM by 2 radiologists to improve the accuracy of ground truth. The training data set was augmented with synthetic BMs of 1025 MRI volumes using a 3D generative pipeline. BM detection was evaluated by lesion-level sensitivity and false-positive (FP) rate. BM segmentation was assessed by lesion-level Dice similarity coefficient, 95-percentile Hausdorff distance, and average Hausdorff distance (HD). The performances were assessed across different BM sizes. Additional testing was performed using a second data set of 206 patients. RESULTS: Of the 6 nnU-Net systems, the nnU-Net with adaptive Dice loss achieved the best detection and segmentation performance on the first testing data set. At an FP rate of 0.65 ± 1.17, overall sensitivity was 0.904 for all sizes of BM, 0.966 for BM ≥0.1 cm3, and 0.824 for BM <0.1 cm3. Mean values of Dice similarity coefficient, 95-percentile Hausdorff distance, and average HD of all detected BMs were 0.758, 1.45, and 0.23 mm, respectively. Performances on the second testing data set achieved a sensitivity of 0.907 at an FP rate of 0.57 ± 0.85 for all BM sizes, and an average HD of 0.33 mm for all detected BM. CONCLUSIONS: Our proposed extension of the self-configuring nnU-Net framework substantially improved small BM detection sensitivity while maintaining a controlled FP rate. Clinical utility of the extended nnU-Net model for assisting early BM detection and stereotactic radiosurgery planning will be investigated.

Gender-Based Discrimination and Sexual Harassment in Medical Physics.

PURPOSE: Gender-based discrimination and sexual harassment have been well-studied in the fields of science, technology, engineering, math, and medicine. However, less is known about these topics and their effect within the profession of medical physics. We aimed to better understand and clarify the views and experiences of practicing medical physicists and medical physics residents regarding gender-based discrimination and sexual harassment. METHODS AND MATERIALS: We conducted in-depth, semistructured, and confidential interviews with 32 practicing medical physicists and medical physics residents across the United States. The interviews were broad and covered the topics of discrimination, mentorship, and work/life integration. All participants were associated with a department with a residency program accredited by the Commission on Accreditation of Medical Physics Education Programs and had appointments with a clinical component. RESULTS: Participants shared views about gender-based discrimination and sexual harassment that were polarized. Some perceived that discrimination and harassment were a current concern within medical physics, while some either perceived that they were not a concern or that discrimination positively affected women and minoritized populations. Many participants shared personal experiences of discrimination and harassment, including those related to unequal compensation, discrimination against mothers, discrimination during the hiring process, gender-biased assumptions about behaviors or goals, communication biases, and overt and persistent sexual harassment. CONCLUSIONS: There is an urgent need to acknowledge, better understand, and address gender-based discrimination and sexual harassment in the field of medical physics.

Does Vascular Collapse Occur After Treatment of Hepatocellular Cancer With Stereotactic Body Radiation Therapy?

There is debate about why stereotactic body radiation therapy (SBRT) produces superior control of hepatocellular cancer (HCC) compared to fractionated treatment. Both preclinical and clinical evidence has been presented to support a "classic" biological explanation: the greater BED of SBRT produces more DNA damage and tumor cell kill. More recently, preclinical evidence has supported the concept of a "new biology", particularly radiation-induced vascular collapse, which increases hypoxia and free radical activation. This is hypothesized to cause much greater tumor cell death than was produced by the initial radiation-induced DNA damage to the tumor. We decided to investigate if vascular collapse occurs after standard SBRT for patients with HCC. Eight patients with 10 lesions underwent dynamic contrast enhanced MRI at the time of simulation and either 48 or 96 hours after the first fraction. Only three of 10 tumors showed a decrease in blood flow. These findings suggest that vascular collapse does not typically occur after SBRT for HCC.

Dosimetric measurements of Onyx embolization material for stereotactic radiosurgery.

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) ρ(e) (w) 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.

Real time volumetric MRI for 3D motion tracking via geometry-informed deep learning.

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.

Real-time prediction of stomach motions based upon gastric contraction and breathing models.

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. Mainresults. 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.