ISSN: 0970-938X (Print) | 0976-1683 (Electronic)

Biomedical Research

An International Journal of Medical Sciences


Clinical diagnostic value of static CT myocardial perfusion for ischemia with non-obstructive coronary artery disease.

Background: Currently coronary anomalies mainly rely on anatomical examination (coronary angiography, CAG and coronary CTA, CCTA), but it cannot observe arteries with an inner diameter of <300 um, such as, Ischemia with Non-Obstructive Coronary Artery Disease (INOCA), resulting in insufficient diagnosis and treatment. So, we used static CT Myocardial Perfusion Imaging (sCTMPI) and CAG to evaluate the distribution and diagnosis of ischemic lesions of INOCA patients and test their consistency.

Methods: 35patients with INOCA received sCTMPI, CCTA and CAG. Taking the Right Coronary Artery (RCA), Left Anterior Descending (LAD), and Left Circumflex Artery (LCX) for the target vessel, analyzed all patient images with target blood vessels and vascular perfusion areas in sCTMPI, CAG and CCTA. The iodine content and CT value parameters of the resting myocardial perfusion defect area were determined. Statistical analysis was performed for the CCTA abnormality is determined according to the ischemic area of the coronary artery blood supply and compared with the CAG.

Results: There were 172 segments of myocardial ischemia in 35 patients with INOCA received sCTMP. CCTA assessment of stenosis changes were 73 branches in LAD, LCX and RCA, where lesions were found by sCTMPI. while CAG 77 branches, the Kappa value between CCTA and CAG was 0.478, 0.943, 0.935 respectively.

Conclusion: The relationship between the degree of coronary artery stenosis and myocardial ischemia was nonlinear. Patients with suspected myocardial ischemia-related symptoms, even if the degree of CCTA stenosis is less than 50%, should undergo further sCTMPI to exclude the possibility of INOCA.

Author(s): Shihao Yan, Deyue Yan, Xiaoshuang Che, Chenglin Xu, Baijie Li, Qigan Xia, Xiaoyan Wang, Yubo Tian, Hairong Yu, Xiaomei Luan, Peiji Song, Huating Wang
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