Real Time Volumetric MRI Using a Physics-Aware Deep Neural Network for MR-Guided Radiation Therapy

Abstract

To investigate the feasibility of real time volumetric MRI using a physics-aware deep neural network, in support of MRI-based 3D motion tracking for MR-guided adaptive radiotherapy.A deep neural network was proposed to generate volumetric MRI from orthogonal Cine MRI, which can be acquired on MR-Linac systems real time. The network consisted of a 2D generation module where oblique slices were synthesized from two orthogonal MRI slices and a 3D refinement module to generate final high quality volumetric images. A physics module that utilized prior knowledge of imaging physics was used to bridge 2D and 3D domains. The proposed method was evaluated on four patients with intrahepatic carcinoma. Each patient received two 4D MRI examinations that were more than 1 month apart, where one examination was used for network training and another for evaluation. Volumetric images of 21 motion phases were reconstructed via retrospective binning and served as the ground truth. Orthogonal Cine MRI were sampled from volumetric MRI and served as network inputs. To evaluate tracking accuracy using the network predicted volumetric MRI, a reference MRI with gross tumor volume (GTV) contours was deformed to match each of the 21 motion phases. The deformed GTV centroid positions and contours were compared between ground truth and network prediction.Across all the 4 patients evaluated, the averaged distance between predicted and ground truth GTV centroids was less than 1 mm in all 3 directions (anterior-posterior, inferior-superior and lateral). The averaged 95-percentile Hausdorff distances between the predicted and ground truth GTV contours was 4.2 mm, which was similar to the cross-plane imaging resolution (4 mm).Physics-aware deep neural network enabled real time volumetric MRI with sufficient accuracy to support 3D motion tracking. The proposed technique is robust to longitudinal patient position and configuration changes, and has the potential of reducing treatment margins and improving treatment delivery precision on a MR-Linac system.