Implementation and evaluation of a dynamic contrast-enhanced MR perfusion protocol for glioblastoma using a 0.35 T MRI-Linac system.

PURPOSE: MRI-linear accelerator (MRI-Linac) systems allow for daily tracking of MRI changes during radiotherapy (RT). Since one common MRI-Linac operates at 0.35 T, there are efforts towards developing protocols at that field strength. In this study we demonstrate the implementation of a post-contrast 3DT1-weighted (3D-T1w) and dynamic contrast-enhancement (DCE) protocol to assess glioblastoma response to RT using a 0.35 T MRI-Linac. METHODS AND MATERIALS: The protocol implemented was used to acquire 3D-T1w and DCE data from a flow phantom and two patients with glioblastoma (a responder and a non-responder) who underwent RT on a 0.35 T MRI-Linac. The detection of post-contrast-enhanced volumes was evaluated by comparing the 3DT1w images from the 0.35 T MRI-Linac to images obtained using a 3 T scanner. The DCE data were tested temporally and spatially using data from a flow phantom and patients. Ktrans maps were derived from DCE at three time points (a week before treatment-Pre RT, four weeks through treatment-Mid RT, and three weeks after treatment-Post RT) and were validated with patients' treatment outcomes. RESULTS: The 3D-T1w contrast-enhancement volumes were visually and volumetrically similar between 0.35 T MRI-Linac and 3 T. DCE images showed temporal stability, and associated Ktrans maps were consistent with patient response to treatment. On average, Ktrans values showed a 54 % decrease and 8.6 % increase for a responder and non-responder respectively when Pre RT and Mid RT images were compared. CONCLUSION: Our findings support the feasibility of obtaining post-contrast 3D-T1w and DCE data from patients with glioblastoma using a 0.35 T MRI-Linac system.

Deep learning auto-segmentation on multi-sequence magnetic resonance images for upper abdominal organs.

Introduction: Multi-sequence multi-parameter MRIs are often used to define targets and/or organs at risk (OAR) in radiation therapy (RT) planning. Deep learning has so far focused on developing auto-segmentation models based on a single MRI sequence. The purpose of this work is to develop a multi-sequence deep learning based auto-segmentation (mS-DLAS) based on multi-sequence abdominal MRIs. Materials and methods: Using a previously developed 3DResUnet network, a mS-DLAS model using 4 T1 and T2 weighted MRI acquired during routine RT simulation for 71 cases with abdominal tumors was trained and tested. Strategies including data pre-processing, Z-normalization approach, and data augmentation were employed. Additional 2 sequence specific T1 weighted (T1-M) and T2 weighted (T2-M) models were trained to evaluate performance of sequence-specific DLAS. Performance of all models was quantitatively evaluated using 6 surface and volumetric accuracy metrics. Results: The developed DLAS models were able to generate reasonable contours of 12 upper abdomen organs within 21 seconds for each testing case. The 3D average values of dice similarity coefficient (DSC), mean distance to agreement (MDA mm), 95 percentile Hausdorff distance (HD95% mm), percent volume difference (PVD), surface DSC (sDSC), and relative added path length (rAPL mm/cc) over all organs were 0.87, 1.79, 7.43, -8.95, 0.82, and 12.25, respectively, for mS-DLAS model. Collectively, 71% of the auto-segmented contours by the three models had relatively high quality. Additionally, the obtained mS-DLAS successfully segmented 9 out of 16 MRI sequences that were not used in the model training. Conclusion: We have developed an MRI-based mS-DLAS model for auto-segmenting of upper abdominal organs on MRI. Multi-sequence segmentation is desirable in routine clinical practice of RT for accurate organ and target delineation, particularly for abdominal tumors. Our work will act as a stepping stone for acquiring fast and accurate segmentation on multi-contrast MRI and make way for MR only guided radiation therapy.

Implementation and evaluation of a dynamic contrast enhanced MR perfusion protocol for glioblastoma using a 0.35T MRI-Linac system.

Purpose: MRI-linear accelerator (MRI-Linac) systems allow for daily tracking of MRI changes during radiotherapy (RT). Since one common MRI-Linac operates at 0.35T, there are efforts towards developing protocols at that field strength. In this study we demonstrate the implementation of a post-contrast 3DT1-weighted (3DT1w) and dynamic contrast enhancement (DCE) protocol to assess glioblastoma response to RT using a 0.35T MRI-Linac. Methods and materials: The protocol implemented was used to acquire 3DT1w and DCE data from a flow phantom and two patients with glioblastoma (a responder and a non-responder) who underwent RT on a 0.35T-MRI-Linac. The detection of post-contrast enhanced volumes was evaluated by comparing the 3DT1w images from the 0.35T-MRI-Linac to images obtained using a 3T-standalone scanner. The DCE data were tested temporally and spatially using data from the flow phantom and patients. Ktrans maps were derived from DCE at three time points (a week before treatment-Pre RT, four weeks through treatment-Mid RT, and three weeks after treatment-Post RT) and were validated with patients' treatment outcomes. Results: The 3D-T1 contrast enhancement volumes were visually and volumetrically similar (±0.6-3.6%) between 0.35T MRI-Linac and 3T. DCE images showed temporal stability, and associated Ktrans maps were consistent with patient response to treatment. On average, Ktrans values showed a 54% decrease and 8.6% increase for a responder and non-responder respectively when Pre RT and Mid RT images were compared. Conclusion: Our findings support the feasibility of obtaining post-contrast 3DT1w and DCE data from patients with glioblastoma using a 0.35T MRI-Linac system.

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.