3D Fusion between Fluoroscopy Angiograms and SPECT Myocardial Perfusion Images to Guide Percutaneous Coronary Intervention
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Abstract (arXiv)
Background. Percutaneous coronary intervention(PCI) in stable coronary artery disease(CAD) is commonly triggered by abnormal myocardial perfusion imaging(MPI). However, due to the possibilities of multivessel disease and variability of coronary artery perfusion distribution, opportunity exists to better align anatomic stenosis with perfusion abnormalities to improve revascularization decisions. This study aims to develop a 3D multi-modality fusion approach to assist decision-making for PCI. Methods. Coronary arteries from fluoroscopic angiography(FA) were reconstructed into 3D artery anatomy. Left ventricular(LV) epicardial surface was extracted from SPECT. The 3D artery anatomy was non-rigidly fused with the LV epicardial surface. The accuracy of the 3D fusion was evaluated via both computer simulation and real patient data. For technical validation, simulated FA and MPI were integrated and then compared with the ground truth from a digital phantom. For clinical validation, FA and SPECT images were integrated and then compared with the ground truth from CT angiograms. Results. In the technical evaluation, the distance-based mismatch error between simulated fluoroscopy and phantom arteries is 1.86(SD:1.43)mm for left coronary arteries(LCA) and 2.21(SD:2.50)mm for right coronary arteries(RCA). In the clinical validation, the distance-based mismatch errors between the fluoroscopy and CT arteries were 3.84(SD:3.15)mm for LCA and 5.55(SD:3.64)mm for RCA. The presence of the corresponding fluoroscopy and CT arteries in the AHA 17-segment model agreed well with a Kappa value of 0.91(95% CI: 0.89-0.93) for LCA and 0.80(CI: 0.67-0.92) for RCA. Conclusions. Our fusion approach is technically accurate to assist PCI decision-making and is clinically feasible to be used in the catheterization laboratory. There is an opportunity to improve the decision-making and outcomes of PCI in stable CAD.
Key facts. Cited by 3 papers in the map (data through 14 Oct 2026). First posted on arXiv 12 Jun 2020.
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Citing papers per year: 2022: 1; 2023: 0; 2024: 1; 2025: 1.
3 papers.
- 2022Automatic extraction of coronary arteries using deep learning in invasive coronary angiogramscited by 2
- 2024Point cloud-based registration and image fusion between cardiac SPECT MPI and CTAcited by 0
- 2025FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiographycited by 0
- 2025FAD-Net: Frequency-Domain Attention-Guided Diffusion Network for Coronary Artery Segmentation using Invasive Coronary Angiography13 Jun 2025
- 2024Point cloud-based registration and image fusion between cardiac SPECT MPI and CTA10 Feb 2024
- 2022Automatic extraction of coronary arteries using deep learning in invasive coronary angiograms24 Jun 2022