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ADR processes are believed to offer numerous advantages and greater opportunities to women unlike the adversarial system. They are thus regarded as a woman-friendly process. This chapter assesses the limitations and implications of practical, legal factors, and dilemmas affecting ADR mechanisms in focus (counselling and mediation) when used for resolving matrimonial disputes. It evaluates the effect of various attitudinal and prejudicial biases, power imbalance, and neutrality aspect on women and their interplay with ADR and gender justice dimension. Applying the feminist perspective, the chapter questions attitudes that pose challenges and reinforce the position of powerlessness in women, especially while accessing ADR if these attitudes make it difficult for women to realize their rights. A review of these gaps will help in knowing if the final settlements of ADR are voluntarily accepted or if asymmetrical power relations make it difficult for a woman to negotiate well or negotiation her stakes and endure injustice.

This chapter is an empirical evaluation of the practice of ADR, latent gender bias, attitudes of ADR practitioners and gender justice concern for women facing matrimonial litigation. Interviews of women, counsellors, and mediators and case studies conducted over a while highlight the major findings that are persuasive to the theme of this book. The chapter presents the study of the ADR processes from the women litigant’s perspectives with a focus on the gender inconsiderate realm of the law. It presents additional insight into the perception of ADR practitioners regarding women issues and highlights the flaws that impinge upon the effectiveness of the ADR process. Finally, the discussion concludes the findings of the factors that jeopardize fairness and thus need to be recognized and concentrated upon for the protection and promotion of gender equality.

This chapter presents an evaluation and effect of various factors that impact women’s access to justice. It analyses societal biases, patriarchal approaches, social structure, and gender stereotypes against women in Indian society. It analyses how these biases act as a barrier by having a huge impact on the psychology of the women seeking justice from Courts and the ADR process and on the people responsible for justice dispensation. It evaluates the impact of socialization and role assignment that further reinforces cruel practices against women and leads to their subordination by the legal apparatus. Besides, the chapter presents a critique of the concept of gender justice and how women are made to suffer violence, discrimination, and oppression in every walk of their life. It emphasizes the negative impact of financial dependence, legal impediments, and structural constraints that beats the justice needs of women. Women’s self-reluctance to take action against these wrongs strikes their numerous human rights and search for justice.

The second chapter introduces the need to evaluate the ADR process in the context of matrimonial disputes. Marriage in India is a social institution premised on the belief of it being unending, eternal, holy, and sacred. However, contemporary challenges have strained them, with visible transitions modern marriages more becoming complex and demanding. The chapter examines the several reasons behind a steep increase in the number of matrimonial litigation filed in the Courts. It explores the current status of the Indian judicial system that is suffering from the phenomena of delays, overwhelming arrears and inaccessibility of Courts. It elaborates on the advent and utility of legal provisions providing for various ADR mechanisms. It summarizes the potential of ADR processes for resolving the marital disputes and the benefits it offers to battered women.

This chapter focuses on the various matrimonial laws and remedies concerning the resolution of the matrimonial dispute in India. It analyses the impact and implications of divorce on society, children, and women. It highlights the substantive issues faced by women and the ramifying effect of legal proceedings on them. Termination of matrimonial ties can be distressing and may make women the worst sufferers due to its social, psychological, emotional, and economic repercussions. The chapter validates the fact that the dispute resolution process must ensure healthy resolution of the matrimonial dispute with sensitivity about the vulnerability of the battered women. Further, the chapter gives a comprehensive exposition of the concept of three ADR processes, Viz. counselling, mediation, and reconciliation specifically employed to resolve a marital dispute.

This chapter effectively concludes the book on a note that ADR is undoubtedly an efficacious mode of dispute resolution, but in India, it is still in its infancy. It has limited weaknesses similar to the formal justice delivery system which are hampering in reaping the full benefits of these processes. ADR is not a panacea but a remedy that is worth considering. It is here to stay but existing challenges need to be dealt with to completely realize their potential. Besides, in India, there is an almost negligent discussion about the impact of ADR for battered women who face power imbalance, gender bias and insensitivity and paternalism, etc. These factors remain a greater challenge to gender justice. Thus, the chapter seeks to bring insight into various strategies that can be employed for resolving matrimonial disputes in a way that furthers gender justice through ADR. It proposes pertinent suggestions/recommendations in terms of institutional, policy, and legal framework for improving the efficiency of ADR mechanisms.

The use of dedicated magnetic resonance simulation (MR-SIM) platforms in Radiation Oncology has expanded rapidly, introducing new equipment and functionality with the overall goal of improving the accuracy of radiation treatment planning. However, this emerging technology presents a new set of challenges that need to be addressed for safe and effective MR-SIM implementation. The major objectives of this report are to provide recommendations for commercially available MR simulators, including initial equipment selection, siting, acceptance testing, quality assurance, optimization of dedicated radiation therapy specific MR-SIM workflows, patient-specific considerations, safety, and staffing. Major contributions include guidance on motion and distortion management as well as MRI coil configurations to accommodate patients immobilized in the treatment position. Examples of optimized protocols and checklists for QA programs are provided. While the recommendations provided here are minimum requirements, emerging areas and unmet needs are also highlighted for future development.

To develop and evaluate a deep learning method to segment parotid glands from MRI using unannotated MRI and unpaired expert-segmented CT datasets. We introduced a new self-derived organ attention deep learning network for combined CT to MRI image-to-image translation (I2I) and MRI segmentation, all trained as an end-to-end network. The expert segmentations available on CT scans were combined with the I2I translated pseudo MR images to train the MRI segmentation network. Once trained, the MRI segmentation network alone is required. We introduced an organ attention discriminator that constrains the CT to MR generator to synthesize pseudo MR images that preserve organ geometry and appearance statistics as in real MRI. The I2I translation network training was regularized using the organ attention discriminator, global image-matching discriminator, and cycle consistency losses. MRI segmentation training was regularized by using cross-entropy loss. Segmentation performance was compared against multiple domain adaptation-based segmentation methods using the Dice similarity coefficient (DSC) and Hausdorff distance at the 95th percentile (HD95). All networks were trained using 85 unlabeled T2-weighted fat suppressed (T2wFS) MRIs and 96 expert-segmented CT scans. Performance upper-limit was based on fully supervised MRI training done using the 85 T2wFS MRI with expert segmentations. Independent evaluation was performed on 77 MRIs never used in training. The proposed approach achieved the highest accuracy (left parotid: DSC 0.82 ± 0.03, HD95 2.98 ± 1.01 mm; right parotid: 0.81 ± 0.05, HD95 3.14 ± 1.17 mm) compared to other methods. This accuracy was close to the reference fully supervised MRI segmentation (DSC of 0.84 ± 0.04, a HD95 of 2.24 ± 0.77 mm for the left parotid, and a DSC of 0.84 ± 0.06 and HD95 of 2.32 ± 1.37 mm for the right parotid glands).

We developed a new joint probabilistic segmentation and image distribution matching generative adversarial network (PSIGAN) for unsupervised domain adaptation (UDA) and multi-organ segmentation from magnetic resonance (MRI) images. Our UDA approach models the co-dependency between images and their segmentation as a joint probability distribution using a new structure discriminator. The structure discriminator computes structure of interest focused adversarial loss by combining the generated pseudo MRI with probabilistic segmentations produced by a simultaneously trained segmentation sub-network. The segmentation sub-network is trained using the pseudo MRI produced by the generator sub-network. This leads to a cyclical optimization of both the generator and segmentation sub-networks that are jointly trained as part of an end-to-end network. Extensive experiments and comparisons against multiple state-of-the-art methods were done on four different MRI sequences totalling 257 scans for generating multi-organ and tumor segmentation. The experiments included, (a) 20 T1-weighted (T1w) in-phase mdixon and (b) 20 T2-weighted (T2w) abdominal MRI for segmenting liver, spleen, left and right kidneys, (c) 162 T2-weighted fat suppressed head and neck MRI (T2wFS) for parotid gland segmentation, and (d) 75 T2w MRI for lung tumor segmentation. Our method achieved an overall average DSC of 0.87 on T1w and 0.90 on T2w for the abdominal organs, 0.82 on T2wFS for the parotid glands, and 0.77 on T2w MRI for lung tumors., Comment: This paper has been accepted by IEEE Transactions on Medical Imaging

Purpose Acute esophagitis (AE) is a common dose-limiting toxicity in radiation therapy of locally advanced non-small cell lung cancer (LA-NSCLC). We developed an early AE prediction model from weekly accumulated esophagus dose and its associated local volumetric change. Methods and Materials Fifty-one patients with LA-NSCLC underwent treatment with intensity modulated radiation therapy to 60 Gy in 2-Gy fractions with concurrent chemotherapy and weekly cone beam computed tomography (CBCT). Twenty-eight patients (55%) developed grade ≥2 AE (≥AE2) at a median of 4 weeks after the start of radiation therapy. For early ≥AE2 prediction, the esophagus on CBCT of the first 2 weeks was deformably registered to the planning computed tomography images, and weekly esophagus dose was accumulated. Week 1–to–week 2 (w1→w2) esophagus volume changes including maximum esophagus expansion (MEex%) and volumes with ≥x% local expansions (VEx%; x = 5, 10, 15) were calculated from the Jacobian map of deformation vector field gradients. Logistic regression model with 5-fold cross-validation was built using combinations of the accumulated mean esophagus doses (MED) and the esophagus change parameters with the lowest P value in univariate analysis. The model was validated on an additional 18 and 11 patients with weekly CBCT and magnetic resonance imaging (MRI), respectively, and compared with models using only planned mean dose (MEDPlan). Performance was assessed using area under the curve (AUC) and Hosmer-Lemeshow test (PHL). Results Univariately, w1→w2 VE10% (P = .004), VE5% (P = .01) and MEex% (P = .02) significantly predicted ≥AE2. A model combining MEDW2 and w1→w2 VE10% had the best performance (AUC = 0.80; PHL = 0.43), whereas the MEDPlan model had a lower accuracy (AUC = 0.67; PHL = 0.26). The combined model also showed high accuracy in the CBCT (AUC = 0.78) and MRI validations (AUC = 0.75). Conclusions A CBCT-based, cross-validated, and internally validated model on MRI with a combination of accumulated esophagus dose and local volume change from the first 2 weeks of chemotherapy significantly improved AE prediction compared with conventional models using only the planned dose. This model could inform plan adaptation early to lower the risk of esophagitis.

Background and purpose: Magnetic resonance (MR) only radiation therapy for prostate treatment provides superior contrast for defining targets and organs-at-risk (OARs). This study aims to develop a deep learning model to leverage this advantage to automate the contouring process. Materials and methods: Six structures (bladder, rectum, urethra, penile bulb, rectal spacer, prostate and seminal vesicles) were contoured and reviewed by a radiation oncologist on axial T2-weighted MR image sets from 50 patients, which constituted expert delineations. The data was split into a 40/10 training and validation set to train a two-dimensional fully convolutional neural network, DeepLabV3+, using transfer learning. The T2-weighted image sets were pre-processed to 2D false color images to leverage pre-trained (from natural images) convolutional layers’ weights. Independent testing was performed on an additional 50 patient’s MR scans. Performance comparison was done against a U-Net deep learning method. Algorithms were evaluated using volumetric Dice similarity coefficient (VDSC) and surface Dice similarity coefficient (SDSC). Results: When comparing VDSC, DeepLabV3+ significantly outperformed U-Net for all structures except urethra (P

Purpose: To evaluate proton dose calculation accuracy of optimized pencil beam scanning (PBS) plans on MR-derived synthetic-CTs for prostate patients. Material and Methods: Ten patient datasets with both a CT and an MRI were planned with opposed lateral proton beams optimized to single field uniform dose under an IRB-approved study. The proton plans were created on CT datasets generated by a commercial synthetic CT-based software called MRCAT (MR for Calculating ATtenuation) routinely used in our clinic for photon-based MR-only planning. A standard prescription of 79.2 Gy (RBE) and 68.4 Gy (RBE) was used for intact prostate and prostate bed cases, respectively. Proton plans were first generated and optimized using the MRCAT synthetic-CT (syn-CT), and then recalculated on the planning CT rigidly aligned with the syn-CT (aligned-CT) and a deformed planning CT (deformed-CT), which was deformed to match outer contour between syn-CT and aligned-CT. The same beam arrangement, total MUs, MUs/spot, spot positions were used to recalculate dose on deformed-CT and aligned-CT without renormalization. DVH analysis was performed on aligned-CT, deformed-CT, and syn-CT to compare D98%, V100%, V95% for PTV, PTVeval, and GTV as well as V70Gy, V50Gy for OARs. Results: The relative percentage dose difference between syn-CT and deformed-CT, were (0.17 ± 0.33 %) for PTVeval D98% and (0.07 ± 0.1 %) for CTV D98%. Rectum V70Gy, V50Gy, and Bladder V70Gy were (2.76 ± 4.01 %), (11.6 ± 11.2 %), and (3.41 ± 2.86 %), respectively for the syn-CT, and (3.23 ± 3.63 %), (11.3 ± 8.18 %), and (3.29 ± 2.76 %), respectively for the deformed-CT, and (1.37 ± 1.84 %), (8.48 ± 6.67 %), and (4.91 ± 3.65 %), respectively for aligned-CT. Conclusion: Dosimetric analysis shows that MR-only proton planning is feasible using syn-CT based on current clinical margins that account for a range uncertainty.

Purpose To investigate the potential of an atlas‐based approach in generation of synthetic CT for pelvis anatomy. Methods Twenty‐three matched pairs of computed tomography (CT) and magnetic resonance imaging (MRI) scans were selected from a pool of prostate cancer patients. All MR scans were preprocessed to reduce scanner‐ and patient‐induced intensity inhomogeneities and to standardize their intensity histograms. Ten (training dataset) of 23 pairs were then utilized to construct the coregistered CT‐MR atlas. The synthetic CT for a new patient is generated by appropriately weighting the deformed atlas of CT‐MR onto the new patient MRI. The training dataset was used as an atlas to generate the synthetic CT for the rest of the patients (test dataset). The mean absolute error (MAE) between the deformed planning CT and synthetic CT was computed over the entire CT image, bone, fat, and muscle tissues. The original treatment plans were also recomputed on the new synthetic CTs and dose–volume histogram metrics were compared. The results were compared with a commercially available synthetic CT Software (MRCAT) that is routinely used in our clinic. Results MAE errors (±SD) between the deformed planning CT and our proposed synthetic CTs in the test dataset were 47 ± 5, 116 ± 12, 36 ± 6, and 47 ± 5 HU for the entire image, bone, fat, and muscle tissues respectively. The MAEs were 65 ± 5, 172 ± 9, 43 ± 7, and 42 ± 4 HU for the corresponding tissues in MRCAT CT. The dosimetric comparison showed consistent results for all plans using our synthetic CT, deformed planning CT and MRCAT CT. Conclusion We investigated the potential of a multiatlas approach to generate synthetic CT images for the pelvis. Our results demonstrate excellent results in terms of HU value assignment compared to the original CT and dosimetric consistency.

Purpose To compare single-shot echo-planar (SS-EPI)-based and turbo spin-echo (SS-TSE)-based diffusion-weighted imaging (DWI) in Non-Small Cell Lung Cancer (NSCLC) patients and to characterize the distributions of apparent diffusion coefficient (ADC) values generated by the two techniques. Methods Ten NSCLC patients were enrolled in a prospective IRB-approved study to compare and optimize DWI using EPI and TSE-based techniques for radiotherapy planning. The imaging protocol included axial T2w, EPI-based DWI and TSE-based DWI on a 3 T Philips scanner. Both EPI-based and TSE-based DWI sequences used three b values (0, 400, and 800 s/mm2 ). The acquisition times for EPI-based and TSE-based DWI were 5 and 8 min, respectively. DW-MR images were manually coregistered with axial T2w images, and tumor volume contoured on T2w images were mapped onto the DWI scans. A pixel-by-pixel fit of tumor ADC was calculated based on monoexponential signal behavior. Tumor ADC mean, standard deviation, kurtosis, and skewness were calculated and compared between EPI and TSE-based DWI. Image distortion and ADC values between the two techniques were also quantified using fieldmap analysis and a NIST traceable ice-water diffusion phantom, respectively. Results The mean ADC for EPI and TSE-based DWI were 1.282 ± 0.42 × 10-3 and 1.211 ± 0.31 × 10-3 mm2 /s. The average skewness and kurtosis were 0.14 ± 0.4 and 2.43 ± 0.40 for DWI-EPI and -0.06 ± 0.69 and 2.89 ± 0.62 for DWI-TSE. Fieldmap analysis showed a mean distortion of 13.72 ± 8.12 mm for GTV for DWI-EPI and 0.61 ± 0.4 mm for DWI-TSE. ADC values obtained using the diffusion phantom for the two techniques were within 0.03 × 10-3 mm2 /s with respect to each other as well as the established values. Conclusions Diffusion-weighted turbo spin-echo shows better geometrical accuracy compared to DWI-EPI. Mean ADC values were similar with both acquisitions but the shape of the histograms was different based on the skewness and kurtosis values. The impact of differences in respiratory technique on ADC values requires further investigation.

The goal of this study was to develop and evaluate a volumetric three-dimensional (3D) approach to improve the accuracy of ablation margin assessment following thermal ablation of hepatic tumors. The 3D margin assessment technique was developed to generate the new 3D assessment metrics: volumes of insufficient coverage (VICs) measuring volume of tissue at risk post-ablation. VICs were computed for the tumor and tumor plus theoretical 5- and 10-mm margins. The diagnostic accuracy of the 3D assessment to predict 2-year local tumor progression (LTP) was compared to that of manual 2D assessment using retrospective analysis of a patient cohort that has previously been reported as a part of an outcome-centered study. Eighty-six consecutive patients with 108 colorectal cancer liver metastases treated with radiofrequency ablation (2002–2012) were used for evaluation. The 2-year LTP discrimination power was assessed using receiver operating characteristic area under the curve (AUC) analysis. A 3D assessment of margins was successfully completed for 93 out of 108 tumors. The minimum margin size measured using the 3D method had higher discrimination power compared with the 2D method, with an AUC value of 0.893 vs. 0.790 (p = 0.01). The new 5-mm VIC metric had the highest 2-year LTP discrimination power with an AUC value of 0.923 (p = 0.004). Volumetric semi-automated 3D assessment of the ablation zone in the liver is feasible and can improve accuracy of 2-year LTP prediction following thermal ablation of hepatic tumors. • More accurate prediction of local tumor progression risk using volumetric 3D ablation zone assessment can help improve the efficacy of image-guided percutaneous thermal ablation of hepatic tumors. • The accuracy of evaluation of ablation zone margins after thermal ablation of colorectal liver metastases can be improved using a volumetric 3D semi-automated assessment approach and the volume of insufficient coverage assessment metric. • The new 5-mm volume-of-insufficient-coverage metric, indicating the volume of tumor plus 5-mm margin that remained untreated, had the highest 2-year local tumor progression discrimination power.

Purpose To correlate semiquantitative parameters derived from dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) and 18F-fluorodeoxyglucose positron emission tomography (18F-FDG-PET) for non-small cell lung cancer (NSCLC). Methods and Materials Twenty-four NSCLC patients who underwent pretreatment 18F-FDG-PET and DCE-MRI were analyzed. The maximum standardized uptake value (SUVmax) was measured from 18F-FDG-PET. Dynamic contrast-enhanced MRI was obtained on a 3T MRI scanner using 4-dimensional T1-weighted high-resolution imaging with a volume excitation sequence. The DCE-MRI parameters, consisting of mean, median, standard deviation (SD), and median absolute deviation (MAD) of peak enhancement, time to peak (TTP), time to half peak (TTHP), wash-in slope (WIS), wash-out slope (WOS), initial gradient, wash-out gradient, signal enhancement ratio, and initial area under the relative signal enhancement curve taken up to 30, 60, 90, 120, 150, and 180 seconds, TTP, and TTHP (IAUCtthp), were calculated for each lesion. Univariate analysis (UVA) was performed using Spearman correlation. A linear regression model to predict SUVmax from DCE-MRI parameters was developed by multivariate analysis (MVA) using least absolute shrinkage selection operator in combination with leave-one-out cross-validation (LOOCV). Results In UVA, mean(WOS) (ρ = −0.456, P = .025), mean(IAUCtthp) (ρ = −0.439, P = .032), median(IAUCtthp) (ρ = −0.543, P = .006), and MAD(IAUCtthp) (ρ = −0.557, P = .005) were statistically significant; all these parameters were negatively correlated with SUVmax. In MVA, a linear combination of SD(WIS), SD(TTP), MAD(TTHP), and MAD(IAUCtthp) was statistically significant for predicting SUVmax (LOOCV-based adjusted R2 = 0.298, P = .0006). A decrease in SD(WIS), MAD(TTHP), and MAD(IAUCtthp) and an increase in SD(TTP) were associated with a significant increase in SUVmax. Conclusion An association was found between SUVmax, the SD, and MAD of DCE-MRI metrics derived during contrast uptake in NSCLC, reflecting that intratumoral heterogeneity in wash-in contrast kinetics is associated with tumor metabolism. Although MAD(IAUCtthp) was a significant feature in both UVA and MVA, the LASSO-based multivariate regression model yielded better predictability of SUVmax than a univariate regression model using MAD(IAUCtthp). This study will facilitate understanding of the complex relationship between tumor vascularization and metabolism and eventually help in guiding targeted therapy.

PURPOSE/OBJECTIVE(S) To report on our early experience of treating patients with pancreas ductal adenocarcinoma (PDAC) with ablative SBRT (50 Gy in 5 fractions) on the Elekta Unity MR-Linac. MATERIALS/METHODS Ten PDAC patients were treated with abdominal compression (AC) and daily online plan adaptation using Adapt-to-Shape (ATS) workflow. AC is the method of choice to reduce breathing motion since automatic breath-hold/gating or other motion management options are not yet available on Unity MR-Linac. Three tumors were located in the head of the pancreas and seven were in the body. Seven patients were considered inoperable per surgeon and six were node positive. Average GTV volume during simulation was 42.5 ± 24.8 cc. Three orthogonal plane cine MRI were acquired to assess AC belt pressure during MR simulation as well as during each treatment fraction to assess stability of AC in minimizing tumor motion. Three sets of 3D T2w MR scans, pre-treatment (MRIpre), verification (MRIver) and post-treatment (MRIpost) MRI, were acquired for online planning. To assess the dosimetric impact of intrafraction organ motion, a post-treatment quality assurance (QA) was performed before the next fraction by propagating pre-treatment plan and structures to both MRIver and MRIpost, editing the contours and recalculating dose. GTV coverage and selected organs-at-risk (OAR) dose constraints (e.g., < 33 Gy to 0.035 cc, < 25 Gy to 2 cc and < 25 Gy to 5cc volume) were evaluated on MRIver and MRIpost. RESULTS Median total treatment time was 75.5(49-132) minutes. Average contouring, planning, physics QA and beam-on time was 25.1 ± 11.9, 12.2 ± 6.0, 1.9 ± 1.5 and 14.1 ± 3.7 mins respectively. Average AP, LR and SI motion in FB and with compression was 0.3 ± 0.1, 0.6 ± 0.2 and 0.7 ± 0.2 and 0.2 ± 0.1, 0.2 ± 0.1, 0.4 ± 0.1 cm respectively, indicating that compression belt was effective in minimizing patient breathing motion. Average tumor motion in AC belt for all fractions was 0.2 ± 0.1, 0.2 ± 0.1 and 0.4 ± 0.2 cm in AP, LR and SI direction. Median GTV coverage was 78.7% of Rx dose for all fractions. Average GTV Dmax and GTV mean dose for all the fractions was 57.4 ± 0.7 and 51.2 ± 1.6 Gy respectively. Average 0.035 cc, D2cc and D5cc stomach dose was 34.4 ± 5.4, 26.7 ± 2.9 and 24.3 ± 2.1 Gy on MRIver and 35.3 ± 6.2, 27.0 ± 3.3 and 24.6 ± 2.5 Gy on MRIpost. Average 0.035 cc, D2cc and D5cc small bowel dose was 35.1 ± 6.3, 27.1 ± 3.6 and 24.4 ± 2.6 Gy on MRIver and 35.5 ± 6.5, 27.3 ± 3.9 and 24.6 ± 2.5 Gy on MRIpost. CONCLUSION MR-guided adaptive RT enables delivery of curative dose to pancreatic tumors by taking into account interfraction motion of gastrointestinal OARs in daily adaptive planning. To manage intrafraction motion in our current clinical workflow, the organ motion is assessed for first two fractions and conservative strategies (e.g., bigger PRV margins or change the directive to keep stomach 0.035 cc dose to < 30 Gy) are employed for later fractions. ATS workflow with AC and the post-treatment QA allow safe delivery of ablative radiation doses for selected cases of PDAC on Unity MR-Linac. AUTHOR DISCLOSURE N. Tyagi: Honoraria; Elekta. Travel Expenses; Elekta; ISMRM.J. Liang: None. S. Burleson: None. E. Subashi: None. P. Godoy Scripes: None. K.R. Tringale: None. P.B. Romesser: Travel Expenses; Elekta. M. Reyngold: None. C.H. Crane: Honoraria; Elekta. Travel Expenses; Elekta.

MRSIGMA is a real-time volumetric MRI technique to image tumor and organs at risk motion in real-time for radiotherapy applications, where a dictionary of high-resolution 3D motion states and associated motion signatures are computed first during offline training and real-time 3D imaging is performed afterwards using fast signature-only acquisition and signature matching. However, the lack of a reference image with similar spatial resolution and temporal resolution introduces significant challenges for in vivo validation. This work proposes a retrospective self-validation for MRSIGMA, where the same data used for real-time imaging are used to create a non-real-time reference for comparison. MRSIGMA with self-validation is tested in patients with liver tumors using quantitative metrics defined on the tumor and nearby organs-at-risk structures. The dice coefficient between contours defined on the real-time MRSIGMA and non-real-time reference was used to assess motion imaging performance. Total latency (including signature acquisition and signature matching) was between 250–314ms, which is sufficient for organs affected by respiratory motion. Mean ± standard deviation dice coefficient over time was 0.74 ± 0.03 for patients imaged without contrast agent and 0.87 ± 0.03 for patients imaged with contrast agent, which demonstrated high-performance real-time motion imaging. MRSIGMA with self-evaluation provides a means to perform real-time volumetric MRI for organ motion tracking with quantitative performance measures.

Purpose To describe the details and experience of implementing a MR-only workflow in the clinic for simulation and planning of prostate cancer patients. Methods Forty-eight prostate cancer patients from June 2016 - Dec 2016 receiving external beam radiotherapy were scheduled to undergo MR-only simulation. MR images were acquired for contouring (T2w axial, coronal, sagittal), synthetic-CT generation (3D FFE-based) and fiducial identification (3D bFFE-based). The total acquisition time was 25 min. Syn-CT was generated at the console using commercial software called MRCAT. As part of acceptance testing of the MRCAT package, external laser positioning system QA (< 2 mm) and geometric fidelity QA (< 2 mm within 50 cm LR and 30 cm AP) were performed and baseline values were set. Our current combined CT + MR simulation process was modified to accommodate a MRCAT-based MR-only simulation workflow. An automated step-by-step process using a MIM™ workflow was created for contouring on the MR images. Patient setup for treatment was achieved by matching the MRCAT DRRs with the orthogonal KV radiographs based on either fiducial ROIs or bones. 3-D CBCTs were acquired and compared with the MR/syn-CT to assess the rectum and bladder filling compared to simulation conditions. Results Forty-two patients successfully underwent MR-only simulation and met all of our institutional dosimetric objectives that were developed based on a CT + MR-based workflow. The remaining six patients either had a hip prosthesis or their large body size fell outside of the geometric fidelity QA criteria and thus they were not candidates for MR-only simulation. A total time saving of ~15 min was achieved with MR-based simulation as compared to CT + MR-based simulation. An automated and organized MIM workflow made contouring on MR much easier, quicker and more accurate compared with combined CT + MR images because the temporal variations in normal structure was minimal. 2D and 3D treatment setup localization based on bones/fiducials using a MRCAT reference image was successfully achieved for all cases. Conclusions MR-only simulation and planning with equivalent or superior target delineation, planning and treatment setup localization accuracy is feasible in a clinical setting. Future work will focus on implementing a robust 3D isotropic acquisition for contouring. Electronic supplementary material The online version of this article (doi:10.1186/s13014-017-0854-4) contains supplementary material, which is available to authorized users.

Purpose The growing use of magnetic resonance imaging (MRI) as a substitute for computed tomography-based treatment planning requires the development of effective algorithms to generate electron density maps for treatment planning and patient setup verification. The purpose of this work was to develop a method to synthesize computerized tomography (CT) for MR-only radiotherapy of head and neck cancer patients. Methods The algorithm is based on registration of multiple patient datasets containing both MRI and CT images (a “multi-atlas” algorithm). Twelve matched pairs of good quality CT and MRI scans (those without apparent motion and blurring artifacts) were selected from a pool of head and neck cancer patients to form the atlas. All atlas MRI scans were pre-processed to reduce scanner- and patient-induced intensity inhomogeneities and to standardize their intensity histograms. Atlas CT and MRIs were co-registered using a novel bone-to-air replacement technique applied to the CT scans that improves the similarity between CTs and MRIs and facilitates the registration process. For each new patient, all atlas MRIs are deformed initially onto the new patients’ MRI. We introduce a generalized registration error (GRE) metric that automatically measures the goodness of local registration between MRI pairs. The final synthetic CT value at each point is a nonlinear GRE-weighted average of the atlas CTs. For evaluation, the leave-one-out technique was used for synthetic CT generation and the mean absolute error (MAE) between the original and synthetic CT was computed over the entire CT image. The impact of our proposed CT-MR registration scheme on the accuracy of the final synthetic CT was also studied. The original treatment plans were also recomputed on the new synthetic CTs and dose-volume histogram metrics were compared. In addition, the two-dimensional (2D) gamma analysis at 1%/1mm and 2%/2 mm dose difference/distance to agreement was also performed to study the dose distribution at the isocenter. Results MAE error (± standard deviation) between the original and the synthetic CTs was 64 ± 10, 113 ± 12, and 130 ± 28 Hounsfield Unit (HU) for the entire image, air, and bone regions, respectively. Our results showed that our proposed bone-suppression based CT-MR fusion and GRE-weighted strategy could lower the overall MAE error between the original and synthetic CTs by ~69% and ~34%, respectively. Dose re-calculation comparison showed highly consistent results between plans based on the synthetic vs. the original CTs. The 2D gamma analysis revealed the pass rate of 95.44 ± 2.5 and 99.36 ± 0.71 for 1%/1mm and 2%/2 mm criteria, respectively. Due to local registration weighting, the method is robust with respect to MRI imaging artifacts. Conclusion We developed a novel image analysis technique to synthesize CT for head and neck anatomy. Novel methods were introduced to accurately register atlas CTs and MRIs as well as to weight the final electron density maps using local registration goodness estimates. The resulting accuracy is clinically acceptable, at least for these atlas patients. This article is protected by copyright. All rights reserved.

Purpose To compare the image quality of amplitude-binned 4-dimensional magnetic resonance imaging (4DMRI) reconstructed using 2 concurrent respiratory (navigator and bellows) waveforms. Methods and Materials A prospective, respiratory-correlated 4DMRI scanning program was used to acquire T2-weighted single-breath 4DMRI images with internal navigator and external bellows. After a 10-second training waveform of a surrogate signal, 2-dimensional MRI acquisition was triggered at a level (bin) and anatomic location (slice) until the bin-slice table was completed for 4DMRI reconstruction. The bellows signal was always collected, even when the navigator trigger was used, to retrospectively reconstruct a bellows-rebinned 4DMRI. Ten volunteers participated in this institutional review board–approved 4DMRI study. Four scans were acquired for each subject, including coronal and sagittal scans triggered by either navigator or bellows, and 6 4DMRI images (navigator-triggered, bellows-rebinned, and bellows-triggered) were reconstructed. The simultaneously acquired waveforms and resulting 4DMRI quality were compared using signal correlation, bin/phase shift, and binning motion artifacts. The consecutive bellows-triggered 4DMRI scan was used for indirect comparison. Results Correlation coefficients between the navigator and bellows signals were found to be patient-specific and inhalation-/exhalation-dependent, ranging from 0.1 to 0.9 because of breathing irregularities (>50% scans) and commonly observed bin/phase shifts (−1.1 ± 0.6 bin) in both 1-dimensional waveforms and diaphragm motion extracted from 4D images. Navigator-triggered 4DMRI contained many fewer binning motion artifacts at the diaphragm than did the bellows-rebinned and bellows-triggered 4DMRI scans. Coronal scans were faster than sagittal scans because of the fewer slices and higher achievable acceleration factors. Conclusions Navigator-triggered 4DMRI contains substantially fewer binning motion artifacts than bellows-rebinned and bellows-triggered 4DMRI, primarily owing to the deviation of the external from the internal surrogate. The present study compared 2 concurrent surrogates during the same 4DMRI scan and their resulting 4DMRI quality. The navigator-triggered 4DMRI scanning protocol should be preferred to the bellows-based, especially for coronal scans, for clinical respiratory motion simulation.

Background and purpose: In this study we investigated the ability of textures from T1-weighted MRI scans post-contrast (T1wpost) to identify the critical muscle(s) for radiation-induced trismus. Materials and methods: The study included ten cases (Trismus: ≥Grade 1), and ten age-sex-tumor-location-and-stage-matched controls treated with intensity-modulated radiotherapy to 70 Gy@2.12 Gy in 2005–2009. Trismus status and T1wPost were conducted within one year post-radiotherapy. For the masseter, lateral and medial pterygoids, and temporalis (M/LP/MP/T), 24 textures were extracted (Grey Level Co-Occurrence (GLCM), Histogram, and Shape). Univariate logistic regression with Bootstrapping (1000 populations) was applied to compare the muscle mean dose (Dmean) and textures between cases and controls (ipsilateral muscles); candidate predictors were suggested by an average p ≤ 0.20 across all Bootstrap populations. Results: Dmean to M/LP/MP (p = 0.03/0.14/0.09), one MP/T (p = 0.12/0.17), and three M (p = 0.14–0.19) textures were candidate predictors. Three of these textures were GLCM- and two Histogram textures with the former being generally higher and the latter lower for cases compared to controls. The Dmean to M and MP, and Haralick Correlation (GLCM) of MP presented with the best discriminative ability (area under the receiver-operating characteristic curve: 0.85, 0.77, and 0.78), and the correlation between Dmean and this texture was weak (Spearman’s rank correlation coefficient: 0.26–0.27). Conclusions: Our exploratory study points towards an interplay between the dose to the masseter, and the medial pterygoid together with the local relationship between the mean MRI intensity relative to its variance of the medial pterygoid for radiation-induced trismus. This opens up for exploration of this interplay within the radiation-induced trismus etiology in the larger multi-institutional setting. Keywords: Head and neck cancer, Radiotherapy, Trismus, Quantitative image, Texture, Radiomics

Purpose To propose a real-time 3D MRI technique called MR SIGnature MAtching (MRSIGMA) for high-resolution volumetric imaging and motion tracking with very low imaging latency. Methods MRSIGMA consists of two steps: (1) offline learning of a database of possible 3D motion states and corresponding motion signature ranges and (2) online matching of new motion signatures acquired in real time with prelearned motion states. Specifically, the offline learning step (non-real-time) reconstructs motion-resolved 4D images representing different motion states and assigns a unique motion range to each state. The online matching step (real-time) acquires motion signatures only and selects one of the prelearned 3D motion states for each newly acquired signature, which generates 3D images efficiently in real time. The MRSIGMA technique was evaluated on 15 golden-angle stack-of-stars liver data sets, and the performance of respiratory motion tracking with the online-generated real-time 3D MRI was compared with the corresponding 2D projections acquired in real time. Results The total latency of generating each 3D image during online matching was about 300 ms, including acquisition of the motion signature data (~138 ms) and corresponding matching process (~150 ms). Linear correlation assessment suggested excellent correlation (R2 = 0.948) between motion displacement measured from the online-generated real-time 3D images and the 2D real-time projections. Conclusion This proof-of-concept study demonstrates the feasibility of MRSIGMA for high-resolution real-time volumetric imaging, which shifts the acquisition and reconstruction burden to an offline learning step and leaves fast online matching for online imaging with very low imaging latency. The MRSIGMA technique can potentially be used for real-time motion tracking in MRI-guided radiation therapy.

Purpose To predict the spatial and temporal trajectories of lung tumor during radiotherapy monitored under a longitudinal magnetic resonance imaging (MRI) study via a deep learning algorithm for facilitating adaptive radiotherapy (ART). Methods We monitored 10 lung cancer patients by acquiring weekly MRI-T2w scans over a course of radiotherapy. Under an ART workflow, we developed a predictive neural network (P-net) to predict the spatial distributions of tumors in the coming weeks utilizing images acquired earlier in the course. The three-step P-net consisted of a convolutional neural network to extract relevant features of the tumor and its environment, followed by a recurrence neural network constructed with gated recurrent units to analyze trajectories of tumor evolution in response to radiotherapy, and finally an attention model to weight the importance of weekly observations and produce the predictions. The performance of P-net was measured with Dice and root mean square surface distance (RMSSD) between the algorithm-predicted and experts-contoured tumors under a leave-one-out scheme. Results Tumor shrinkage was 60% ± 27% (mean ± standard deviation) by the end of radiotherapy across nine patients. Using images from the first three weeks, P-net predicted tumors on future weeks (4, 5, 6) with a Dice and RMSSD of (0.78 ± 0.22, 0.69 ± 0.24, 0.69 ± 0.26), and (2.1 ± 1.1 mm, 2.3 ± 0.8 mm, 2.6 ± 1.4 mm), respectively. Conclusion The proposed deep learning algorithm can capture and predict spatial and temporal patterns of tumor regression in a longitudinal imaging study. It closely follows the clinical workflow, and could facilitate the decision-making of ART. A prospective study including more patients is warranted.

Lack of large expert annotated MR datasets makes training deep learning models difficult. Therefore, a cross-modality (MR-CT) deep learning segmentation approach that augments training data using pseudo MR images produced by transforming expert-segmented CT images was developed. Eighty-One T2-weighted MRI scans from 28 patients with non-small cell lung cancers were analyzed. Cross-modality prior encoding the transformation of CT to pseudo MR images resembling T2w MRI was learned as a generative adversarial deep learning model. This model augmented training data arising from 6 expert-segmented T2w MR patient scans with 377 pseudo MRI from non-small cell lung cancer CT patient scans with obtained from the Cancer Imaging Archive. A two-dimensional Unet implemented with batch normalization was trained to segment the tumors from T2w MRI. This method was benchmarked against (a) standard data augmentation and two state-of-the art cross-modality pseudo MR-based augmentation and (b) two segmentation networks. Segmentation accuracy was computed using Dice similarity coefficient (DSC), Hausdroff distance metrics, and volume ratio. The proposed approach produced the lowest statistical variability in the intensity distribution between pseudo and T2w MR images measured as Kullback-Leibler divergence of 0.069. This method produced the highest segmentation accuracy with a DSC of 0.75 and the lowest Hausdroff distance on the test dataset. This approach produced highly similar estimations of tumor growth as an expert (P = 0.37). A novel deep learning MR segmentation was developed that overcomes the limitation of learning robust models from small datasets by leveraging learned cross-modality priors to augment training. The results show the feasibility of the approach and the corresponding improvement over the state-of-the-art methods., Comment: Submitted to Medical Physics

MR-only radiotherapy planning is an area of active research and development. Technological advances in MR-guided delivery systems and availability of MR scanners in radiotherapy department have made the clinical integration of MR-only planning possible. The chapter outlines challenges and requirements for MR-only radiotherapy and gives background on the most important requirement of MR-only planning, namely, the development of synthetic CT. The chapter describes the simplest method for generating synthetic CT, mainly bulk density assignment, and sets the background and motivation for more advanced methods.

Purpose: Patients with cancers of oropharynx have a favorable prognosis and are an ideal candidate for adaptive therapy. A replan to improve coverage or escalate/de-escalate dose based on morphological information alone may not be adequate as the grossly involved lymph nodes (LNs) of a subset of these patients tend to become cystic and often do not regress. Functional adaptation may be a better approach when considering replanning for these patients. The purpose of this study was to evaluate the weekly trends in treatment related morphological and physiological changes for these LNs using diffusion-weighted MRI (DW-MRI) and evaluate its implications for adaptive replanning. Methods: Ten patients with histologically proven oropharynx HNSCC undergoing concurrent chemoradiation were analyzed in this study. MR imaging protocol included axial T1w, T2w, and DW-MRI using a 3 T Philips MR scanner. The patients were scanned weekly in radiation treatment planning position using a 16 element phased-array anterior coil and a 44 element posterior coil. A total of 65 DWI and T2w scans were analyzed. DWI was performed using an optimized single-shot echo planar imaging sequence (TR/TE = 5000/65 ms, slice thickness = 5 mm; slices = 28; b values = 0 and 800 s/mm2). Quantification of the DW-MRI images was performed by calculating the apparent diffusion coefficient (ADC). T2w and DWI scans were imported into the Eclipse treatment planning system and gross tumor volumes (GTVs) corresponding to grossly involved LNs were contoured on each axial slice by physician experts. An attempt was made to remove any cystic or necrotic components so that the ADC analysis was of viable tumor only. A pixel-by-pixel fit of signal intensities within the GTVs was performed assuming monoexponential behavior. From each GTV histogram mean, median, standard deviation, skewness, and kurtosis were calculated. Absolute and percent change in weekly ADC histogram parameters and percent change in T2w GTV were also calculated. Results: For all nodes, an immediate change in ADC was observed during first 2–3 weeks after which ADC values either continued to increase or plateaued. A few nodal volumes had a slightly decreased ADC value during later weeks. Percent increase in median ADC from weeks 1 to 6 with respect to baseline was 14%, 25%, 41%, 42%, 45%, and 58%. The corresponding change in median T2 volumes was 8%, 10%, 16%, 22%, 40%, and 42%, respectively. The ADC distribution of the viable tumors was initially highly kurtotic; however, the kurtosis decreased as treatment progressed. The ADC distribution also showed a higher degree of skewness in the first 2 weeks, progressively becoming less skewed as treatment progressed so as to slowly approach a more symmetric distribution. Conclusions: Physiological changes in LNs represented by changes in ADC evaluated using DW-MRI are evident sooner than the morphological changes calculated from T2w MRI. The decisions for adaptive replanning may need to be individualized and should be based primarily on tumor functional information. The authors’ data also suggest that for many patients, week 3 maybe the optimal time to intervene and replan. Larger studies are needed to confirm their findings.

Acute esophagitis (AE) occurs among a significant number of patients with locally advanced lung cancer treated with radiotherapy. Early prediction of AE, indicated by esophageal wall expansion, is critical, as it can facilitate the redesign of treatment plans to reduce radiation-induced esophageal toxicity in an adaptive radiotherapy (ART) workflow. We have developed a novel machine learning framework to predict the patient-specific spatial presentation of the esophagus in the weeks following treatment, using magnetic resonance imaging (MRI)/ cone-beam CT (CBCT) scans acquired earlier in the 6 week radiotherapy course. Our algorithm captures the response patterns of the esophagus to radiation on a patch level, using a convolutional neural network. A recurrence neural network then parses the evolutionary patterns of the selected features in the time series, and produces a predicted esophagus-or-not label for each individual patch over future weeks. Finally, the esophagus is reconstructed, using all the predicted labels. The algorithm is trained and validated by means of ∼ 250 000 patches taken from MRI scans acquired weekly from a variety of patients, and tested using both weekly MRI and CBCT scans under a leave-one-patient-out scheme. In addition, our approach is externally validated using a publicly available dataset (Hugo 2017). Using the first three weekly scans, the algorithm can predict the condition of the esophagus over the succeeding 3 weeks with a Dice coefficient of 0.83 ± 0.04, estimate esophagus volume highly (0.98), correlated with the actual volume, using our institutional MRI/CBCT data. When evaluated using the external weekly CBCT data, the averaged Dice coefficient is 0.89 ± 0.03. Our novel algorithm may prove useful in enabling radiation oncologists to monitor and detect AE in its early stages, and could potentially play an important role in the ART decision-making process.

e20028 Background: Breast Implant Associated Anaplastic Large Cell Lymphoma (BIA-ALCL), a subtype of ALCL, arises as a seroma in the space between the implant and the capsule, or as an adjacent mass. BIA-ALCL appears to be related to textured-surface implants, after 7-10 years of exposure. We conducted two large cohort studies. The 1st, a retrospective series (Sutton, 2019) assessed the incidental findings of masses or seromas in 1070 women with breast implants undergoing MR for FDA recommended screening for silent ruptures of silicone implants. Incidental finding of seromas or breast masses on MRI were found in 18/1070 (1.7%) women, of which 1/15 had BIA-ALCL, and was symptomatic at the time of MRI. The 2nd, a prospective study (Cordeiro, 2020) defined the incidence of BIA-ALCL (1/355) in a cohort of 3546 women with textured implants followed long term. Within this cohort, there were 28 clinically relevant delayed seromas (0.79%), 8 of which were BIA-ALCL (28.5%). We hypothesize that combining these databases will inform whether asymptomatic women with textured implants may benefit from MR to r/o BIA-ALCL. Methods: The two IRB approved databases were merged. Patients with incidental findings of seroma on MRI were identified. A majority of the MRIs in this merged cohort were performed to follow the FDA recommended screening for silent ruptures. We identified all clinically relevant late seromas sent to hematopathology to r/o BIA-ALCL by cross checking pathology reports containing the words “lymphoma” or “anaplastic” or “ALCL”. Results: 572 women were included in both studies: followed long term and received an MRI after a median time of 7.4 years after breast reconstruction. 8 of 572 women had an incidental finding of seroma on random MRI, and 2 had capsular masses. None of these 10 asymptomatic women have developed BIA-ALCL to date (median follow-up 9 years). 11 of 572 women had a symptomatic seroma or mass, 4 of which later developed BIA-ALCL, a fifth patient was found to have BIA-ALCL on a PET/CT + lymph node, despite being asymptomatic. Median time from last MR to lymphoma was 5 years (3-8 years). Conclusions: In this merged cohort of patients with textured breast implants, incidence of BIA-ALCL in patients with symptomatic late seromas is around 30%, while seromas found incidentally on MR of asymptomatic patients were negative for BIA-ALCL.

Technical innovations and developments in areas such as disease localization, dose calculation algorithms, motion management and dose delivery technologies have revolutionized radiation therapy resulting in improved patient care with superior outcomes. A consequence of the ability to design and accurately deliver complex radiation fields is the need for improved target visualization through imaging. While CT imaging has been the standard of care for more than three decades, the superior soft tissue contrast afforded by MR has resulted in the adoption of this technology in radiation therapy. With the development of real time MR imaging techniques, the problem of real time motion management is enticing. Currently, the integration of an MR imaging and megavoltage radiation therapy treatment delivery system (MR-linac or MRL) is a reality that has the potential to provide improved target localization and real time motion management during treatment. Higher magnetic field strengths provide improved image quality potentially providing the backbone for future work related to image texture analysis-a field known as Radiomics-thereby providing meaningful information on the selection of future patients for radiation dose escalation, motion-managed treatment techniques and ultimately better patient care. On-going advances in MRL technologies promise improved real time soft tissue visualization, treatment margin reductions, beam optimization, inhomogeneity corrected dose calculation, fast multileaf collimators and volumetric arc radiation therapy. This review article provides rationale, advantages and disadvantages as well as ideas for future research in MRI related to radiation therapy mainly in adoption of MRL.

External beam radiotherapy for prostate cancer is in a period of rapid change and evolution as treatment paradigms shift toward hypofractionated regimens. Technological advances including image guided radiotherapy, volume modulated arc therapy and the use of magnetic resonance imaging for planning, treatment delivery and response assessment have made the move to hypofractionation possible. In this chapter, the treatment planning process applicable to hypofractionated, stereotactic radiotherapy for prostate cancer using a conventional linear accelerator is described with an emphasis on the increasing role of magnetic resonance imaging in simulation and planning. A simulation workflow using MR imaging only is presented and compared with one based on multi-modality imaging (CT and MRI). An overview of volume modulated arc therapy planning is provided with examples for extreme hypofractionation and simultaneous boost treatment of intra-prostatic disease. Further developments of MR-based planning methods to target dominant intra-prostatic lesions, perform on-line adaptive therapy and MR-guided radiotherapy delivery are discussed.

We present an adversarial domain adaptation based deep learning approach for automatic tumor segmentation from T2-weighted MRI. Our approach is composed of two steps: (i) a tumor-aware unsupervised cross-domain adaptation (CT to MRI), followed by (ii) semi-supervised tumor segmentation using Unet trained with synthesized and limited number of original MRIs. We introduced a novel target specific loss, called tumor-aware loss, for unsupervised cross-domain adaptation that helps to preserve tumors on synthesized MRIs produced from CT images. In comparison, state-of-the art adversarial networks trained without our tumor-aware loss produced MRIs with ill-preserved or missing tumors. All networks were trained using labeled CT images from 377 patients with non-small cell lung cancer obtained from the Cancer Imaging Archive and unlabeled T2w MRIs from a completely unrelated cohort of 6 patients with pre-treatment and 36 on-treatment scans. Next, we combined 6 labeled pre-treatment MRI scans with the synthesized MRIs to boost tumor segmentation accuracy through semi-supervised learning. Semi-supervised training of cycle-GAN produced a segmentation accuracy of 0.66 computed using Dice Score Coefficient (DSC). Our method trained with only synthesized MRIs produced an accuracy of 0.74 while the same method trained in semi-supervised setting produced the best accuracy of 0.80 on test. Our results show that tumor-aware adversarial domain adaptation helps to achieve reasonably accurate cancer segmentation from limited MRI data by leveraging large CT datasets.

In this paper, a new heuristic algorithm is constructed for three machines stochastic flow shop scheduling problem to utilized four parameters simultaneously. The parameters which we have used are probabilistic processing time, transportation times, Job block, and weight of the job. The main objective to construct this algorithm is to obtain the optimal sequence to minimize the makespan, Total utilization times and Total rental cost of the hired machines. A numerical example and Gantt chart is also provided to verify the effectiveness of the developed heuristic algorithm.

In this paper, a new heuristic algorithm is constructed for four machines stochastic flow shop scheduling problem to utilized stochastic processing times and stochastic sequence dependent setup times of the jobs on the machines simultaneously. The main objective to construct this heuristic algorithm is to find the optimal or near optimal sequence to minimize the makespan, total utilization time of the machines and total rental cost of all the hired machines. A numerical example is provided to verify the effectiveness of the developed heuristic algorithm.

Purpose: To generate hybrid VMAT‐IMRT treatment plans by utilizing an IMRT beam‐angle‐optimality (BAO) search under a commercially available TPS without the aid of custom optimizationsoftware or high performance computing.Methods: The high dose modulation provided by IMRT beams can be exploited to improve the quality of VMAT treatments. To achieve this, a VMAT treatment was created where the gantry pauses at predetermined angles to deliver IMRT segments. To determine IMRT BAO, an IMRT plan (‘poly‐IMRT’) was made with many beams (>30) equally spaced around the patient. For practical reasons and to reduce the total time to approximately 1 hour, BAO was approximated by removing one beam out of the set and noting the new objective score. Determining this‘score penalty’ for each of the beams serves as a proxy for true BAO. The hybrid plan was created by combining the VMAT arc with a user‐determined number of top‐ranked beams from the poly‐IMRT set. The BAO from this approach was compared with a more rigorous method (‘VMAT+1’), in which a VMAT plan was optimized with 1 IMRT beam at various angles, allowing a direct determination of objective score versus gantry angle. The overall hybrid planning process was demonstrated by creating separate plans for a SBRTlung patient, with dose normalized to the limiting maximum aorta dose. Results: Large score penalties from poly‐IMRT coincided with large score benefits from VMAT+1, indicating both methods identified the same optimal beams. The VMAT, IMRT, and hybrid plans delivered the prescription dose to 84.3%, 85.6% and 87.7% of the PTV and had homogeneity indices of 1.38, 1.41, and 1.32 respectively. Normal tissue doses were within 0.5%. Conclusion: The presented method can create hybrid VMAT‐IMRT plans which combine delivery efficiency with improved target coverage. The planning process takes about an hour using a standard TPS.

Purpose: To develop a real time dose monitoring and dose reconstruction tool to identify and quantify sources of errors during patient specific VMAT delivery and QAMethods: The VMAT delivery monitor tool called Linac Data Monitor (LDM) has been developed that connects to the linac in clinical mode and displays, records and compares real‐time machine parameters to the planned parameters. A new quantity called integral error keeps a running total of leaf overshoot and undershoots errors in each leaf pair multiplied by leaf width and the amount of time during which error exists in MU delivery. Another tool reconstructs pinnacle format delivered plan based on the saved machine logfile and recalculates actual delivered dose in patient anatomy.Delivery characteristics of various standard and hypofractionation VMAT plans delivered on Elekta Axesse and Synergy linacs were quantified. Results: The MLC and gantry errors for all the treatment sites were 0.00±0.59mm and 0.05±0.31°, indicating a good MLC gain calibration. Standard fractionation plans had a larger gantry error than hypofractionation plans due to frequent dose rate changes. On average the MLC errors were negligible but larger errors of 4–6 mm and 2.5° were seen when dose rate varied frequently. Large gantry errors occurred during the acceleration and deceleration process, and correlated well with MLC errors (p

Objectives: To find the optimum solution for minimization of bicriterion (makespan, weighted mean flowtime) objective function of three machines flowshop scheduling problem with transportation times and weight of the jobs. Methods/Statistical Analysis: In this paper, we used two types of methodologies first one is based on a Branch and Bound (BB we developed a new heuristic algorithm using Palmer approach for obtaining the optimal or near optimal sequence to minimize the bicriterion objective function of three machines scheduling problem in flowshop environments with transportation times and weights of the jobs. Comparative study between both the proposed algorithms is also considered to select the best methodology of our bicriterion objective function with the help of numerical illustration. Directed graphs, Gantt chart and Branch Tree are also generated to understand the process of lower bound and effectiveness of proposed algorithms. Findings: We solved the same numerical by constructed Branch & Bound (B&B) algorithm and Palmer based heuristic algorithm. Hence, comparatative result show that our originated B&B algorithm gives the optimal solution or better result as compare to Palmer based heuristic algorithm for minimization of bicriterion (makespan and weighted mean flowtime) objective function. We also calculated the percentage improvement of our constructive B&B algorithm over palmer based new heuristic algorithm and it is examined that constructive B&B algorithm gives the 8.33% improvement in make span and 6.52% improvement in weighted mean flowtime. The directed graph of each computational level is also originated to understand the computational process of the lower bounds easily. The Gantt chart between both the proposed algorithms is also generated to verify the effectiveness of new originated B&B algorithm. Directed graph is also generated of the optimal sequence. Finally, Branch Tree is generated to empathize the process of Lower Bound. Application/Improvements: Our constructed B&B algorithm provide an important tool for decision maker to minimize the makespan and weighted mean flowtime together as bicriterion objective function of three machine flowshop scheduling problems.

To evaluate a commercial synthetic CT (syn-CT) software for use in prostate radiotherapy. Twenty-five prostate patients underwent CT and MR simulation scans in treatment position on a 3T MR scanner. A commercially available MR protocol was used that included a T2w turbo spin-echo sequence for soft-tissue contrast and a dual echo 3D mDIXON fast field echo (FFE) sequence for generating syn-CT. A dual-echo 3D FFE B 0 map was used for patient-induced susceptibility distortion analysis and a new 3D balanced-FFE sequence was evaluated for identification of implanted gold fiducial markers and subsequent image-guidance during radiotherapy delivery. Tissues were classified as air, adipose, water, trabecular/spongy bone and compact/cortical bone and assigned bulk HU values. The accuracy of syn-CT for treatment planning was analyzed by transferring the structures and plan from planning CT to syn-CT and recalculating the dose. Accuracy of localization at the treatment machine was evaluated by comparing registration of kV radiographs to either digitally reconstructed radiographs (DRRs) generated from syn-CT or traditional DRRs generated from the planning CT. Similarly, accuracy of setup using CBCT and syn-CT was compared to that using the planning CT. Finally, a MR-only simulation workflow was established and end-to-end testing was completed on five patients undergoing MR-only simulation. Dosimetric comparison between the original CT and syn-CT plans was within 0.5% on average for all structures. The de-novo optimized plans on the syn-CT met institutional clinical objectives for target and normal structures. Patient-induced susceptibility distortion based on B 0 maps was within 1 mm and 0.5 mm in the body and prostate respectively. DRR and CBCT localization based on MR-localized fiducials showed a standard deviation of

Objectives: In this paper, we consider the Flexible Flowshop Scheduling (FFS) problem with parallel machines. The main objective of this paper is to obtain a good schedule of jobs to minimize the makespan of FFS problem. Methods/Statistical analysis: In this study, two heuristic algorithms have been developed of FFS to reduce the makespan. First, we constructed the new heuristic algorithm based on Minimum Processing Time Selective Approach (MPTSA) and Longest Processing Times (LPT) approach to find the optimal or near optimal sequence for minimization of makespan of FFS problem with parallel machines. Next, we developed the heuristic algorithm using PALMER approach. In the PALMER approach we sequence the jobs based on Longest Slope Value (LSV) and obtained the value of objective function. Findings: We compared both the heuristic algorithms with the help of numerical illustrations. We solved the same numerical by both the heuristic algorithm and result show that our constructed heuristic algorithm has resulted in a better industrial production makespan. The percentage improvement of our constructive heuristic algorithm is also calculated. Gantt chart is also generated to verify the effectiveness of constructed heuristic algorithm. Application/Improvements: Our constructed heuristic algorithm is more effective to reduced the makespan of FFS problems as compare to classic heuristic algorithm as Palmer approach and provide an important tool for decision makers in production management.

In this paper, we have developed a new heuristic algorithm for three machines flowshop scheduling problem using five parameters together. The parameters, which we have used in this paper, are separate setup times, transportation times, Job block, weight of job and specified rental policy. The main aim to introduce this algorithm is to obtain the optimal sequence to minimize the makespan, utilization times and rental cost of the machines. We used the Johnson's Techniques of three machines for developing this heuristic algorithm. A numerical illustration is also provided to verify the effectiveness of developed heuristic algorithm.

Objectives: In this paper, Five Dispatching Rules and a Branch & Bound algorithm is introduced for Single Machine Total Tardiness Scheduling Problem (SMTTSP) to minimize the total (average) tardiness and number of tardy jobs. Methods/Statistical Analysis: We proposed five dispatching (priority) rules as Shortest Processing Time, Earliest Due Dates, Longest Processing Time, Minimum Slack Time and First Come First Serve for SMTTSP and compared the performance of all the proposed priority rules. Furthermore, a numerical illustrations is also provided to select the best dispatched rule of SMTTSP. Next, we developed a Branch & Bound Algorithm for SMTTSP using best selected dispatching rule. We also developed Branch Tree to understand the Lower bound process of the B& B Algorithm. Findings: The main aim to proposed these dispatching rules and a Branch and Bound Algorithm is to obtain the optimal sequence to optimize the total (average) tardiness and number of total tardy jobs. The comparative analysis of the dispatching rules shows that EDD rule is better than other dispatching rules for minimization of total tardy jobs and tardiness of the jobs while SPT rule is better for minimization of make span. But Dispatching rules do not have the guarantee to give an optimal solution. So in this study, an exact algorithm (Branch & Bound) was developed with EDD rule for finding the optimal solution for SMTTSP. The comparative study between dispatching rules and an exact (B&B) algorithm is being justified by numerical illustrations and we found that the EDD rule and B&B Algorithm give the same results. Hence it was concluded that EDD rule works as an Exact algorithm and gives the optimal solution for SMTTSP. Application/Improvements: The computational results of the proposed (B&B) algorithm and Dispatching rules show that our methodology is more useful than other optimal approach for SMTTSP and it provides an important tool for decision makers.

Capsular contracture (CC) is a serious complication in patients receiving implant-based reconstruction for breast cancer. Currently, no objective methods are available for assessing CC. The goal of the present study was to identify image-based surrogates of CC using magnetic resonance imaging (MRI).We analyzed a retrospective data set of 50 patients who had undergone both a diagnostic MRI scan and a plastic surgeon's evaluation of the CC score (Baker's score) within a 6-month period after mastectomy and reconstructive surgery. The MRI scans were assessed for morphologic shape features of the implant and histogram features of the pectoralis muscle. The shape features, such as roundness, eccentricity, solidity, extent, and ratio length for the implant, were compared with the Baker score. For the pectoralis muscle, the muscle width and median, skewness, and kurtosis of the intensity were compared with the Baker score. Univariate analysis (UVA) using a Wilcoxon rank-sum test and multivariate analysis with the least absolute shrinkage and selection operator logistic regression was performed to determine significant differences in these features between the patient groups categorized according to their Baker's scores.UVA showed statistically significant differences between grade 1 and grade ≥2 for morphologic shape features and histogram features, except for volume and skewness. Only eccentricity, ratio length, and volume were borderline significant in differentiating grade ≤2 and grade ≥3. Features with P.1 on UVA were used in the multivariate least absolute shrinkage and selection operator logistic regression analysis. Multivariate analysis showed a good level of predictive power for grade 1 versus grade ≥2 CC (area under the receiver operating characteristic curve 0.78, sensitivity 0.78, and specificity 0.82) and for grade ≤2 versus grade ≥3 CC (area under the receiver operating characteristic curve 0.75, sensitivity 0.75, and specificity 0.79).The morphologic shape features described on MR images were associated with the severity of CC. MRI has the potential to further improve the diagnostic ability of the Baker score in breast cancer patients who undergo implant reconstruction.