Computer simulation of Cerebral Arteriovenous Malformation—validation analysis of hemodynamics parameters
Author(s) -
Y. Kiran Kumar,
Shashi Bhushan Mehta,
Manjunath Ramachandra
Publication year - 2016
Publication title -
peerj
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.927
H-Index - 70
ISSN - 2167-8359
DOI - 10.7717/peerj.2724
Subject(s) - univariate , arteriovenous malformation , hemodynamics , medicine , multivariate statistics , computer science , radiology , cardiology , machine learning
Problem The purpose of this work is to provide some validation methods for evaluating the hemodynamic assessment of Cerebral Arteriovenous Malformation (CAVM). This article emphasizes the importance of validating noninvasive measurements for CAVM patients, which are designed using lumped models for complex vessel structure. Methods The validation of the hemodynamics assessment is based on invasive clinical measurements and cross-validation techniques with the Philips proprietary validated software’s Qflow and 2D Perfursion. Results The modeling results are validated for 30 CAVM patients for 150 vessel locations. Mean flow, diameter, and pressure were compared between modeling results and with clinical/cross validation measurements, using an independent two-tailed Student t test. Exponential regression analysis was used to assess the relationship between blood flow, vessel diameter, and pressure between them. Univariate analysis is used to assess the relationship between vessel diameter, vessel cross-sectional area, AVM volume, AVM pressure, and AVM flow results were performed with linear or exponential regression. Discussion Modeling results were compared with clinical measurements from vessel locations of cerebral regions. Also, the model is cross validated with Philips proprietary validated software’s Qflow and 2D Perfursion. Our results shows that modeling results and clinical results are nearly matching with a small deviation. Conclusion In this article, we have validated our modeling results with clinical measurements. The new approach for cross-validation is proposed by demonstrating the accuracy of our results with a validated product in a clinical environment.
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