Multicentre, deep learning, synthetic-CT generation for ano-rectal MR-only radiotherapy treatment planning

Highlights: • Accurate Synthetic-CT (sCT) generation for anorectal cancers. • Deep learning sCT generation with varied input data. • T2-SPACE MRI sequence use for generalisable pelvic synthetic-CT. Comprehensive dosimetric analysis is required prior to the clinical implementation of pelvic MR-only sites, other than prostate, due to the limited number of site specific synthetic-CT (sCT) dosimetric assessments in the literature. This study aims to provide a comprehensive assessment of a deep learning-based, conditional generative adversarial network (cGAN) model for a large ano-rectal cancer cohort. The following challenges were investigated; T2-SPACE MR sequences, patient data from multiple centres and the impact of sex and cancer site on sCT quality.