Lesion-robust longitudinal brain CT registration via MR-guided image translation

Abstract

Longitudinal brain computed tomography (CT) is routinely used to monitor disease progression in patients with focal lesions and other neuropathologies. Accurate registration across time points is essential for reliable quantitative analysis. However, this task remains challenging due to the intrinsically low soft-tissue contrast of CT, particularly between gray and white matter, and the presence of space-occupying lesions, which induce substantial anatomical deformation and violate the intensity-consistency assumptions underlying conventional registration methods. To address these challenges, we propose a lesion-robust framework for longitudinal brain CT registration based on MRguided image translation. Specifically, an Image-to-Image Schrödinger Bridge (I2SB) network is employed to translate CT images into pseudo-MR images with enhanced anatomical contrast. We then perform joint deformable registration on both pseudo-MR and original CT images, where pseudo-MR provides structurally informative guidance while CT enforces modality-consistent data fidelity. This joint formulation enables more reliable correspondence estimation in the presence of lesion-induced deformation and intensity inconsistencies. The resulting deformation field is subsequently applied to the original CT images to achieve accurate longitudinal alignment. We evaluate the proposed method on an in-house longitudinal CT dataset of patients with intracerebral hemorrhage. Experimental results demonstrate that our approach consistently outperforms conventional intensity-based registration methods as well as existing image-translation–assisted strategies in both quantitative metrics and visual alignment quality. By jointly leveraging contrast-enhanced structural cues and modality-consistent constraints, the proposed framework provides a robust solution for longitudinal brain CT registration in the presence of large lesions.