Improving Pelvic MR-CT Image Alignment with Self-Supervised Reference-Augmented Pseudo-CT Generation Framework

RegistFormer our novel reference-augmented image synthesis framework generates aligned pseudo-CT images (with respect to MR) from misaligned MR and CT pairs. RegistFormer addresses the limitations of intensity-based registration methods which often fail due to dissimilar image features and complex deformation fields. Unlike conventional image-to-image (I2I) translation methods our method uses a misaligned CT scan as an auxiliary input to guide the synthesis task through the Deformation-Aware Cross-Attention (DACA) mechanism. DACA integrates the deformation field from a registration method to aggregate spatially matched features from the misaligned CT into MR spatial coordinates. Additionally we propose a novel combination of loss functions for training with datasets of misaligned MR-CT pairs in a self-supervised manner eliminating the need for pre-aligned training data. Experiments were conducted with the synthRAD2023 MR-CT pelvis pair dataset. RegistFormer outperforms past state-of-the-art methods including I2I registration and hybrid (registration + I2I) across metrics evaluating both structure alignment and distribution similarity. Moreover RegistFormer demonstrates superior performance in zero-shot segmentation downstream tasks highlighting its clinical value. Source code: https://github.com/danny4159/RegistFormer