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.

翻译：单光子发射计算机断层扫描心肌灌注成像（SPECT MPI）与计算机断层血管造影（CTA）的临床融合仍受限于跨模态配准误差及对人工标记点的依赖，这阻碍了心肌缺血的精准定位与病灶水平的功能评估。为解决该问题，本文提出一种融合功能和结构信息的SPECT MPI与CTA配准融合框架，以实现全面心脏评估。该流程对两种模态实施基于U-Net的分割：在SPECT MPI中仅提取左心室（LV），并依据LV特征结构自动推导解剖标记点；在CTA中对双心室进行分割，利用其空间关系在室间隔交界处自动定义标记点。通过尺度空间一致性预处理与标记点驱动的粗配准减轻初始偏移。基于此初始配准，在左心室心外膜表面点云上评估多种精细配准方法，包括ICP、SICP、CPD、CluReg、FFD及BCPD-plus-plus。所得变换矩阵进一步传播至体素级重采样，实现高精度SPECT-CTA融合。在60例患者的回顾性队列中，本框架保留了CTA的亚毫米级冠状动脉细节，同时精确叠加定量SPECT灌注信息。各方法中，BCPD-plus-plus在平均点云距离1.7毫米下取得最高精度。通过结合稳健初始化、对比性精细配准与体素级融合，本方法为心肌缺血定位及冠状动脉病变功能评估提供了独立于特定精细配准算法的实用方案。