title: Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution method.
abstract: BACKGROUND
  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 13N-ammonia PET.
  
  
  RESULTS
  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 13N-ammonia PET (y = 0.90x + 0.24, r = 0.85) and were similar to results from other validated CMR studies.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Bioengineering, University of Utah, Salt Lake City, Salt Lake County, Utah, USA. npack@ucair.med.utah.edu
authors.forename: Nathan A
authors.initials: NA
authors.lastname: Pack
authors.forename: Edward V R
authors.initials: EV
authors.lastname: DiBella
authors.forename: Thomas C
authors.initials: TC
authors.lastname: Rust
authors.forename: Dan J
authors.initials: DJ
authors.lastname: Kadrmas
authors.forename: Christopher J
authors.initials: CJ
authors.lastname: McGann
authors.forename: Regan
authors.initials: R
authors.lastname: Butterfield
authors.forename: Paul E
authors.initials: PE
authors.lastname: Christian
authors.forename: John M
authors.initials: JM
authors.lastname: Hoffman
mesh_terms: D000328:Adult; D000368:Aged; D000641:Ammonia; D003287:Contrast Media; D003326:Coronary Circulation; D006801:Humans; D007090:Image Interpretation, Computer-Assisted; D008279:Magnetic Resonance Imaging; D008297:Male; D008875:Middle Aged; D008955:Models, Cardiovascular; D055414:Myocardial Perfusion Imaging; D009590:Nitrogen Radioisotopes; D049268:Positron-Emission Tomography; D011237:Predictive Value of Tests; D019275:Radiopharmaceuticals; D012016:Reference Values; D013997:Time Factors
chemical_list: D003287:Contrast Media; D009590:Nitrogen Radioisotopes; D019275:Radiopharmaceuticals; D000641:Ammonia