Myocardial infarction detection framework using cardiac motion estimation with deformable registration field.
Authors
Affiliations (5)
Affiliations (5)
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan.
- AIP, RIKEN, Tokyo, Japan.
- Research Center for Advanced Science and Technology, The University of Tokyo, Tokyo, Japan. [email protected].
- AIP, RIKEN, Tokyo, Japan. [email protected].
- Sakurabashi Watanabe Advanced Healthcare Hospital, Osaka, Japan.
Abstract
Cardiovascular diseases are the leading cause of mortality worldwide. Among them, myocardial infarction (MI), which is characterized by abnormal myocardial motion, remains a critically important condition to detect. Accurate diagnosis of MI requires detailed motion analysis; however, motion-based assessment is time-consuming and imposes a considerable burden on physicians. Moreover, most previous studies have relied on local motion descriptors or limited shape features, resulting in suboptimal detection performance. To address these limitations, we propose a deep learning framework, termed MambaMorph-Motion Infarction Detector (MMMID), for myocardial infarction detection from cardiac CT images. The proposed pipeline integrates semantic segmentation, sequential deformable registration using MambaMorph, and a DenseNet-based classification module to effectively leverage rich spatiotemporal myocardial motion information across the cardiac cycle. The proposed method enables accurate tracking of myocardial motion aligned with anatomical structures by associating time-resolved motion fields with corresponding CT volumes. MMMID achieved an accuracy of 91.92% and an AUC of 0.9335 on an in-house cardiac CT dataset. Compared with the CT-only configuration, the integration of sequential motion fields improved recall, suggesting that motion information provides complementary diagnostic cues for myocardial infarction detection rather than replacing anatomical CT information. The proposed framework enables myocardial infarction detection by jointly leveraging cardiac anatomy and estimated myocardial motion. Although CT appearance remained a strong source of discriminative information, sequential motion fields contributed complementary functional information, particularly for improving sensitivity to infarction cases. MMMID may serve as a supportive tool for automated cardiac CT analysis and could help reduce the burden of motion-based assessment in clinical workflows.