PASCAL - Pattern Analysis, Statistical Modelling and Computational Learning

Voxel-Wise Quantification of Myocardial Perfusion by Cardiac Magnetic Resonance: Feasibility and Methods Comparison
Zoran Cvetkovic, Niloufar Zarinabad, Amedeo Chiribiri, Gilion / L / T / F Hautvast, Masaki Ishida, Andreas Schuster, Philip / G Batchelor and Eike Nagel
Magnetic Resonance in Medicine 2011.

Abstract

The purpose of this study is to enable high spatial resolution voxel-wise quantitative analysis of myocardial perfusion in dynamic contrast-enhanced cardiovascular MR, in particular by finding the most favorable quantification algorithm in this context. Four deconvolution algorithms—Fermi function modeling, deconvolution using B-spline basis, deconvolution using exponential basis, and autoregressive moving average modeling —were tested to calculate voxel-wise perfusion estimates. The algorithms were developed on synthetic data and validated against a true gold- standard using a hardware perfusion phantom. The accuracy of each method was assessed for different levels of spatial averaging and perfusion rate. Finally, voxel-wise analysis was used to gener- ate high resolution perfusion maps on real data acquired from five patients with suspected coronary artery disease and two healthy volunteers. On both synthetic and perfusion phantom data, the B-spline method had the highest error in estimation of myocardial blood flow. The auto-regressive moving average modeling and exponential methods gave accurate estimates of myocardial blood flow. The Fermi model was the most robust method to noise. Both simulations and maps in the patients and hardware phantom showed that voxel-wise quantification of myocardium perfusion is feasible and can be used to detect abnormal regions.

EPrint Type:Article
Additional Information:The selected category is not quite appropriate for this publication, but seemed closest of available options.
Project Keyword:Project Keyword UNSPECIFIED
Subjects:Learning/Statistics & Optimisation
ID Code:9087
Deposited By:Zoran Cvetkovic
Deposited On:21 February 2012