Point Cloud Registration for Fusion between SPECT MPI and CTA Images

2026-04-27

AI Summary

This study addresses the challenges of inaccurate cross-modality registration between SPECT myocardial perfusion imaging (MPI) and coronary computed tomography angiography (CTA), as well as the reliance on manual landmarking, which hinders precise localization of myocardial ischemia and functional assessment of coronary lesions. To overcome these limitations, the authors propose a general, high-precision automatic fusion framework. The method employs a U-Net architecture to segment ventricles and automatically extract anatomical landmarks, followed by scale-space preprocessing and landmark-driven coarse registration. Multiple point-cloud fine-registration algorithms—including ICP, CPD, FFD, and BCPD++—are systematically evaluated. In a clinical cohort of 60 patients, BCPD++ achieves the best performance with a mean registration error of 1.7 mm, enabling voxel-level alignment of perfusion and anatomy while preserving sub-millimeter coronary details from CTA, thereby significantly improving the accuracy of ischemia localization and lesion-specific functional evaluation.

Technology Category

• Computer Vision: Multi-modal Vision
• Intelligent Robots: Multimodal Perception & Sensor Fusion
• Machine Learning: Calibration & Uncertainty Quantification

Application Category

• User Modeling, Personalization and Recommendation: Federated recommendation systems and personalization
• Systems and Infrastructure for Web, Mobile and WoT: Web performance, measurement, and characterization
• Security and Privacy: Large-scale security measurements

Abstract

Clinical fusion of Single Photon Emission Computed Tomography Myocardial Perfusion Imaging (SPECT MPI) and Computed Tomography Angiography (CTA) remains limited by cross-modality misregistration and reliance on manual landmarks, which can hinder accurate ischemia localization and lesion-level functional assessment. To address this issue, we propose a registration and fusion framework for SPECT MPI and CTA that integrates functional and structural information for comprehensive cardiac evaluation. The proposed pipeline performs U-Net-based segmentation on both modalities. On SPECT MPI, only the left ventricle (LV) is extracted, and anatomical landmarks are automatically derived from characteristic LV structures. On CTA, both ventricles are segmented, and their spatial relationship is used to automatically define landmarks at the interventricular septal junction. Scale-space consistency preprocessing and landmark-driven coarse registration are applied to mitigate initial misalignment. Based on this initialization, multiple fine registration methods are evaluated on LV epicardial surface point clouds, including ICP, SICP, CPD, CluReg, FFD, and BCPD-plus-plus. The resulting transformations are then propagated to voxel-level resampling for high-precision SPECT-CTA fusion. In a retrospective cohort of 60 patients, the proposed framework preserved sub-millimeter coronary detail from CTA while accurately overlaying quantitative SPECT perfusion. Among the evaluated methods, BCPD-plus-plus achieved the highest accuracy with a mean point cloud distance of 1.7 mm. By combining robust initialization, comparative fine registration, and voxel-level fusion, the proposed approach provides a practical solution for myocardial ischemia localization and functional evaluation of coronary lesions, while remaining independent of any specific fine registration algorithm.

Problem

• Point Cloud Registration
• SPECT MPI
• CTA
• Cross-modality Fusion
• Myocardial Ischemia Localization

Innovation

• point cloud registration
• SPECT-CTA fusion
• landmark-free initialization
• BCPD-plus-plus
• multi-modality cardiac imaging