DHR-Net: Dynamic Harmonized registration network for multimodal medical images.
Authors
Affiliations (6)
Affiliations (6)
- Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, 150080, Heilongjiang, China; Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, Guangdong, China. Electronic address: [email protected].
- Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, 150080, Heilongjiang, China. Electronic address: [email protected].
- School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China. Electronic address: [email protected].
- Heilongjiang Provincial Key Laboratory of Complex Intelligent System and Integration, School of Automation, Harbin University of Science and Technology, Harbin, 150080, Heilongjiang, China; School of Computer Science and Technology, Harbin University of Science and Technology, Harbin, 150080, China. Electronic address: [email protected].
- Institute for Brain Research and Rehabilitation, South China Normal University, Guangzhou, 510631, Guangdong, China. Electronic address: [email protected].
- Department of Radiation Oncology, Sun Yat-sen University Cancer Center, State Key Laboratory of Oncology in South China, Collaborative Innovation Center for Cancer Medicine, Guangdong Key Laboratory of Nasopharyngeal Carcinoma Diagnosis and Therapy, Guangzhou, 510060, Guangdong, China. Electronic address: [email protected].
Abstract
Although deep learning has driven remarkable advancements in medical image registration, deep neural network-based non-rigid deformation field generation methods demonstrate high accuracy in single-modality scenarios. However, multi-modal medical image registration still faces critical challenges. To address the issues of insufficient anatomical consistency and unstable deformation field optimization in cross-modal registration tasks among existing methods, this paper proposes an end-to-end medical image registration method based on a Dynamic Harmonized Registration framework (DHR-Net). DHR-Net employs a cascaded two-stage architecture, comprising a translation network and a registration network that operate in sequential processing phases. Furthermore, we propose a loss function based on the Noise Contrastive Estimation framework, which enhances anatomical consistency in cross-modal translation by maximizing mutual information between input and transformed image patches. This loss function incorporates a dynamic temperature adjustment mechanism that progressively optimizes feature contrast constraints during training to improve high-frequency detail preservation, thereby better constraining the topological structure of target images. Experiments conducted on the M&M Heart Dataset demonstrate that DHR-Net outperforms existing methods in registration accuracy, deformation field smoothness, and cross-modal robustness. The framework significantly enhances the registration quality of cardiac images while demonstrating exceptional performance in preserving anatomical structures, exhibiting promising potential for clinical applications.