Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution method
<p>Abstract</p> <p>Background</p> <p>Model-independent analysis with B-spline regularization has been used to quantify myocardial blood flow (perfusion) in dynamic contrast-enhanced cardiovascular magnetic resonance (CMR) studies. However, the model-independent approach...
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doaj-edadcdbc7a894837b1647fcd15b421c92020-11-24T21:55:12ZengBMCJournal of Cardiovascular Magnetic Resonance1097-66471532-429X2008-11-011015210.1186/1532-429X-10-52Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution methodKadrmas Dan JRust Thomas CDiBella Edward VRPack Nathan AMcGann Christopher JButterfield ReganChristian Paul EHoffman John M<p>Abstract</p> <p>Background</p> <p>Model-independent analysis with B-spline regularization has been used to quantify myocardial blood flow (perfusion) in dynamic contrast-enhanced cardiovascular magnetic resonance (CMR) studies. However, the model-independent approach has not been extensively evaluated to determine how the contrast-to-noise ratio between blood and tissue enhancement affects estimates of myocardial perfusion and the degree to which the regularization is dependent on the noise in the measured enhancement data. We investigated these questions with a model-independent analysis method that uses iterative minimization and a temporal smoothness regularizer. Perfusion estimates using this method were compared to results from dynamic <sup>13</sup>N-ammonia PET.</p> <p>Results</p> <p>An iterative model-independent analysis method was developed and tested to estimate regional and pixelwise myocardial perfusion in five normal subjects imaged with a saturation recovery turboFLASH sequence at 3 T CMR. Estimates of myocardial perfusion using model-independent analysis are dependent on the choice of the regularization weight parameter, which increases nonlinearly to handle large decreases in the contrast-to-noise ratio of the measured tissue enhancement data. Quantitative perfusion estimates in five subjects imaged with 3 T CMR were 1.1 ± 0.8 ml/min/g at rest and 3.1 ± 1.7 ml/min/g at adenosine stress. The perfusion estimates correlated with dynamic <sup>13</sup>N-ammonia PET (y = 0.90x + 0.24, r = 0.85) and were similar to results from other validated CMR studies.</p> <p>Conclusion</p> <p>This work shows that a model-independent analysis method that uses iterative minimization and temporal regularization can be used to quantify myocardial perfusion with dynamic contrast-enhanced perfusion CMR. Results from this method are robust to choices in the regularization weight parameter over relatively large ranges in the contrast-to-noise ratio of the tissue enhancement data.</p> http://www.jcmr-online.com/content/10/1/52 |
collection |
DOAJ |
language |
English |
format |
Article |
sources |
DOAJ |
author |
Kadrmas Dan J Rust Thomas C DiBella Edward VR Pack Nathan A McGann Christopher J Butterfield Regan Christian Paul E Hoffman John M |
spellingShingle |
Kadrmas Dan J Rust Thomas C DiBella Edward VR Pack Nathan A McGann Christopher J Butterfield Regan Christian Paul E Hoffman John M Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution method Journal of Cardiovascular Magnetic Resonance |
author_facet |
Kadrmas Dan J Rust Thomas C DiBella Edward VR Pack Nathan A McGann Christopher J Butterfield Regan Christian Paul E Hoffman John M |
author_sort |
Kadrmas Dan J |
title |
Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution method |
title_short |
Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution method |
title_full |
Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution method |
title_fullStr |
Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution method |
title_full_unstemmed |
Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution method |
title_sort |
estimating myocardial perfusion from dynamic contrast-enhanced cmr with a model-independent deconvolution method |
publisher |
BMC |
series |
Journal of Cardiovascular Magnetic Resonance |
issn |
1097-6647 1532-429X |
publishDate |
2008-11-01 |
description |
<p>Abstract</p> <p>Background</p> <p>Model-independent analysis with B-spline regularization has been used to quantify myocardial blood flow (perfusion) in dynamic contrast-enhanced cardiovascular magnetic resonance (CMR) studies. However, the model-independent approach has not been extensively evaluated to determine how the contrast-to-noise ratio between blood and tissue enhancement affects estimates of myocardial perfusion and the degree to which the regularization is dependent on the noise in the measured enhancement data. We investigated these questions with a model-independent analysis method that uses iterative minimization and a temporal smoothness regularizer. Perfusion estimates using this method were compared to results from dynamic <sup>13</sup>N-ammonia PET.</p> <p>Results</p> <p>An iterative model-independent analysis method was developed and tested to estimate regional and pixelwise myocardial perfusion in five normal subjects imaged with a saturation recovery turboFLASH sequence at 3 T CMR. Estimates of myocardial perfusion using model-independent analysis are dependent on the choice of the regularization weight parameter, which increases nonlinearly to handle large decreases in the contrast-to-noise ratio of the measured tissue enhancement data. Quantitative perfusion estimates in five subjects imaged with 3 T CMR were 1.1 ± 0.8 ml/min/g at rest and 3.1 ± 1.7 ml/min/g at adenosine stress. The perfusion estimates correlated with dynamic <sup>13</sup>N-ammonia PET (y = 0.90x + 0.24, r = 0.85) and were similar to results from other validated CMR studies.</p> <p>Conclusion</p> <p>This work shows that a model-independent analysis method that uses iterative minimization and temporal regularization can be used to quantify myocardial perfusion with dynamic contrast-enhanced perfusion CMR. Results from this method are robust to choices in the regularization weight parameter over relatively large ranges in the contrast-to-noise ratio of the tissue enhancement data.</p> |
url |
http://www.jcmr-online.com/content/10/1/52 |
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