Digital Subtraction Angiography Contrast Material Transport as a Direct Assessment for Blood Perfusion of Middle Cerebral Artery Stenosis.

Abstract

Digital subtraction angiography (DSA) is a fluoroscopic technique used extensively in interventional radiology for visualizing blood vessels. It has also been used to evaluate blood perfusion. However, the perfusion obtained in previous techniques was extracted from signal intensity rather than by the transport of contrast material (CM) through blood flow. The main aim of this study is to evaluate the morphological effects on the hemodynamics and the CM concentration in the middle cerebral artery (MCA) stenosis. We proposed a quantitative parameter, i.e., contrast material remaining time (CMRT), to describe the variation in the transport of CM over time. Computational fluid dynamics simulations were performed on both reconstructive synthetic and patient-derived models. In the synthetic models, we evaluated the variation of flow patterns and the transport of CM with different degrees of stenosis and the location of the lesion. It was found that an increase in the degree of stenosis (from 30 to 80%) resulted in a significant increase in CMRT at the anterior cerebral artery (ACA) outlet (p = 0.0238) and a significant decrease in CMRT at the MCA outlet (p = 0.012). The patient-derived models were reconstructed from the pre- and post-interventional DSA images of a patient with MCA stenosis. Both blood flow velocity and CMRT increased at the ACA outlet but decreased at the MCA outlet. The perfusion analysis demonstrated that the perfusion function was improved after interventional surgery. In conclusion, changes in stenotic degree at MCA may lead to apparent differences in the hemodynamic distribution and the transport of CM. CMRT could be a quantitative indicator to evaluate the changes in blood perfusion after the intervention for MCA stenosis.

• Authors: Yun-Hao Lu, Yan Cai, Yi Zhang, Rui Wang, Zhi-Yong Li
• Journal: Frontiers in physiology (Front Physiol) Vol. 12 Pg. 716173 ( 2021) ISSN: 1664-042X [Print] Switzerland
• PMID: 34421658 (Publication Type: Journal Article)
• Copyright: Copyright © 2021 Lu, Cai, Zhang, Wang and Li.