Noninvasive Detection of Coronary Microvascular Dysfunction.
Authors
Affiliations (5)
Affiliations (5)
- Department of Neurology, Zhoushan Hospital, Wenzhou Medical University, Zhoushan, China.
- School of Nursing and Health Studies, Hong Kong Metropolitan University, Hong Kong, China.
- Department of Cardiology, Sabah Al Ahmed Cardiac Centre, Al Amiri Hospital, Kuwait City, Kuwait.
- Department of Radiology, School of Medicine, Second Affiliated Hospital of Zhejiang University, Hangzhou, China.
- Centre for Technology Transfer, Universidad Santa Paula, Curridabat, San José, Costa Rica.
Abstract
Coronary Microvascular Dysfunction (CMVD) can lead to myocardial ischemia and increase the risk of adverse cardiovascular events. In clinical practice, early and accurate diagnosis of CMVD is essential for effective intervention and management. However, because CMVD and obstructive coronary artery disease share similar clinical presentations, distinguishing CMVD remains challenging. We conducted a narrative review by searching PubMed, Web of Science, and Google Scholar. Two reviewers independently screened studies and extracted technical and clinical details from the included articles. This review elaborates on noninvasive tools for CMVD evaluation. Positron Emission Tomography (PET) quantifies Myocardial Blood Flow (MBF). Cardiovascular Magnetic Resonance (CMR) provides a quantitative myocardial perfusion reserve index. Myocardial computed tomography perfusion, in static or dynamic modes, enables concurrent anatomic and functional assessment. Echocardiography includes transthoracic Doppler-derived coronary flow reserve and myocardial contrast echocardiography for bedside perfusion evaluation. SPECT supports MBF quantification in selected settings. CMVD pathobiology is reflected in circulating biomarkers, with microRNAs showing promise. Artificial Intelligence (AI) and computational fluid dynamics can further assist in noninvasive CMVD diagnosis. Each imaging modality has distinct strengths and limitations. Blood biomarkers and computational models are promising for scalable clinical use, but require confirmation in prospective studies. Cardiovascular imaging and circulating biomarkers reveal CMVD-related changes in anatomy, hemodynamics, and metabolism, while computational models can improve diagnostic precision. Future research should include large, multicenter, prospective studies, such as trials comparing the diagnostic accuracy of AI-enhanced CMR with the invasive gold standard, the index of microvascular resistance, to validate these methods and establish integrated, cost-effective diagnostic pathways for early CMVD detection across diverse cohorts.