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- Open Access
Comparison of different deconvolution algorithms for voxel-wise quantitative MR perfusion assessment
© Nooralipour et al; licensee BioMed Central Ltd. 2011
- Published: 2 February 2011
- Rest Perfusion Image
- Lower Perfusion Rate
- Baseline Signal Intensity
- Saturation Recovery Gradient
- Myocardial Perfusion Rate
To apply deconvolution algorithms to voxel-wise analysis of first-pass myocardial perfusion MR data and to determine how noise affects perfusion estimation using different quantification methods.
One of the main advantages of cardiac first-pass perfusion MR is its high spatial resolution. Though several methods have been used to quantify myocardial perfusion rate, no previous work has been done on voxel-wise analysis and published methods were applied to standard myocardial segments. Signal-to-noise ratio (SNR) might negatively affect the accuracy of the measurements obtained with methods developed for segmental analysis.
ARMA method analysis resulted in the lowest model curve-fit error for different level of noise in both rest and stress condition. Exponential deconvolution had a lower error when compared to Fermi function modelling and model independent analysis (Figure 1A). The overall rest and stress error was in the voxel-wise/segment-wise analyses 0.6%/0.5% for ARMA, 3.2/2.4% for exponential, 4%/3.3% for Fermi, and 7%/5.6% for B-spline deconvolution, respectively.
The curve-fit error increased when analyzing rest perfusion images, most likely as a result of the increased baseline signal intensity values or of a lower perfusion rate in the rest images, acquired after stress.
This study confirms the importance of adequate SNR in first-pass perfusion images. Before voxel-wise analysis can be used in clinical practice, more studies will be needed to define the best algorithms to deal with reduced SNR typical for voxel-wise tissue data. ARMA approach and Exponential basis deconvolution were the least sensitive methods to noise.
This article is published under license to BioMed Central Ltd. This is an open access article distributed under the terms of the Creative Commons Attribution License (<url>http://creativecommons.org/licenses/by/2.0</url>), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.