Real-Time Internal Motion Monitoring during Radiation Therapy Using Cine MRI and Deep Learning

Abstract

<h3>Purpose/Objective(s)</h3> Fast and accurate internal motion estimation is fundamental to enabling real-time MRI-guided radiation therapy (RT). We hypothesized that a deep learning (DL) model for fast deformable image registration using 2D sagittal cine MRI is feasible. Herein, we report performance of a DL-based image registration model as compared to conventional image registration methods. <h3>Materials/Methods</h3> Our DL model uses a pair of cine MRI images acquired using an MR-LINAC as inputs and outputs a motion vector field (MVF) which is applied to align the images. Cine delivery data for patients (pts) undergoing treatment for abdominal and thoracic tumors were retrospectively analyzed. Cine MRI scans were sampled, constructing a large set of image registration pairs capturing pt respiratory motion to train the DL model. Data were partitioned by pt using five-fold cross validation. Model outputs (transformed images and MVFs) on test set images were saved for comparison with three conventional registration methods (affine, b-spline, and demons). Performance was quantified via average registration error and computation time. Paired t-tests determined whether differences in performance were statistically significant. <h3>Results</h3> We analyzed >21 hours of cine MRI (>629,000 frames) acquired during 86 treatment fractions from 21 pts. In a test set of 10,320 image registration pairs, DL registration outperformed conventional methods in both registration error (affine, b-spline, demons vs. DL; RMSE: 0.067, 0.040, 0.036 vs. 0.032; paired t-test demons vs DL: t(20) = 4.2, p<0.001) and computation time per frame (51 ms, 1150 ms, 4583 ms vs. 8 ms). <h3>Conclusion</h3> DL-based image registration can leverage large-scale cine MRI datasets to learn to predict pt internal motion fields using a single reference and a single moving frame as inputs. Low registration error and fast computation speed indicate that DL-based registration is a promising solution for real-time deformable motion tracking in RT. Further inter-institutional validation is underway.