AAPM Task Group 334: A guidance document to using radiotherapy immobilization devices and accessories in an MR environment.

Use of magnetic resonance (MR) imaging in radiation therapy has increased substantially in recent years as more radiotherapy centers are having MR simulators installed, requesting more time on clinical diagnostic MR systems, or even treating with combination MR linear accelerator (MR-linac) systems. With this increased use, to ensure the most accurate integration of images into radiotherapy (RT), RT immobilization devices and accessories must be able to be used safely in the MR environment and produce minimal perturbations. The determination of the safety profile and considerations often falls to the medical physicist or other support staff members who at a minimum should be a Level 2 personnel as per the ACR. The purpose of this guidance document will be to help guide the user in making determinations on MR Safety labeling (i.e., MR Safe, Conditional, or Unsafe) including standard testing, and verification of image quality, when using RT immobilization devices and accessories in an MR environment.

Impact of proton PBS machine operating parameters on the effectiveness of layer rescanning for interplay effect mitigation in lung SBRT treatment.

BACKGROUND: Rescanning is a common technique used in proton pencil beam scanning to mitigate the interplay effect. Advances in machine operating parameters across different generations of particle therapy systems have led to improvements in beam delivery time (BDT). However, the potential impact of these improvements on the effectiveness of rescanning remains an underexplored area in the existing research. METHODS: We systematically investigated the impact of proton machine operating parameters on the effectiveness of layer rescanning in mitigating interplay effect during lung SBRT treatment, using the CIRS phantom. Focused on the Hitachi synchrotron particle therapy system, we explored machine operating parameters from our institution's current (2015) and upcoming systems (2025A and 2025B). Accumulated dynamic 4D dose were reconstructed to assess the interplay effect and layer rescanning effectiveness. RESULTS: Achieving target coverage and dose homogeneity within 2% deviation required 6, 6, and 20 times layer rescanning for the 2015, 2025A, and 2025B machine parameters, respectively. Beyond this point, further increasing the number of layer rescanning did not further improve the dose distribution. BDTs without rescanning were 50.4, 24.4, and 11.4 s for 2015, 2025A, and 2025B, respectively. However, after incorporating proper number of layer rescanning (six for 2015 and 2025A, 20 for 2025B), BDTs increased to 67.0, 39.6, and 42.3 s for 2015, 2025A, and 2025B machine parameters. Our data also demonstrated the potential problem of false negative and false positive if the randomness of the respiratory phase at which the beam is initiated is not considered in the evaluation of interplay effect. CONCLUSION: The effectiveness of layer rescanning for mitigating interplay effect is affected by machine operating parameters. Therefore, past clinical experiences may not be applicable to modern machines.

A high-resolution cone beam computed tomography (HRCBCT) reconstruction framework for CBCT-guided online adaptive therapy.

BACKGROUND: Kilo-voltage cone-beam computed tomography (CBCT) is a prevalent modality used for adaptive radiotherapy (ART) due to its compatibility with linear accelerators and ability to provide online imaging. However, the widely-used Feldkamp-Davis-Kress (FDK) reconstruction algorithm has several limitations, including potential streak aliasing artifacts and elevated noise levels. Iterative reconstruction (IR) techniques, such as total variation (TV) minimization, dictionary-based methods, and prior information-based methods, have emerged as viable solutions to address these limitations and improve the quality and applicability of CBCT in ART. PURPOSE: One of the primary challenges in IR-based techniques is finding the right balance between minimizing image noise and preserving image resolution. To overcome this challenge, we have developed a new reconstruction technique called high-resolution CBCT (HRCBCT) that specifically focuses on improving image resolution while reducing noise levels. METHODS: The HRCBCT reconstruction technique builds upon the conventional IR approach, incorporating three components: the data fidelity term, the resolution preservation term, and the regularization term. The data fidelity term ensures alignment between reconstructed values and measured projection data, while the resolution preservation term exploits the high resolution of the initial Feldkamp-Davis-Kress (FDK) algorithm. The regularization term mitigates noise during the IR process. To enhance convergence and resolution at each iterative stage, we applied Iterative Filtered Backprojection (IFBP) to the data fidelity minimization process. RESULTS: We evaluated the performance of the proposed HRCBCT algorithm using data from two physical phantoms and one head and neck patient. The HRCBCT algorithm outperformed all four different algorithms; FDK, Iterative Filtered Back Projection (IFBP), Compressed Sensing based Iterative Reconstruction (CSIR), and Prior Image Constrained Compressed Sensing (PICCS) methods in terms of resolution and noise reduction for all data sets. Line profiles across three line pairs of resolution revealed that the HRCBCT algorithm delivered the highest distinguishable line pairs compared to the other algorithms. Similarly, the Modulation Transfer Function (MTF) measurements, obtained from the tungsten wire insert on the CatPhan 600 physical phantom, showed a significant improvement with HRCBCT over traditional algorithms. CONCLUSION: The proposed HRCBCT algorithm offers a promising solution for enhancing CBCT image quality in adaptive radiotherapy settings. By addressing the challenges inherent in traditional IR methods, the algorithm delivers high-definition CBCT images with improved resolution and reduced noise throughout each iterative step. Implementing the HR CBCT algorithm could significantly impact the accuracy of treatment planning during online adaptive therapy.

Technical note: Progressive deep learning: An accelerated training strategy for medical image segmentation.

BACKGROUND: Recent advancements in Deep Learning (DL) methodologies have led to state-of-the-art performance in a wide range of applications especially in object recognition, classification, and segmentation of medical images. However, training modern DL models requires a large amount of computation and long training times due to the complex nature of network structures and the large number of training datasets involved. Moreover, it is an intensive, repetitive manual process to select the optimized configuration of hyperparameters for a given DL network. PURPOSE: In this study, we present a novel approach to accelerate the training time of DL models via the progressive feeding of training datasets based on similarity measures for medical image segmentation. We term this approach Progressive Deep Learning (PDL). METHODS: The two-stage PDL approach was tested on the auto-segmentation task for two imaging modalities: CT and MRI. The training datasets were ranked according to similarity measures between each sample based on Mean Square Error (MSE), Peak Signal-to-Noise Ratio (PSNR), Structural Similarity Index (SSIM), and the Universal Quality Image Index (UQI) values. At the start of the training process, a relatively coarse sampling of training datasets with higher ranks was used to optimize the hyperparameters of the DL network. Following this, the samples with higher ranks were used in step 1 to yield accelerated loss minimization in early training epochs and the total dataset was added in step 2 for the remainder of training. RESULTS: Our results demonstrate that the PDL approach can reduce the training time by nearly half (∼49%) and can predict segmentations (CT U-net/DenseNet dice coefficient: 0.9506/0.9508, MR U-net/DenseNet dice coefficient: 0.9508/0.9510) without major statistical difference (Wilcoxon signed-rank test) compared to the conventional DL approach. The total training times with a fixed cutoff at 0.95 DSC for the CT dataset using DenseNet and U-Net architectures, respectively, were 17 h, 20 min and 4 h, 45 min in the conventional case compared to 8 h, 45 min and 2 h, 20 min with PDL. For the MRI dataset, the total training times using the same architectures were 2 h, 54 min and 52 min in the conventional case and 1 h, 14 min and 25 min with PDL. CONCLUSION: The proposed PDL training approach offers the ability to substantially reduce the training time for medical image segmentation while maintaining the performance achieved in the conventional case.

Ensemble learning and personalized training for the improvement of unsupervised deep learning-based synthetic CT reconstruction.

BACKGROUND: The growing adoption of magnetic resonance imaging (MRI)-guided radiation therapy (RT) platforms and a focus on MRI-only RT workflows have brought the technical challenge of synthetic computed tomography (sCT) reconstruction to the forefront. Unpaired-data deep learning-based approaches to the problem offer the attractive characteristic of not requiring paired training data, but the gap between paired- and unpaired-data results can be limiting. PURPOSE: We present two distinct approaches aimed at improving unpaired-data sCT reconstruction results: a cascade ensemble that combines multiple models and a personalized training strategy originally designed for the paired-data setting. METHODS: Comparisons are made between the following models: (1) the paired-data fully convolutional DenseNet (FCDN), (2) the FCDN with the Intentional Deep Overfit Learning (IDOL) personalized training strategy, (3) the unpaired-data CycleGAN, (4) the CycleGAN with the IDOL training strategy, and (5) the CycleGAN as an intermediate model in a cascade ensemble approach. Evaluation of the various models over 25 total patients is carried out using a five-fold cross-validation scheme, with the patient-specific IDOL models being trained for the five patients of fold 3, chosen at random. RESULTS: In both the paired- and unpaired-data settings, adopting the IDOL training strategy led to improvements in the mean absolute error (MAE) between true CT images and sCT outputs within the body contour (mean improvement, paired- and unpaired-data approaches, respectively: 38%, 9%) and in regions of bone (52%, 5%), the peak signal-to-noise ratio (PSNR; 15%, 7%), and the structural similarity index (SSIM; 6%, <1%). The ensemble approach offered additional benefits over the IDOL approach in all three metrics (mean improvement over unpaired-data approach in fold 3; MAE: 20%; bone MAE: 16%; PSNR: 10%; SSIM: 2%), and differences in body MAE between the ensemble approach and the paired-data approach are statistically insignificant. CONCLUSIONS: We have demonstrated that both a cascade ensemble approach and a personalized training strategy designed initially for the paired-data setting offer significant improvements in image quality metrics for the unpaired-data sCT reconstruction task. Closing the gap between paired- and unpaired-data approaches is a step toward fully enabling these powerful and attractive unpaired-data frameworks.

Real-time liver tumor localization via a single x-ray projection using deep graph neural network-assisted biomechanical modeling.

Objective.Real-time imaging is highly desirable in image-guided radiotherapy, as it provides instantaneous knowledge of patients' anatomy and motion during treatments and enables online treatment adaptation to achieve the highest tumor targeting accuracy. Due to extremely limited acquisition time, only one or few x-ray projections can be acquired for real-time imaging, which poses a substantial challenge to localize the tumor from the scarce projections. For liver radiotherapy, such a challenge is further exacerbated by the diminished contrast between the tumor and the surrounding normal liver tissues. Here, we propose a framework combining graph neural network-based deep learning and biomechanical modeling to track liver tumor in real-time from a single onboard x-ray projection.Approach.Liver tumor tracking is achieved in two steps. First, a deep learning network is developed to predict the liver surface deformation using image features learned from the x-ray projection. Second, the intra-liver deformation is estimated through biomechanical modeling, using the liver surface deformation as the boundary condition to solve tumor motion by finite element analysis. The accuracy of the proposed framework was evaluated using a dataset of 10 patients with liver cancer.Main results.The results show accurate liver surface registration from the graph neural network-based deep learning model, which translates into accurate, fiducial-less liver tumor localization after biomechanical modeling (<1.2 (±1.2) mm average localization error).Significance.The method demonstrates its potentiality towards intra-treatment and real-time 3D liver tumor monitoring and localization. It could be applied to facilitate 4D dose accumulation, multi-leaf collimator tracking and real-time plan adaptation. The method can be adapted to other anatomical sites as well.

Intentional deep overfit learning (IDOL): A novel deep learning strategy for adaptive radiation therapy.

PURPOSE: Applications of deep learning (DL) are essential to realizing an effective adaptive radiotherapy (ART) workflow. Despite the promise demonstrated by DL approaches in several critical ART tasks, there remain unsolved challenges to achieve satisfactory generalizability of a trained model in a clinical setting. Foremost among these is the difficulty of collecting a task-specific training dataset with high-quality, consistent annotations for supervised learning applications. In this study, we propose a tailored DL framework for patient-specific performance that leverages the behavior of a model intentionally overfitted to a patient-specific training dataset augmented from the prior information available in an ART workflow-an approach we term Intentional Deep Overfit Learning (IDOL). METHODS: Implementing the IDOL framework in any task in radiotherapy consists of two training stages: (1) training a generalized model with a diverse training dataset of N patients, just as in the conventional DL approach, and (2) intentionally overfitting this general model to a small training dataset-specific the patient of interest ( N + 1 ) generated through perturbations and augmentations of the available task- and patient-specific prior information to establish a personalized IDOL model. The IDOL framework itself is task-agnostic and is, thus, widely applicable to many components of the ART workflow, three of which we use as a proof of concept here: the autocontouring task on replanning CTs for traditional ART, the MRI super-resolution (SR) task for MRI-guided ART, and the synthetic CT (sCT) reconstruction task for MRI-only ART. RESULTS: In the replanning CT autocontouring task, the accuracy measured by the Dice similarity coefficient improves from 0.847 with the general model to 0.935 by adopting the IDOL model. In the case of MRI SR, the mean absolute error (MAE) is improved by 40% using the IDOL framework over the conventional model. Finally, in the sCT reconstruction task, the MAE is reduced from 68 to 22 HU by utilizing the IDOL framework. CONCLUSIONS: In this study, we propose a novel IDOL framework for ART and demonstrate its feasibility using three ART tasks. We expect the IDOL framework to be especially useful in creating personally tailored models in situations with limited availability of training data but existing prior information, which is usually true in the medical setting in general and is especially true in ART.

Abdominal synthetic CT reconstruction with intensity projection prior for MRI-only adaptive radiotherapy.

Objective. Owing to the superior soft tissue contrast of MRI, MRI-guided adaptive radiotherapy (ART) is well-suited to managing interfractional changes in anatomy. An MRI-only workflow is desirable, but producing synthetic CT (sCT) data through paired data-driven deep learning (DL) for abdominal dose calculations remains a challenge due to the highly variable presence of intestinal gas. We present the preliminary dosimetric evaluation of our novel approach to sCT reconstruction that is well suited to handling intestinal gas in abdominal MRI-only ART.Approach. We utilize a paired data DL approach enabled by the intensity projection prior, in which well-matching training pairs are created by propagating air from MRI to corresponding CT scans. Evaluations focus on two classes: patients with (1) little involvement of intestinal gas, and (2) notable differences in intestinal gas presence between corresponding scans. Comparisons between sCT-based plans and CT-based clinical plans for both classes are made at the first treatment fraction to highlight the dosimetric impact of the variable presence of intestinal gas.Main results. Class 1 patients (n= 13) demonstrate differences in prescribed dose coverage of the PTV of 1.3 ± 2.1% between clinical plans and sCT-based plans. Mean DVH differences in all structures for Class 1 patients are found to be statistically insignificant. In Class 2 (n= 20), target coverage is 13.3 ± 11.0% higher in the clinical plans and mean DVH differences are found to be statistically significant.Significance. Significant deviations in calculated doses arising from the variable presence of intestinal gas in corresponding CT and MRI scans result in uncertainty in high-dose regions that may limit the effectiveness of adaptive dose escalation efforts. We have proposed a paired data-driven DL approach to sCT reconstruction for accurate dose calculations in abdominal ART enabled by the creation of a clinically unavailable training data set with well-matching representations of intestinal gas.

Evaluation of super-resolution on 50 pancreatic cancer patients with real-time cine MRI from 0.35T MRgRT.

MR-guided radiotherapy (MRgRT) systems provide excellent soft tissue imaging immediately prior to and in real time during radiation delivery for cancer treatment. However, 2D cine MRI often has limited spatial resolution due to high temporal resolution. This work applies a super resolution machine learning framework to 3.5 mm pixel edge length, low resolution (LR), sagittal 2D cine MRI images acquired on a MRgRT system to generate 0.9 mm pixel edge length, super resolution (SR), images originally acquired at 4 frames per second (FPS). LR images were collected from 50 pancreatic cancer patients treated on a ViewRay MR-LINAC. SR images were evaluated using three methods. 1) The first method utilized intrinsic image quality metrics for evaluation. 2) The second used relative metrics including edge detection and structural similarity index (SSIM). 3) Finally, automatically generated tumor contours were created on both low resolution and super resolution images to evaluate target delineation and compared with DICE and SSIM. Intrinsic image quality metrics all had statistically significant improvements for SR images versus LR images, with mean (±1 SD) BRISQUE scores of 29.65 ± 2.98 and 42.48 ± 0.98 for SR and LR, respectively. SR images showed good agreement with LR images in SSIM evaluation, indicating there was not significant distortion of the images. Comparison of LR and SR images with paired high resolution (HR) 3D images showed that SR images had a mean (±1 SD) SSIM value of 0.633 ± 0.063 and LR a value of 0.587 ± 0.067 (p ≪ 0.05). Contours generated on SR images were also more robust to noise addition than those generated on LR images. This study shows that super resolution with a machine learning framework can generate high spatial resolution images from 4fps low spatial resolution cine MRI acquired on the ViewRay MR-LINAC while maintaining tumor contour quality and without significant acquisition or post processing delay.

Development of Dosimetric Verification System for Patient-Specific Quality Assurance of High-Dose-Rate Brachytherapy.

Purpose: The aim of this study was to develop a dosimetric verification system (DVS) using a solid phantom for patient-specific quality assurance (QA) of high-dose-rate brachytherapy (HDR-BT). Methods: The proposed DVS consists of three parts: dose measurement, dose calculation, and analysis. All the dose measurements were performed using EBT3 film and a solid phantom. The solid phantom made of acrylonitrile butadiene styrene (ABS, density = 1.04 g/cm3) was used to measure the dose distribution. To improve the accuracy of dose calculation by using the solid phantom, a conversion factor [CF(r)] according to the radial distance between the water and the solid phantom material was determined by Monte Carlo simulations. In addition, an independent dose calculation program (IDCP) was developed by applying the obtained CF(r). To validate the DVS, dosimetric verification was performed using gamma analysis with 3% dose difference and 3 mm distance-to-agreement criterion for three simulated cases: single dwell position, elliptical dose distribution, and concave elliptical dose distribution. In addition, the possibility of applying the DVS in the high-dose range (up to 15 Gy) was evaluated. Results: The CF(r) between the ABS and water phantom was 0.88 at 0.5 cm. The factor gradually increased with increasing radial distance and converged to 1.08 at 6.0 cm. The point doses 1 cm below the source were 400 cGy in the treatment planning system (TPS), 373.73 cGy in IDCP, and 370.48 cGy in film measurement. The gamma passing rates of dose distributions obtained from TPS and IDCP compared with the dose distribution measured by the film for the simulated cases were 99.41 and 100% for the single dwell position, 96.80 and 100% for the elliptical dose distribution, 88.91 and 99.70% for the concave elliptical dose distribution, respectively. For the high-dose range, the gamma passing rates in the dose distributions between the DVS and measurements were above 98% and higher than those between TPS and measurements. Conclusion: The proposed DVS is applicable for dosimetric verification of HDR-BT, as confirmed through simulated cases for various doses.

Direct tumor visual feedback during free breathing in 0.35T MRgRT.

To present a tumor motion control system during free breathing using direct tumor visual feedback to patients in 0.35 T magnetic resonance-guided radiotherapy (MRgRT). We present direct tumor visualization to patients by projecting real-time cine MR images on an MR-compatible display system inside a 0.35 T MRgRT bore. The direct tumor visualization included anatomical images with a target contour and an auto-segmented gating contour. In addition, a beam-status sign was added for patient guidance. The feasibility was investigated with a six-patient clinical evaluation of the system in terms of tumor motion range and beam-on time. Seven patients without visual guidance were used for comparison. Positions of the tumor and the auto-segmented gating contour from the cine MR images were used in probability analysis to evaluate tumor motion control. In addition, beam-on time was recorded to assess the efficacy of the visual feedback system. The direct tumor visualization system was developed and implemented in our clinic. The target contour extended 3 mm outside of the gating contour for 33.6 ± 24.9% of the time without visual guidance, and 37.2 ± 26.4% of the time with visual guidance. The average maximum motion outside of the gating contour was 14.4 ± 11.1 mm without and 13.0 ± 7.9 mm with visual guidance. Beam-on time as a percentage was 43.9 ± 15.3% without visual guidance, and 48.0 ± 21.2% with visual guidance, but was not significantly different (P = 0.34). We demonstrated the clinical feasibility and potential benefits of presenting direct tumor visual feedback to patients in MRgRT. The visual feedback allows patients to visualize and attempt to minimize tumor motion in free breathing. The proposed system and associated clinical workflow can be easily adapted for any type of MRgRT.

Development of Volumetric Independent Dose Calculation System for Verification of the Treatment Plan in Image-Guided Adaptive Brachytherapy.

Purpose: This study aimed to develop a volumetric independent dose calculation (vIDC) system for verification of the treatment plan in image-guided adaptive brachytherapy (IGABT) and to evaluate the feasibility of the vIDC in clinical practice with simulated cases. Methods: The vIDC is based on the formalism of TG-43. Four simulated cases of cervical cancer were selected to retrospectively evaluate the dose distributions in IGABT. Some reference point doses, such as points A and B and rectal points, were calculated by vIDC using absolute coordinate. The 3D dose volume was also calculated to acquire dose-volume histograms (DVHs) with grid resolutions of 1.0 × 1.0 (G1.0), 2.5 × 2.5 (G2.5), and 0.5 × 0.5 mm2 (G0.5). Dosimetric parameters such as D90% and D2cc doses covering 90% of the high-risk critical target volume (HR-CTV) and 2 cc of the organs at risk (OARs) were obtained from DVHs. D90% also converted to equivalent dose in 2-Gy fractions (EQD2) to produce the same radiobiological effect as external beam radiotherapy. In addition, D90% was obtained in two types with or without the applicator volume to confirm the effect of the applicator itself. Validation of the vIDC was also performed using gamma evaluation by comparison with Monte Carlo simulation. Results: The average percentage difference of point doses was <2.28%. The DVHs for the HR-CTV and OARs showed no significant differences between the vIDC and the treatment planning system (TPS). Without considering the applicator volume, the D90% of the HR-CTV calculated by the vIDC decreases with a decreasing calculated dose-grid size (32.4, 5.65, and -2.20 cGy in G2.5, G1.0, and G0.5, respectively). The overall D90% is higher when considering the applicator volume. The converted D90% by EQD2 ranged from -1.29 to 1.00%. The D2cc of the OARs showed that the averaged dose deviation is <10 cGy regardless of the dose-grid size. Based on gamma analysis, the passing rate was 98.81% for 3%/3-mm criteria. Conclusion: The vIDC was developed as an independent dose verification system for verification of the treatment plan in IGABT. We confirmed that the vIDC is suitable for second-check dose validation of the TPS under various conditions.

Technical Note: Real-time 3D MRI in the presence of motion for MRI-guided radiotherapy: 3D Dynamic keyhole imaging with super-resolution.

PURPOSE: The purpose of this study was to present real-time three-dimensional (3D) magnetic resonance imaging (MRI) in the presence of motion for MRI-guided radiotherapy (MRgRT) using dynamic keyhole imaging for high-temporal acquisition and super-resolution generative (SRG) model for high-spatial reconstruction. METHOD: We propose a unique real-time 3D MRI technique by combining a data sharing technique (3D dynamic keyhole imaging) with a SRG model using cascaded deep learning technique. 3D dynamic keyhole imaging utilizes the data sharing mechanism by combining keyhole central k-space data acquired in real-time with high-spatial, high-temporal resolution prior peripheral k-space data at various motion positions prepared by the SRG model. The efficacy of the 3D dynamic keyhole imaging with super-resolution (SR_dKeyhole) was compared to the ground-truth super-resolution images with the original full k-space data. It was also compared with the zero-filling reconstruction (zero-filling), conventional keyhole reconstruction with low-spatial high-temporal prior data (LR_cKeyhole), and conventional keyhole reconstruction with super-resolution prior data (SR_cKeyhole). RESULTS: High-spatial, high-temporal resolution 3D MRI datasets (1.5 × 1.5 × 6 mm3 ) were generated from low-spatial, high-temporal resolution 3D MRI datasets (6 × 6 × 6 mm3 ) using the cascaded deep learning SRG framework (<100 ms/volume). 3D dynamic keyhole imaging with the SRG model provided high-spatial, high-temporal resolution images (1.5 × 1.5 × 6 mm3 , 455 ms) with the highest similarity to the ground-truth SR images without any noticeable artifacts. Structural similarity indices (SSIM) of the reconstructed 3D MRI to the original SR 3D MRI were 0.65, 0.66, 0.86, and 0.89 for zero-filling, LR_cKeyhole, SR_cKeyhole, and SR_dKeyhole, respectively (1 for identical image). In addition, average value of image relative error (IRE) of the reconstructed 3D MRI to the original SR 3D MRI were 0.169, 0.191, 0.079, and 0.067 for zero-filling, LR_cKeyhole, SR_cKeyhole, and SR_dKeyhole, respectively (0 for identical image). CONCLUSIONS: We demonstrated that high-spatial, high-temporal resolution 3D MRI was feasible by combing 3D dynamic keyhole imaging with a SRG model in terms of image quality and imaging time. The proposed technique can be utilized for real-time 3D MRgRT.

MRI super-resolution reconstruction for MRI-guided adaptive radiotherapy using cascaded deep learning: In the presence of limited training data and unknown translation model.

PURPOSE: Deep learning (DL)-based super-resolution (SR) reconstruction for magnetic resonance imaging (MRI) has recently been receiving attention due to the significant improvement in spatial resolution compared to conventional SR techniques. Challenges hindering the widespread implementation of these approaches remain, however. Low-resolution (LR) MRIs captured in the clinic exhibit complex tissue structures obfuscated by noise that are difficult for a simple DL framework to handle. Moreover, training a robust network for a SR task requires abundant, perfectly matched pairs of LR and high-resolution (HR) images that are often unavailable or difficult to collect. The purpose of this study is to develop a novel SR technique for MRI based on the concept of cascaded DL that allows for the reconstruction of high-quality SR images in the presence of insufficient training data, an unknown translation model, and noise. METHODS: The proposed framework, based on the concept named cascaded deep learning, consists of three components: (a) a denoising autoencoder (DAE) trained using clinical LR noisy MRI scans that have been processed with a nonlocal means filter that generates denoised LR data; (b) a down-sampling network (DSN) trained with a small amount of paired LR/HR data from volunteers that allows for the generation of perfectly paired LR/HR data for the training of a generative model; and (c) the proposed SR generative model (p-SRG) trained with data generated by the DSN that maps from LR inputs to HR outputs. After training, LR clinical images may be fed through the DAE and p-SRG to yield SR reconstructions of the LR input. The application of this framework was explored in two settings: 3D breath-hold MRI axial SR reconstruction from LR axial scans (<3 sec/vol) and in the enhancement of the spatial resolution of LR 4D-MRI acquisitions (0.5 sec/vol). RESULTS: The DSN produces LR scans from HR inputs with a higher fidelity to true, LR clinical scans compared to conventional k-space down-sampling methods based on the metrics of root mean square error (RMSE) and structural similarity index (SSIM). Furthermore, HR outputs generated by the p-SRG exhibit improved scores in the peak signal-to-noise ratio, normalized RMSE, SSIM, and in the blind/reference-less image spatial quality evaluator assessment compared to conventional approaches to MRI SR. CONCLUSIONS: The robust, SR reconstruction method for MRI based on the novel cascaded deep learning framework is an end-to-end method for producing detail-preserving SR reconstructions from noisy, LR clinical MRI scans. Fourfold enhancements in spatial resolution facilitate target delineation and motion management during radiation therapy, enabling precise MRI-guided radiation therapy with 3D LR breath-hold MRI and 4D-MRI in a clinically feasible time frame.

Synthetic CT reconstruction using a deep spatial pyramid convolutional framework for MR-only breast radiotherapy.

PURPOSE: The superior soft-tissue contrast achieved using magnetic resonance imaging (MRI) compared to x-ray computed tomography (CT) has led to the popularization of MRI-guided radiation therapy (MR-IGRT), especially in recent years with the advent of first and second generation MRI-based therapy delivery systems for MR-IGRT. The expanding use of these systems is driving interest in MRI-only RT workflows in which MRI is the sole imaging modality used for treatment planning and dose calculations. To enable such a workflow, synthetic CT (sCT) data must be generated based on a patient's MRI data so that dose calculations may be performed using the electron density information derived from CT images. In this study, we propose a novel deep spatial pyramid convolutional framework for the MRI-to-CT image-to-image translation task and compare its performance to the well established U-Net architecture in a generative adversarial network (GAN) framework. METHODS: Our proposed framework utilizes atrous convolution in a method named atrous spatial pyramid pooling (ASPP) to significantly reduce the total number of parameters required to describe the model while effectively capturing rich, multi-scale structural information in a manner that is not possible in the conventional framework. The proposed framework consists of a generative model composed of stacked encoders and decoders separated by the ASPP module, where atrous convolution is applied at increasing rates in parallel to encode large-scale features. The performance of the proposed method is compared to that of the conventional GAN framework in terms of the time required to train the model and the image quality of the generated sCT as measured by the root mean square error (RMSE), structural similarity index (SSIM), and peak signal-to-noise ratio (PSNR) depending on the size of the training data set. Dose calculations based on sCT data generated using the proposed architecture are also compared to clinical plans to evaluate the dosimetric accuracy of the method. RESULTS: Significant reductions in training time and improvements in image quality are observed at every training data set size when the proposed framework is adopted instead of the conventional framework. Over 1042 test images, values of 17.7 ± 4.3 HU, 0.9995 ± 0.0003, and 71.7 ± 2.3 are observed for the RMSE, SSIM, and PSNR metrics, respectively. Dose distributions calculated based on sCT data generated using the proposed framework demonstrate passing rates equal to or greater than 98% using the 3D gamma index with a 2%/2 mm criterion. CONCLUSIONS: The deep spatial pyramid convolutional framework proposed here demonstrates improved performance compared to the conventional GAN framework that has been applied to the image-to-image translation task of sCT generation. Adopting the method is a first step toward an MRI-only RT workflow that enables widespread clinical applications for MR-IGRT including online adaptive therapy.

Characterization of a prototype rapid kilovoltage x-ray image guidance system designed for a ring shape radiation therapy unit.

PURPOSE: This study aims to characterize the performance of a prototype rapid kilovoltage (kV) x-ray image guidance system onboard the newly released Halcyon 2.0 linear accelerator (Varian Medical Systems, Palo Alto, CA) by use of conventional and innovatively designed testing procedures. METHODS: Basic imaging system performance tests and radiation dose measurements were performed for all eleven kV-cone beam computed tomography (CBCT) imaging protocols available on a preclinical Halcyon 2.0 LINAC. Both conventional CBCT reconstruction using the Feldkamp-Davis-Kress (FDK) algorithm and a novel, advanced iterative reconstruction (iCBCT) available on this platform were evaluated. Standard image quality metrics, including slice thickness accuracy, high-contrast resolution, low-contrast resolution, regional uniformity and noise, and CT Hounsfield unit (HU) number accuracy and linearity were evaluated using a manufacturer-supplied QUART phantom (GmbH, Zorneding, Germany) and an independent image quality phantom (Catphan 500, The Phantom Laboratory, New York, NY). Due to the simplified design of the QUART phantom, we developed surrogate and clinically feasible strategies for measuring slice thickness and high- and low-contrast resolution. Imaging dose delivered by these eleven protocols was measured using a computed tomography dose index phantom and pencil chamber with commonly accepted methods and procedures. A subset of measurements were repeated on a conventional C-arm LINAC (TrueBeam and Trilogy, Varian Medical System) for comparison. Clinical patient images of pelvic and abdominal regions are also presented for qualitative assessment as part of a feasibility study for clinical implementation. RESULTS: Image acquisition time was 17-42 s on the Halcyon system compared with 60 s on the C-arm LINAC systems. The kV imager projection offset, imaging and treatment isocenter coincidence and the couch three-dimensional match movement all achieved less than1 mm mechanical accuracy. All major image quality metrics were within either the national guideline or vendor-recommended tolerances. The designed surrogate approach with the QUART phantom showed a range of 0.24-0.35 cycles/mm for spatial resolution, a contrast-to-noise ratio (CNR) of 2-20 for FDK reconstruction and a tolerance of 0.5 mm for slice thickness. Other metrics derived from the Catphan images obtained on the Halcyon and C-arm LINACs showed comparable values for the FDK reconstruction. The iterative reconstruction tended to reduce noise, as evidenced by a higher CNR ratio. The fast scan pelvis protocols for Halcyon resulted in 50% lower dose compared to the standard scans, and the thorax fast protocol similarly delivered 10% lower dose than the standard thoracic scan. Preliminary patient images indicated that rapid kV CBCT with breath-hold is feasible, with improved imaging quality compared to free-breathing scans. CONCLUSION: Independent and comprehensive characterization of the kV imaging guidance system on the Halcyon 2.0 system demonstrated acceptable image quality for clinical use. The imaging unit onboard the Halcyon meets vendor specifications and satisfies requirements for routine clinical use. The fast kV imaging system enables the potential for volumetric CBCT acquisition during a single breath-hold and the iterative reconstruction tends to reduce the noise therefore has the potential to improve the CNR for normal size patient.

Optimization of treatment planning workflow and tumor coverage during daily adaptive magnetic resonance image guided radiation therapy (MR-IGRT) of pancreatic cancer.

BACKGROUND: To simplify the adaptive treatment planning workflow while achieving the optimal tumor-dose coverage in pancreatic cancer patients undergoing daily adaptive magnetic resonance image guided radiation therapy (MR-IGRT). METHODS: In daily adaptive MR-IGRT, the plan objective function constructed during simulation is used for plan re-optimization throughout the course of treatment. In this study, we have constructed the initial objective functions using two methods for 16 pancreatic cancer patients treated with the ViewRay™ MR-IGRT system: 1) the conventional method that handles the stomach, duodenum, small bowel, and large bowel as separate organs at risk (OARs) and 2) the OAR grouping method. Using OAR grouping, a combined OAR structure that encompasses the portions of these four primary OARs within 3 cm of the planning target volume (PTV) is created. OAR grouping simulation plans were optimized such that the target coverage was comparable to the clinical simulation plan constructed in the conventional manner. In both cases, the initial objective function was then applied to each successive treatment fraction and the plan was re-optimized based on the patient's daily anatomy. OAR grouping plans were compared to conventional plans at each fraction in terms of coverage of the PTV and the optimized PTV (PTV OPT), which is the result of the subtraction of overlapping OAR volumes with an additional margin from the PTV. RESULTS: Plan performance was enhanced across a majority of fractions using OAR grouping. The percentage of the volume of the PTV covered by 95% of the prescribed dose (D95) was improved by an average of 3.87 ± 4.29% while D95 coverage of the PTV OPT increased by 3.98 ± 4.97%. Finally, D100 coverage of the PTV demonstrated an average increase of 6.47 ± 7.16% and a maximum improvement of 20.19%. CONCLUSIONS: In this study, our proposed OAR grouping plans generally outperformed conventional plans, especially when the conventional simulation plan favored or disregarded an OAR through the assignment of distinct weighting parameters relative to the other critical structures. OAR grouping simplifies the MR-IGRT adaptive treatment planning workflow at simulation while demonstrating improved coverage compared to delivered pancreatic cancer treatment plans in daily adaptive radiation therapy.

Variable step size methods for solving simultaneous algebraic reconstruction technique (SART)-type cbct reconstructions.

Compared to analytical reconstruction by Feldkamp-Davis-Kress (FDK), simultaneous algebraic reconstruction technique (SART) offers a higher degree of flexibility in input measurements and often produces superior quality images. Due to the iterative nature of the algorithm, however, SART requires intense computations which have prevented its use in clinical practice. In this paper, we developed a fast-converging SART-type algorithm and showed its clinical feasibility in CBCT reconstructions. Inspired by the quasi-orthogonal nature of the x-ray projections in CBCT, we implement a simple yet much faster algorithm by computing Barzilai and Borwein step size at each iteration. We applied this variable step-size (VS)-SART algorithm to numerical and physical phantoms as well as cancer patients for reconstruction. By connecting the SART algebraic problem to the statistical weighted least squares problem, we enhanced the reconstruction speed significantly (i.e., less number of iterations). We further accelerated the reconstruction speed of algorithms by using the parallel computing power of GPU.

Development of a fast Monte Carlo dose calculation system for online adaptive radiation therapy quality assurance.

Online adaptive radiation therapy (ART) based on real-time magnetic resonance imaging represents a paradigm-changing treatment scheme. However, conventional quality assurance (QA) methods based on phantom measurements are not feasible with the patient on the treatment couch. The purpose of this work is to develop a fast Monte Carlo system for validating online re-optimized tri-60Co IMRT adaptive plans with both high accuracy and speed. The Monte Carlo system is based on dose planning method (DPM) code with further simplification of electron transport and consideration of external magnetic fields. A vendor-provided head model was incorporated into the code. Both GPU acceleration and variance reduction were implemented. Additionally, to facilitate real-time decision support, a C++ GUI was developed for visualizing 3D dose distributions and performing various analyses in an online adaptive setting. A thoroughly validated Monte Carlo code (gPENELOPE) was used to benchmark the new system, named GPU-accelerated DPM with variance reduction (gDPMvr). The comparison using 15 clinical IMRT plans demonstrated that gDPMvr typically runs 43 times faster with only 0.5% loss in accuracy. Moreover, gDPMvr reached 1% local dose uncertainty within 2.3 min on average, and thus is well-suited for ART QA.

Angular under-sampling effect on VMAT dose calculation: An analysis and a solution strategy.

PURPOSE: Most VMAT algorithms compute the dose on discretized apertures with small angular separations for practical reasons. However, machines deliver the VMAT dose with a continuously moving MLC and gantry and a continuously changing dose rate. The computed dose can deviate from the delivered dose, especially if no, or loose, MLC movement constraints are applied for the VMAT optimization. The goal of this paper is to establish a simplified mathematical model to analyze the discrepancy between the VMAT plan calculation dose and the delivered dose and to provide a reasonable solution for clinical implementation. METHODS: A simplified metric is first introduced to describe the discrepancy between doses computed with discretized apertures and a continuous delivery model. The delivery fluences were formed separately for six different leaf movement scenarios. The formula was then rewritten in a more general form. The correlation between discretized and continuous fluence is summarized using this general form. The Fourier analysis for the impacts from three separate factors - dose kernel width, aperture width, aperture distance - to the dose discrepancy is also presented in order to provide insight into the dose discrepancy caused by under-sampling in the frequency domain. Finally, a weighting-based interpolation (WBI) algorithm, which can improve the aperture interpolation efficiency, is proposed. The associated evaluation methods and criteria for the proposed algorithm are also given. RESULTS: The comparisons between the WBI algorithm and the equal angular interpolation (EAI) method suggested that the proposed algorithm has a great advantage with regard to aperture number efficiency. To achieve a 90% gamma passing rate using the dose computed with apertures generated with 0.5° EAI, with the initial optimization apertures as the standard for the comparison, the WBI needs only 66% and 54% of the aperture numbers that the EAI method needs for a 2° and a 4° angular separation of the VMAT optimization, respectively. The results also suggested that the weighted dose error index value, Θ, can be used as a stopping criterion for an interpolation algorithm, e.g., WBI or EAI, or as an indicator for sampling level evaluations. The phantom results indicate that the gamma passing rate decreases with increasing depth, from the phantom surface to the iso center, for the plans computed with under-sampled apertures. No obvious variation trends were observed for the plans computed with well-sampled apertures. CONCLUSIONS: The mathematical analysis suggests that the dose discrepancies due to under-sampling are strongly correlated with the aperture width, the distance between apertures, and the width of the dose kernel. The WBI algorithm proves to be an efficient aperture interpolation strategy and is useful for dose computation of VMAT plans.