Estimating myocardial perfusion from dynamic contrast-enhanced CMR with a model-independent deconvolution method

Area of Science:

• Cardiovascular Imaging
• Medical Physics
• Biophysics

Background:

• Model-independent analysis with B-spline regularization is used for myocardial blood flow (perfusion) quantification in dynamic contrast-enhanced cardiovascular magnetic resonance (CMR).
• Previous evaluations have not extensively assessed how contrast-to-noise ratio (CNR) affects perfusion estimates and regularization dependence on noise.
• This study addresses these limitations by investigating an iterative model-independent analysis method.

Purpose of the Study:

• To evaluate a model-independent analysis method for quantifying myocardial perfusion using dynamic contrast-enhanced CMR.
• To determine the impact of contrast-to-noise ratio on perfusion estimates.
• To assess the robustness of regularization to noise in enhancement data.

Main Methods:

• Developed and tested an iterative model-independent analysis method with a temporal smoothness regularizer.
• Applied the method to estimate regional and pixelwise myocardial perfusion in five normal subjects using 3 T CMR.
• Compared perfusion estimates with dynamic 13N-ammonia PET.

Main Results:

• Myocardial perfusion estimates are dependent on the regularization weight parameter, which adjusts nonlinearly with CNR.
• Quantitative perfusion estimates at rest and stress were 1.1 ± 0.8 ml/min/g and 3.1 ± 1.7 ml/min/g, respectively.
• Perfusion estimates correlated well with 13N-ammonia PET (r = 0.85) and aligned with other CMR studies.

Conclusions:

• A model-independent analysis method using iterative minimization and temporal regularization can reliably quantify myocardial perfusion in dynamic contrast-enhanced CMR.
• The method demonstrates robustness to regularization parameter choices across a wide range of CNR.
• This approach offers a valuable tool for myocardial perfusion assessment.