Dosimetric Evaluation of a Data-driven DL Approach to sCT Reconstruction for Abdominal MRI-only ART
Abdominal synthetic CT reconstruction with intensity projection prior for MRI-only adaptive radiotherapy
We propose a paired data-driven deep learning (dl) approach to sct reconstruction for accurate dose calculations in abdominal adaptive radiotherapy (art) enabled by the creation of a clinically unavailable training data set with well-matching representations of intestinal gas.We utilize a paired data dl approach enabled by the intensity projectionprior, in which well-matching training pairs are created by propagating airfrom mri to corresponding ct scans.We present the preliminary dosimetric evaluation of our novel approach to sctreconstruction that is well suited to handling intestinal gas in abdominalmri-only art.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.
