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.

In Vivo Microstructure Imaging in Oropharyngeal Squamous Cell Carcinoma Using the Random Walk With Barriers Model.

BACKGROUND: Apparent diffusion coefficient is not specifically sensitive to tumor microstructure and therapy-induced cellular changes. PURPOSE: To investigate time-dependent diffusion imaging with the short-time-limit random walk with barriers model (STL-RWBM) for quantifying microstructure parameters and early cancer cellular response to therapy. STUDY TYPE: Prospective. POPULATION: Twenty-seven patients (median age of 58 years and 7.4% of females) with p16+/p16- oropharyngeal/oral cavity squamous cell carcinomas (OPSCC/OCSCC) underwent MRI scans before therapy, of which 16 patients had second scans at 2 weeks of the 7-weeks chemoradiation therapy (CRT). FIELD STRENGTH/SEQUENCE: 3-T, diffusion sequence with oscillating gradient spine echo (OGSE) and pulse gradient spin echo (PGSE). ASSESSMENT: Diffusion weighted images were acquired using OGSE and PGSE. Effective diffusion times were derived for the STL-RWBM to estimate free diffusion coefficient D0 , volume-to-surface area ratio of cellular membranes V/S, and cell membrane permeability κ. Mean values of these parameters were calculated in tumor volumes. STATISTICAL TESTS: Tumor microstructure parameters were compared with clinical stages of p16+ I-II OPSCC, p16+ III OPSCC, and p16- IV OCSCC by Spearman's rank correlation and with digital pathological analysis of a resected tissue sample. Tumor microstructure parameter responses during CRT in the 16 patients were assessed by paired t-tests. A P-value of <0.05 was considered statistically significant. RESULTS: The derived effective diffusion times affected estimated values of V/S and κ by 40%. The tumor V/S values were significantly correlated with clinical stages (r = 0.47) as an increase from low to high clinical stages. The in vivo estimated cell size agreed with one from pathological analysis of a tissue sample. Early tumor cellular responses showed a significant increase in D0 (14%, P = 0.03) and non-significant increases in κ (56%, P = 0.6) and V/S (10%, P = 0.1). DATA CONCLUSION: Effective diffusion time estimation might impact microstructure parameter estimation. The tumor V/S was correlated with OPSCC/OCSCC clinical stages. LEVEL OF EVIDENCE: 1 TECHNICAL EFFICACY STAGE: 1.

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.

Development of an abdominal dose accumulation tool and assessments of accumulated dose in gastrointestinal organs.

Objective.Online adaptive radiotherapy has demonstrated improved dose conformality in response to inter-fraction geometric variations in the abdomen. The dosimetric impact of intra-fractional variations in anatomic configuration resulting from breathing, gastric contraction and slow configuration motion, however, have been largely ignored, leading to differences between delivered and planned. To investigate the impact of intra-fractional abdominal motions on delivered dose, anatomical deformations due to these three motion modes were extracted from dynamic MRI data using a previously developed hierarchical motion modeling methodology.Approach. Motion magnitudes were extracted from deformation fields between a reference state and all other motion states of the patient. Delivered dose estimates to various gastrointestinal organs (stomach, duodenum, small bowel and colon) were calculated on each motion state of the patient and accumulated to estimate the delivered dose to each organ for the entire treatment fraction.Main results. Across a sample of 10 patients, maximal motions of 33.6, 33.4, 47.6 and 49.2 mm were observed over 20 min for the stomach, duodenum, small bowel and colon respectively. Dose accumulation results showed that motions could lead to average increases of 2.0, 2.1, 1.1, 0.7 Gy to the maximum dose to 0.5cc (D0.5cc) and 3.0, 2.5, 1.3, 0.9 Gy to the maximum dose to 0.1cc (D0.1cc) for these organs at risk. From the 40 dose accumulations performed (10 for each organ at risk), 27 showed increases of modeled delivered dose compared to planned doses, 4 of which exceeded planned dose constraints.Significance. The use of intra-fraction motion measurements to accumulate delivered doses is feasible, and supports retrospective estimation of dose delivery to improve estimates of delivered doses, and further guide strategies for both plan adaptation as well as advances in intra-fraction motion management.

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.

Contrastive self-supervised learning from 100 million medical images with optional supervision.

Purpose: Building accurate and robust artificial intelligence systems for medical image assessment requires the creation of large sets of annotated training examples. However, constructing such datasets is very costly due to the complex nature of annotation tasks, which often require expert knowledge (e.g., a radiologist). To counter this limitation, we propose a method to learn from medical images at scale in a self-supervised way. Approach: Our approach, based on contrastive learning and online feature clustering, leverages training datasets of over 100,000,000 medical images of various modalities, including radiography, computed tomography (CT), magnetic resonance (MR) imaging, and ultrasonography (US). We propose to use the learned features to guide model training in supervised and hybrid self-supervised/supervised regime on various downstream tasks. Results: We highlight a number of advantages of this strategy on challenging image assessment problems in radiography, CT, and MR: (1) significant increase in accuracy compared to the state-of-the-art (e.g., area under the curve boost of 3% to 7% for detection of abnormalities from chest radiography scans and hemorrhage detection on brain CT); (2) acceleration of model convergence during training by up to 85% compared with using no pretraining (e.g., 83% when training a model for detection of brain metastases in MR scans); and (3) increase in robustness to various image augmentations, such as intensity variations, rotations or scaling reflective of data variation seen in the field. Conclusions: The proposed approach enables large gains in accuracy and robustness on challenging image assessment problems. The improvement is significant compared with other state-of-the-art approaches trained on medical or vision images (e.g., ImageNet).

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.

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.

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.

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.

Cardiac Magnetic Resonance Imaging and Blood Biomarkers for Evaluation of Radiation-Induced Cardiotoxicity in Patients With Breast Cancer: Results of a Phase 2 Clinical Trial.

PURPOSE: Radiation therapy (RT) can increase the risk of cardiac events in patients with breast cancer (BC), but biomarkers predicting risk for developing RT-induced cardiac disease are currently lacking. We report results from a prospective clinical trial evaluating early magnetic resonance imaging (MRI) and serum biomarker changes as predictors of cardiac injury and risk of subsequent cardiac events after RT for left-sided disease. METHODS: Women with node-negative and node-positive (N-/+) left-sided BC were enrolled on 2 institutional review board (IRB)-approved protocols at 2 institutions. MRI was conducted pretreatment (within 1 week of starting radiation), at the end of treatment (last day of treatment ±1 week), and 3 months after the last day of treatment (±2 weeks) to quantify left and right ventricular volumes and function, myocardial fibrosis, and edema. Perfusion changes during regadenoson stress perfusion were also assessed on a subset of patients (n = 28). Serum was collected at the same time points. Whole heart and cardiac substructures were contoured using CT and MRI. Models were constructed using baseline cardiac and clinical risk factors. Associations between MRI-measured changes and dose were evaluated. RESULTS: Among 51 women enrolled, mean heart dose ranged from 0.80 to 4.7 Gy and mean left ventricular (LV) dose from 1.1 to 8.2 Gy, with mean heart dose 2.0 Gy. T1 time, a marker of fibrosis, and right ventricular (RV) ejection fraction (EF) significantly changed with treatment; these were not dose dependent. T2 (marker of edema) and LV EF did not significantly change. No risk factors were associated with baseline global perfusion. Prior receipt of doxorubicin was marginally associated with decreased myocardial perfusion after RT (P = .059), and mean MHD was not associated with perfusion changes. A significant correlation between baseline IL-6 and mean heart dose (MHD) at the end of RT (ρ 0.44, P = .007) and a strong trend between troponin I and MHD at 3 months post-treatment (ρ 0.33, P = .07) were observed. No other significant correlations were identified. CONCLUSIONS: In this prospective study of women with left-sided breast cancer treated with contemporary treatment planning, cardiac radiation doses were very low relative to historical doses reported by Darby et al. Although we observed significant changes in T1 and RV EF shortly after RT, these changes were not correlated with whole heart or substructure doses. Serum biomarker analysis of cardiac injury demonstrates an interesting trend between markers and MHD that warrants further investigation.

Volumetric prediction of breathing and slow drifting motion in the abdomen using radial MRI and multi-temporal resolution modeling.

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.

Findings of the AAPM Ad Hoc committee on magnetic resonance imaging in radiation therapy: Unmet needs, opportunities, and recommendations.

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.

Gender Differences in Work-Life Integration Among Medical Physicists.

PURPOSE: To generate an understanding of the primary concerns facing medical physicists regarding integration of a demanding technical career with their personal lives. METHODS AND MATERIALS: In 2019, we recruited 32 medical physics residents, faculty, and staff via emails to US medical physics residency program directors to participate in a 1-hour, semistructured interview that elicited their thoughts on several topics, including work-life integration. Standard techniques of qualitative thematic analysis were used to generate the research findings. RESULTS: Of the participants, 50% were women and 69% were non-Hispanic White individuals, with a mean (SD) age of 37.5 (7.4) years. They were evenly split between residents and faculty or staff. Participant responses centered around 5 primary themes: the gendered distribution of household responsibilities, the effect of career or work on home and family life, the effect of family on career or work, support and strategies for reconciling work-life conflicts, and the role of professional societies in addressing work-life integration. Participants expressed concern about the effect of heavy workloads on home life, with female respondents more likely to report carrying the majority of the household burden. CONCLUSIONS: Medical physicists experience challenges in managing work-life conflict amid a diverse array of personal and professional responsibilities. Further investigations are needed to quantitatively assess the division of work and household labor by gender in medical physics, particularly after the outbreak of the COVID-19 pandemic, but this study's qualitative findings suggest that the profession should consider ways to address root causes of work-life conflict to promote the future success and well-being of all medical physicists, and perhaps women in particular.

Evaluating deep learning methods in detecting and segmenting different sizes of brain metastases on 3D post-contrast T1-weighted images.

Purpose: We investigate the impact of various deep-learning-based methods for detecting and segmenting metastases with different lesion volume sizes on 3D brain MR images. Approach: A 2.5D U-Net and a 3D U-Net were selected. We also evaluated weak learner fusion of the prediction features generated by the 2.5D and the 3D networks. A 3D fully convolutional one-stage (FCOS) detector was selected as a representative of bounding-box regression-based detection methods. A total of 422 3D post-contrast T1-weighted scans from patients with brain metastases were used. Performances were analyzed based on lesion volume, total metastatic volume per patient, and number of lesions per patient. Results: The performance of detection of the 2.5D and 3D U-Net methods had recall of > 0.83 and precision of > 0.44 for lesion volume > 0.3 cm 3 but deteriorated as metastasis size decreased below 0.3 cm 3 to 0.58 to 0.74 in recall and 0.16 to 0.25 in precision. Compared the two U-Nets for detection capability, high precision was achieved by the 2.5D network, but high recall was achieved by the 3D network for all lesion sizes. The weak learner fusion achieved a balanced performance between the 2.5D and 3D U-Nets; particularly, it increased precision to 0.83 for lesion volumes of 0.1 to 0.3 cm 3 but decreased recall to 0.59. The 3D FCOS detector did not outperform the U-Net methods in detecting either the small or large metastases presumably because of the limited data size. Conclusions: Our study provides the performances of four deep learning methods in relationship to lesion size, total metastasis volume, and number of lesions per patient, providing insight into further development of the deep learning networks.

Investigating the SPECT Dose-Function Metrics Associated With Radiation-Induced Lung Toxicity Risk in Patients With Non-small Cell Lung Cancer Undergoing Radiation Therapy.

PURPOSE: Dose to normal lung has commonly been linked with radiation-induced lung toxicity (RILT) risk, but incorporating functional lung metrics in treatment planning may help further optimize dose delivery and reduce RILT incidence. The purpose of this study was to investigate the impact of the dose delivered to functional lung regions by analyzing perfusion (Q), ventilation (V), and combined V/Q single-photon-emission computed tomography (SPECT) dose-function metrics with regard to RILT risk in patients with non-small cell lung cancer (NSCLC) patients who received radiation therapy (RT). METHODS AND MATERIALS: SPECT images acquired from 88 patients with locally advanced NSCLC before undergoing conventionally fractionated RT were retrospectively analyzed. Dose was converted to the nominal dose equivalent per 2 Gy fraction, and SPECT intensities were normalized. Regional lung segments were defined, and the average dose delivered to each lung region was quantified. Three functional categorizations were defined to represent low-, normal-, and high-functioning lungs. The percent of functional lung category receiving ≥20 Gy and mean functional intensity receiving ≥20 Gy (iV20) were calculated. RILT was defined as grade 2+ radiation pneumonitis and/or clinical radiation fibrosis. A logistic regression was used to evaluate the association between dose-function metrics and risk of RILT. RESULTS: By analyzing V/Q normalized intensities and functional distributions across the population, a wide range in functional capability (especially in the ipsilateral lung) was observed in patients with NSCLC before RT. Through multivariable regression models, global lung average dose to the lower lung was found to be significantly associated with RILT, and Q and V iV20 were correlated with RILT when using ipsilateral lung metrics. Through a receiver operating characteristic analysis, combined V/Q low-function receiving ≥20 Gy (low-functioning V/Q20) in the ipsilateral lung was found to be the best predictor (area under the curce: 0.79) of RILT risk. CONCLUSIONS: Irradiation of the inferior lung appears to be a locational sensitivity for RILT risk. The multivariable correlation between ipsilateral lung iV20 and RILT, as well as the association of low-functioning V/Q20 and RILT, suggest that irradiating low-functioning regions in the lung may lead to higher toxicity rates.

Modeling intra-fractional abdominal configuration changes using breathing motion-corrected radial MRI.

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-1and 0.7 to 1.4 mm min-1for 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.

Gastrointestinal 4D MRI with respiratory motion correction.

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.

Mapping lung ventilation through stress maps derived from biomechanical models of the lung.

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.

A Phase 2 Study of Dose-intensified Chemoradiation Using Biologically Based Target Volume Definition in Patients With Newly Diagnosed Glioblastoma.

PURPOSE: We hypothesized that dose-intensified chemoradiation therapy targeting adversely prognostic hypercellular (TVHCV) and hyperperfused (TVCBV) tumor volumes would improve outcomes in patients with glioblastoma. METHODS AND MATERIALS: This single-arm, phase 2 trial enrolled adult patients with newly diagnosed glioblastoma. Patients with a TVHCV/TVCBV >1 cm3, identified using high b-value diffusion-weighted magnetic resonance imaging (MRI) and dynamic contrast-enhanced perfusion MRI, were treated over 30 fractions to 75 Gy to the TVHCV/TVCBV with temozolomide. The primary objective was to estimate improvement in 12-month overall survival (OS) versus historical control. Secondary objectives included evaluating the effect of 3-month TVHCV/TVCBV reduction on OS using Cox proportional-hazard regression and characterizing coverage (95% isodose line) of metabolic tumor volumes identified using correlative 11C-methionine positron emission tomography. Clinically meaningful change was assessed for quality of life by the European Organisation for the Research and Treatment of Cancer Quality of Life Questionnaire C30, for symptom burden by the MD Anderson Symptom Inventory for brain tumor, and for neurocognitive function (NCF) by the Controlled Oral Word Association Test, the Trail Making Test, parts A and B, and the Hopkins Verbal Learning Test-Revised. RESULTS: Between 2016 and 2018, 26 patients were enrolled. Initial patients were boosted to TVHCV alone, and 13 patients were boosted to both TVHCV/TVCBV. Gross or subtotal resection was performed in 87% of patients; 22% were O6-methylguanine-DNA methyltransferase (MGMT) methylated. With 26-month follow-up (95% CI, 19-not reached), the 12-month OS rate among patients boosted to the combined TVHCV/TVCBV was 92% (95% CI, 78%-100%; P = .03) and the median OS was 20 months (95% CI, 18-not reached); the median OS for the whole study cohort was 20 months (95% CI, 14-29 months). Patients whose 3-month TVHCV/TVCBV decreased to less than the median volume (3 cm3) had superior OS (29 vs 12 months; P = .02). Only 5 patients had central or in-field failures, and 93% (interquartile range, 59%-100%) of the 11C-methionine metabolic tumor volumes received high-dose coverage. Late grade 3 neurologic toxicity occurred in 2 patients. Among non-progressing patients, 1-month and 7-month deterioration in quality of life, symptoms, and NCF were similar in incidence to standard therapy. CONCLUSIONS: Dose intensification against hypercellular/hyperperfused tumor regions in glioblastoma yields promising OS with favorable outcomes for NCF, symptom burden, and quality of life, particularly among patients with greater tumor reduction 3 months after radiation therapy.