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.

Design and experimental verification of a photoacoustic flow sensor using computational fluid dynamics.

A photoacoustic (PA) sensor for fast and real-time gas sensing is demonstrated. The PA sensor is a stand-alone system controlled by a field-programmable gate array. The PA cell has been designed for flow noise immunity using computational fluid dynamics (CFD) analysis. The aim of the CFD analysis was to investigate and minimize the influence of the gas distribution and flow noise on the PA signal. PA measurements were conducted at different flow rates by exciting molecular C-H stretch vibrational bands of hexane (C6H14) and decane (C10H22) molecules in clean air at 2950 cm-1 (3.38 µm) with a custom-made mid-infrared interband cascade laser. We observe a (1σ, standard deviation) sensitivity of 0.4±0.1 ppb (nmol/mol) for hexane in clean air at flow rates up to 1.7 L/min, corresponding to a normalized noise equivalent absorption coefficient of 2.5×10-9 W cm-1 Hz-1/2, demonstrating high sensitivity and fast real-time gas analysis. An Allan deviation analysis for decane shows that the detection limit at optimum integration time is 0.25 ppbV (nmol/mol).