In the medical sector, various imaging methodologies or modalities (e.g. MRI, PET, CT) are used to assess the health of various parts of the bodies of patients. One such investigation is the blood flow or perfusion of the heart muscle, expressed as the (blood) flow rate normalized by the mass of the volume of interest. Currently there is no physical flow standard for the validation of quantitative perfusion measurements. This need has been addressed in the EMPIR 15HLT05 PerfusImaging project. A phantom simulating the heart muscle has been developed with the capability that it can reproducibly generate a flow profile with individual flow rates known with a relative uncertainty of about 10% (k = 2) and total flow rate known with an uncertainty of 1% (k = 2). An overview of the phantom and its validation is given. Next, a new analysis method is presented to analyse the sequence of images which are acquired when using a standard dynamic imaging protocol. It is concluded that the new, alternative approach gives results comparable to the standard analysis method.

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.