Diagnostic Accuracy of Quantitative Perfusion MRI for Detecting Coronary Artery Disease: Comparison of Model-Based Deconvolution Techniques

Motivation: Dual-sequence quantitative perfusion (QP) MRI is feasible utilizing a model-based deconvolution. However, few studies have compared the diagnostic accuracy of QP MRI across different model-based deconvolution techniques in detecting significant coronary artery disease (CAD). Goal(s): To compare the diagnostic accuracy of QP MRI among 3 model-based deconvolution (Fermi function, Tofts and Modified Tofts models) for the detection of significant coronary artery disease. Approach: Stress myocardial blood flow (MBF) determined by 3 model-based deconvolution was compared with coronary angiography. Results: Fermi-function model-based deconvolution demonstrated the highest diagnostic accuracy with the AUC of 0.756, outperforming both Tofts and Modified Tofts models (0.704 and 0.680, respectively). Impact: Dual-sequence quantitative myocardial perfusion MRI using model-based deconvolution techniques enables accurate detection of significant coronary artery disease. Among the deconvolution models, the Fermi function model demonstrated the highest diagnostic accuracy, outperforming both the Tofts and Modified Tofts models.