ESTRO 2022 - Abstract Book

S691

Abstract book

ESTRO 2022

We propose a novel cycleGANs-based CBCT-to-CT synthesis pipeline where only global residuals are learned and predicted. This approach allows one to refine the raw CBCTs by removing the unwanted artefacts, rather than generating new images “inspired” by the input. We showed this using UNet and ResNet architectures and then investigated different configuration options: with/without geometrical loss, and a smart data selection based on the common (abdominal) field-of-view across the dataset, acting as a weakly paired data approach. evaluated. We compared our proposed framework to vanilla cycleGANs (without smart data selection, geometrical loss or residual learning, with UNet and ResNet generators). Due to the lack of simultaneously acquired CT/CBCT scans, the synCTs were evaluated against two complementary ground-truths (GTs): the raw CBCT and a virtual CT (vCT). The vCT consisted of the CT deformably registered to the CBCTs using NiftyReg. In vCTs the original gas regions were replaced with water intensity and then gas regions were added from CBCTs. Three global image similarity metrics were calculated between synCTs and GTs: sum of squared differences (SSD), normalised cross-correlation (NCC) and root mean square error (RMSE). Metrics were calculated within the common field-of-view, the same as was used for smart data selection. Results A summary of all metrics calculated is shown in Tab.1, showing improvements in the agreement with vCT and CBCT after adding proposed extensions for both tested generator architectures. The worst scores were observed for vanilla cycleGANs and the best for the configurations incorporating all the proposed extensions. Our proposed cycleGAN workflow improved the anatomical consistency between source and synthetic images compared to the vanilla counterpart, Fig.1. A total of 10 configurations were trained (150 epochs) and