A deep learning based approach to generate synthetic CT images from multi-modal MRI data

Motivation: Synthetic CT is a useful technique to generate CT images from MR images. Most existing methods exploit only one single MRI modality such as T1-weighted (T1w) images. Goal(s): We aim to develop a synthetic CT method integrating dual-channel T1w+FLAIR input images. Approach: A dual-channel, multi-task deep learning approach based on the 3D Transformer U-net was tested using a public human brain MRI-CT dataset. Its performance was compared to single-modal T1w-based CT synthesis. Results: Our results indicate that dual-modal T1w+FLAIR images can provide richer details, particularly in pixel-level predictions compared to single-modal synthetic CT. The improvement in morphology was moderate. Impact: The proposed framework may be used to integrate two or more MRI modalities to improve the performance of CT image synthesis.