Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA
<|reference_start|>Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA: A method was proposed for the point cloud-based registration and image fusion between cardiac single photon emission computed tomography (SPECT) myocardial perfusion images (MPI) and cardiac computed tomography angiograms (CTA). Firstly, the left ventricle (LV) epicardial regions (LVERs) in SPECT and CTA images were segmented by using different U-Net neural networks trained to generate the point clouds of the LV epicardial contours (LVECs). Secondly, according to the characteristics of cardiac anatomy, the special points of anterior and posterior interventricular grooves (APIGs) were manually marked in both SPECT and CTA image volumes. Thirdly, we developed an in-house program for coarsely registering the special points of APIGs to ensure a correct cardiac orientation alignment between SPECT and CTA images. Fourthly, we employed ICP, SICP or CPD algorithm to achieve a fine registration for the point clouds (together with the special points of APIGs) of the LV epicardial surfaces (LVERs) in SPECT and CTA images. Finally, the image fusion between SPECT and CTA was realized after the fine registration. The experimental results showed that the cardiac orientation was aligned well and the mean distance error of the optimal registration method (CPD with affine transform) was consistently less than 3 mm. The proposed method could effectively fuse the structures from cardiac CTA and SPECT functional images, and demonstrated a potential in assisting in accurate diagnosis of cardiac diseases by combining complementary advantages of the two imaging modalities.<|reference_end|>
@article{tang2024point,
    title={Point cloud-based registration and image fusion between cardiac SPECT
  MPI and CTA},
    author={Shaojie Tang, Penpen Miao, Xingyu Gao, Yu Zhong, Dantong Zhu, Haixing
  Wen, Zhihui Xu, Qiuyue Wei, Hongping Yao, Xin Huang, Rui Gao, Chen Zhao,
  Weihua Zhou},
    journal={arXiv preprint arXiv:2402.06841},
    year={2024},
    archivePrefix={arXiv},
    eprint={2402.06841},
    primaryClass={eess.IV cs.CV}
}