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Impact of Temporal Resolution and Methods for Correction on Cardiac Magnetic Resonance Perfusion Quantification
Xenios Milidonis1, Muhummad Sohaib Nazir1, Amedeo Chiribiri1
1School of Biomedical Engineering & Imaging Sciences, King's College London, London, UK.
Background:
Acquisition of magnetic resonance first-pass perfusion images is synchronized to the patient's heart rate (HR) and governs the temporal resolution. This is inherently linked to the process of myocardial blood flow (MBF) quantification and impacts MBF accuracy but to an unclear extent.
Purpose:
To assess the impact of temporal resolution on quantitative perfusion and compare approaches for accounting for its variability.
Study Type:
Prospective phantom and retrospective clinical study.
Population And Phantom:
Simulations, a cardiac perfusion phantom, and 30 patients with (16, 53%) or without (14, 47%) coronary artery disease.
Field Strength/Sequence:
3.0 T/2D saturation recovery spoiled gradient echo sequence.
Assessment:
Dynamic perfusion data were simulated for a range of reference MBF (1 mL/g/min-5 mL/g/min) and HR (30 bpm-150 bpm). Perfusion imaging was performed in patients and a phantom for different temporal resolutions. MBF and myocardial perfusion reserve (MPR) were quantified without correction for temporal resolution or following correction by either MBF scaling based on the sampling interval or data interpolation prior to quantification. Simulated data were quantified using Fermi deconvolution, truncated singular value decomposition, and one-compartment modeling, whereas phantom and clinical data were quantified using Fermi deconvolution alone.
Statistical Tests:
Shapiro-Wilk tests for normality, percentage error (PE) for measuring MBF accuracy in simulations, and one-way repeated measures analysis of variance with Bonferroni correction to compare clinical MBF and MPR. Statistical significance set at P < 0.05.
Results:
For Fermi deconvolution and an example simulated 1 mL/g/min, the MBF PE without correction for temporal resolution was between 55.4% and -62.7% across 30-150 bpm. PE was between -22.2% and -6.8% following MBF scaling and between -14.2% and -14.2% following data interpolation across the same HR. An interpolated HR of 240 bpm reduced PE to ≤10%. Clinical rest and stress MBF and MPR were significantly different between analyses.
Data Conclusion:
Accurate perfusion quantification needs to account for the variability of temporal resolution, with data interpolation prior to quantification reducing MBF variability across different resolutions.
Level Of Evidence:
3 TECHNICAL EFFICACY STAGE: 1.
