Related Experiment Video
Updated: May 24, 2026
In Vivo Quantitative Assessment of Myocardial Structure, Function, Perfusion and Viability Using Cardiac Micro-computed Tomography
Published on: February 16, 2016
Voxel-wise quantification of myocardial perfusion by cardiac magnetic resonance. Feasibility and methods comparison
Niloufar Zarinabad1, Amedeo Chiribiri, Gilion L T F Hautvast
1Division of Imaging Sciences and Biomedical Engineering, Wellcome Trust and EPSRC Medical Engineering Centre at Guy's and St. Thomas' NHS Foundation Trust, The Rayne Institute, St. Thomas' Hospital, London, United Kingdom. niloufar.zarinabad@kcl.ac.uk
Insights
This study evaluates deconvolution algorithms for high-resolution myocardial perfusion analysis using cardiovascular MR. Autoregressive moving average and exponential methods accurately estimate blood flow, while the Fermi model excels in noisy conditions.
Area of Science:
- Cardiovascular Magnetic Resonance Imaging
- Medical Physics
- Quantitative Perfusion Analysis
Background:
- Accurate assessment of myocardial perfusion is crucial for diagnosing coronary artery disease.
- Current quantitative analysis methods in dynamic contrast-enhanced cardiovascular MR may lack sufficient spatial resolution.
- Identifying optimal algorithms for voxel-wise perfusion quantification is essential.
Purpose of the Study:
- To enable high spatial resolution, voxel-wise quantitative analysis of myocardial perfusion in dynamic contrast-enhanced cardiovascular MR.
- To identify the most favorable deconvolution algorithm for this analysis.
Main Methods:
- Four deconvolution algorithms were tested: Fermi function modeling, B-spline basis, exponential basis, and autoregressive moving average (ARMA) modeling.
- Algorithms were developed using synthetic data and validated with a hardware perfusion phantom.
- Voxel-wise analysis was applied to real patient data (suspected coronary artery disease) and healthy volunteers.
Main Results:
- The B-spline method showed the highest error in myocardial blood flow estimation.
- ARMA and exponential methods provided accurate myocardial blood flow estimates.
- The Fermi model demonstrated robustness against noise.
- Voxel-wise quantification successfully generated high-resolution perfusion maps.
Conclusions:
- Voxel-wise quantification of myocardial perfusion is feasible using dynamic contrast-enhanced cardiovascular MR.
- The developed methods can effectively detect abnormal perfusion regions.
- ARMA and exponential methods are recommended for accurate myocardial blood flow estimation, with Fermi as a robust alternative in noisy scenarios.
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 generate 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 autoregressive 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.
