Pixel-wise assessment of cardiovascular magnetic resonance first-pass perfusion using a cardiac phantom mimicking transmural myocardial perfusion gradients

Purpose: Cardiovascular magnetic resonance first-pass perfusion for the pixel-wise detection of coronary artery disease is rapidly becoming the clinical standard, yet no widely available method exists for its assessment and validation. This study introduces a novel phantom capable of generating spatially dependent flow values to enable assessment of new perfusion imaging methods at the pixel level. Methods: A synthetic multicapillary myocardial phantom mimicking transmural myocardial perfusion gradients was designed and manufactured with high-precision 3D printing. The phantom was used in a stationary flow setup providing reference myocardial perfusion rates and was scanned on a 3T system. Repeated first-pass perfusion MRI for physiological perfusion rates between 1 and 4 mL/g/min was performed using a clinical dual-sequence technique. Fermi function-constrained deconvolution was used to estimate pixel-wise perfusion rate maps. Phase contrast (PC)-MRI was used to obtain velocity measurements that were converted to perfusion rates for validation of reference values and cross-method comparison. The accuracy of pixel-wise maps was assessed against simulated reference maps. Results: PC-MRI indicated excellent reproducibility in perfusion rate (coefficient of variation [CoV] 2.4-3.5%) and correlation with reference values (R2 = 0.985) across the full physiological range. Similar results were found for first-pass perfusion MRI (CoV 3.7-6.2%, R2 = 0.987). Pixel-wise maps indicated a transmural perfusion difference of 28.8-33.7% for PC-MRI and 23.8-37.7% for first-pass perfusion, matching the reference values (30.2-31.4%). Conclusion: The unique transmural perfusion pattern in the phantom allows effective pixel-wise assessment of first-pass perfusion acquisition protocols and quantification algorithms before their introduction into routine clinical use.Multi Phase System