Cross-Modality Image Registration via Generating Aligned Image Using Reference-Augmented Framework

作者：Daniel Kim, Abdullah Shazly, Mohammed A. Al‐masni, Donghyun Kim, Kanghyun Ryu · 发表于：IEEE Journal of Biomedical and Health Informatics · 年份：2025 · DOI：10.1109/jbhi.2025.3642431 · 被引用次数：1 · 研究领域：Generative Adversarial Networks and Image Synthesis、Domain Adaptation and Few-Shot Learning、Advanced Neural Network Applications

Aligning a pair of cross-modality images (e.g., MR-CT, CBCT-CT) is important, yet conventional approaches, including registration or image-to-image (I2I) translation methods often have limitations. To overcome these challenges, we introduce a "Register by Generation (RbG)" framework, a novel 2D deep learning approach designed to generate images that are structurally well-aligned with the fixed image while preserving the detailed intensity and contrast of the moving image, which we refer to as the reference image. Our approach operates in two sequential key stages: first, we employ a novel semi-global reference-augmented image synthesis network incorporating Patch Adaptive Instance Normalization (PAdaIN). This method leverages a down-sampled reference image to guide local adaptive synthesis, generating a more accurately aligned image with a reduced risk of hallucinations. In the second stage, we introduce a detailed refining reference-augmented network featuring a Deformation-Aware Cross-Attention (DACA) block, which aims to recover finer details and textures that may be missing from the initial stage. This unique component (DACA block) enables the transfer of corresponding relevant features from the reference image, effectively performing a "copy-and-paste" operation within the latent feature space. Additionally, we propose a novel combination of loss functions that enables self-supervised training on misaligned datasets, eliminating the need for pre-aligned data. We rigorous...