Abstract
Introduction.Real-time 2D-kV-triggered images used to evaluate intra-fraction motion during abdominal radiotherapy only provides 2D information with poor soft-tissue contrast. The main goal of this research is to evaluate a novel method that generates synthetic 3D-MRI from single 2D-kV images for online motion monitoring in abdominal radiotherapy.Methods.Deformable image registration (DIR) is performed between one 4D-MRI reference phase and all other phases, and principal-component-analysis (PCA) is implemented on their respective deformation vectors. By sampling 1000 times the PCA eigenvalues and applying the new deformations over a reference CT, 1000 digital reconstructed radiographs (DRRs) were generated to train a convolutional neural network to predict their respective eigenvalues. The method was implemented and tested using a digital phantom (XCAT) and an MRI-compatible phantom (ZEUS) with five DRR angles (0°, 45°, 90°, 135°, 180°). Seven motion scenarios were tested. For model performance, mean absolute error (MAE) and root mean square error (RMSE) were reported. Image quality was evaluated with structure similarity index (SSIM) and normalized RMSE (nRMSE), and target-volume variations were evaluated with volumetric dice coefficient (VDC) and Hausdorff-distance (HD).Results.The model performance across the evaluated angles were MAE((XCAT, ZEUS))= (0.053 ± 0.003, 0.094 ± 0.003), and RMSE((XCAT, ZEUS))= (0.054 ± 0.007, 0.103 ± 0.002). Similarly, SSIM((XCAT, ZEUS))= (0.994 ± 0.001, 0.96 ± 0.02), and nRMSE((XCAT, ZEUS))= (0.13 ± 0.01, 0.17 ± 0.03). For all motion scenarios for XCAT and ZEUS, SSIM were 0.98 ± 0.01 and 0.84 ± 0.02, nRMSE were 0.14 ± 0.01 and 0.27 ± 0.02, VDC were 0.98 ± 0.01 and 0.90 ± 0.01, and HD were 0.24 ± 0.02 mm and 2.3 ± 0.8 mm, respectively, averaged across all angles. Finally, SSIM, nRMSE, VDC and HU values for ZEUS using the(deformed)images as ground truth, presented an improvement of 13%, 28%, 4%, and 76%, respectively.Conclusions. Results from a digital and physical phantom demonstrate a novel approach to generate real-time 3D synthetic MRI from onboard kV images on a conventional LINAC for intra-fraction monitoring in abdominal radiotherapy.