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Impact of Temporal Resolution and Methods for Correction on Cardiac Magnetic Resonance Perfusion Quantification

Research output: Contribution to journalArticlepeer-review

Original languageEnglish
Pages (from-to)1707-1719
Number of pages13
JournalJournal of Magnetic Resonance Imaging
Issue number6
Early online date26 Mar 2022
Accepted/In press15 Mar 2022
E-pub ahead of print26 Mar 2022
PublishedDec 2022

Bibliographical note

Funding Information: X.M. and A.C. were funded by the British Heart Foundation (TG/18/2/33768). M.S.N. was funded by the UK Medical Research Council (MR/P01979X/1) and by a National Institute for Health Research (NIHR) Clinical Lectureship (CL‐2019‐17‐001). Further support was received by Rosetree's Trust (A1380), the Wellcome/EPSRC Centre for Medical Engineering (WT 203148/Z/16/Z), and the EPSRC Centre for Doctoral Training in Medical Imaging (EP/L015226/1), as well as the NIHR Biomedical Research Centre based at Guy's and St Thomas' National Health Service (NHS) Foundation Trust, the NIHR Cardiovascular MedTech Co‐operative at Guy's and St Thomas' NHS Foundation Trust and King's College London and supported by the NIHR Clinical Research Facility (CRF) at Guy's and St Thomas'. The views expressed are those of the authors and not necessarily those of the Wellcome Trust, the EPSRC, the NIHR, or the NHS. Funding information: Publisher Copyright: © 2022 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.


King's Authors


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.

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