Unsupervised Multimodal Image Registration with Adaptative Gradient Guidance
This work addresses a domain-specific problem in medical imaging for image-guided therapies, offering an incremental improvement over existing methods.
The paper tackled the problem of unsatisfactory organ boundary alignment in unsupervised multimodal image registration by proposing a framework that fuses deformation fields from original images and gradient intensity maps, achieving improved registration accuracy on CT-MRI datasets.
Multimodal image registration (MIR) is a fundamental procedure in many image-guided therapies. Recently, unsupervised learning-based methods have demonstrated promising performance over accuracy and efficiency in deformable image registration. However, the estimated deformation fields of the existing methods fully rely on the to-be-registered image pair. It is difficult for the networks to be aware of the mismatched boundaries, resulting in unsatisfactory organ boundary alignment. In this paper, we propose a novel multimodal registration framework, which leverages the deformation fields estimated from both: (i) the original to-be-registered image pair, (ii) their corresponding gradient intensity maps, and adaptively fuses them with the proposed gated fusion module. With the help of auxiliary gradient-space guidance, the network can concentrate more on the spatial relationship of the organ boundary. Experimental results on two clinically acquired CT-MRI datasets demonstrate the effectiveness of our proposed approach.