School of Computing Sciences and Computer Engineering, University of Southern Mississippi, Hattiesburg, MS, 39406, USA.
Department of Cardiology, Ochsner Medical Center, New Orleans, LA, 70121, USA.
J Nucl Cardiol. 2022 Aug;29(4):1870-1884. doi: 10.1007/s12350-021-02574-1. Epub 2021 Apr 6.
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, serial stenoses and variability of coronary artery perfusion distribution, an opportunity exists to better align anatomic stenosis with perfusion abnormalities to improve revascularization decisions. This study aims to develop a multi-modality fusion approach to assist decision-making for PCI.
Coronary arteries from fluoroscopic angiography (FA) were reconstructed into 3D artery anatomy. Left ventricular (LV) epicardial surface was extracted from SPECT. The artery anatomy and epicardial surface were non-rigidly fused. The accuracy of the 3D fusion was evaluated via both computer simulation and real patient data. Simulated FA and MPI were integrated and then compared with the ground truth from a digital phantom. The distance-based mismatch errors between simulated fluoroscopy and phantom arteries were 1.86 ± 1.43 mm for left coronary arteries (LCA) and 2.21 ± 2.50 mm for right coronary arteries (RCA). FA and SPECT images in 30 patients were integrated and then compared with the ground truth from CT angiograms. The distance-based mismatch errors between the fluoroscopy and CT arteries were 3.84 ± 3.15 mm for LCA and 5.55 ± 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 (CI 0.89-0.93) for LCA and a Kappa value of 0.80 (CI 0.67-0.92) for RCA.
Our fusion approach is technically accurate to assist PCI decision-making and is clinically feasible to be used in the catheterization laboratory. Future studies are necessary to determine if fusion improves PCI-related outcomes.
经皮冠状动脉介入治疗(PCI)在稳定型冠状动脉疾病(CAD)中通常是由异常心肌灌注成像(MPI)触发的。然而,由于多血管病变、连续狭窄和冠状动脉灌注分布的可变性,存在一种机会可以更好地将解剖学狭窄与灌注异常相匹配,以改善血运重建决策。本研究旨在开发一种多模态融合方法来辅助 PCI 的决策。
从荧光透视血管造影(FA)重建冠状动脉的 3D 动脉解剖结构。从 SPECT 提取左心室(LV)心外膜表面。将动脉解剖结构和心外膜表面进行非刚性融合。通过计算机模拟和真实患者数据评估 3D 融合的准确性。将模拟的 FA 和 MPI 进行整合,然后与数字体模的真实数据进行比较。模拟荧光透视与体模动脉之间的基于距离的不匹配误差为左冠状动脉(LCA)的 1.86 ± 1.43 毫米和右冠状动脉(RCA)的 2.21 ± 2.50 毫米。将 30 例患者的 FA 和 SPECT 图像进行整合,然后与 CT 血管造影的真实数据进行比较。荧光透视与 CT 动脉之间的基于距离的不匹配误差为 LCA 的 3.84 ± 3.15 毫米和 RCA 的 5.55 ± 3.64 毫米。AHA-17 节段模型中存在相应的荧光透视和 CT 动脉,与 LCA 的 Kappa 值为 0.91(CI 0.89-0.93)和 RCA 的 Kappa 值为 0.80(CI 0.67-0.92)高度一致。
我们的融合方法在技术上是准确的,可以辅助 PCI 决策,并且在临床上是可行的,可以在导管室中使用。未来的研究需要确定融合是否可以改善 PCI 相关的结果。