Purpose: Radiotherapy (RT) is widely used for cancer management. The standard RT clinical workflow imposes substantial burdens on patients. Multiple image acquisitions for diagnosis and RT planning increase the travel, cost, and wait time before actual RT treatment, which is critical for cancer patients. Diagnostic CT (dCT) is shown to be suitable for RT planning, however different planning CT (pCT) acquisition setup (e.g., table curvature) and motion management procedure (e.g., deep inspiration/active breath control) makes it infeasible. In this study, we present the feasibility of a fully automatic image adaptation method to omit the need for pCT and expedite the treatment course at institutions where on-table adaptive radiotherapy is available.

Methods: We designed a 3D convolutional neural network (3DCNN) to perform the dCT to pCT image adaptation. An automatic table removal algorithm was developed to isolate the patient's body without altering the curvature. The patient body isolated dCT and pCT pair were, then, deformably registered (rCT). The Network was trained on pCTs and the 3D displacement fields (DF). We evaluated the performance of our method using the root averaged sum of squared differences (RASSD), and body contour similarity using Dice similarity coefficient (DSC) and Hausdorff distance (HD).

Results: The generated DFs were applied to dCT to generate the synthetic pCTs (sCT). For test cases, the difference of RASSDs, DSCs, and HD between (rCT-vs-pCT) and (sCT-vs-pCT) were 6 HU, 0.02 and 0.3 mm, respectively.