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

Xenios Milidonis; Muhummad Sohaib Nazir; Amedeo Chiribiri

doi:10.1002/jmri.28180

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

Xenios Milidonis, Muhummad Sohaib Nazir, Amedeo Chiribiri

Cardiovascular Imaging

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Abstract

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.

Original language	English
Pages (from-to)	1707-1719
Number of pages	13
Journal	Journal of Magnetic Resonance Imaging
Volume	56
Issue number	6
Early online date	26 Mar 2022
DOIs	https://doi.org/10.1002/jmri.28180
Publication status	Published - Dec 2022

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Magnetic Resonance Imaging - 2022 - Milidonis - Impact of Temporal Resolution and Methods for Correction on CardiacFinal published version, 5.06 MBLicence: CC BY

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@article{91ce29a1ab09423fb2b27da2846e13b9,

title = "Impact of Temporal Resolution and Methods for Correction on Cardiac Magnetic Resonance Perfusion Quantification",

abstract = "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.",

author = "Xenios Milidonis and Nazir, {Muhummad Sohaib} and Amedeo Chiribiri",

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: {\textcopyright} 2022 The Authors. Journal of Magnetic Resonance Imaging published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.",

year = "2022",

month = dec,

doi = "10.1002/jmri.28180",

language = "English",

volume = "56",

pages = "1707--1719",

journal = "Journal of Magnetic Resonance Imaging",

issn = "1522-2586",

publisher = "John Wiley and Sons Inc.",

number = "6",

}

TY - JOUR

T1 - Impact of Temporal Resolution and Methods for Correction on Cardiac Magnetic Resonance Perfusion Quantification

AU - Milidonis, Xenios

AU - Nazir, Muhummad Sohaib

AU - Chiribiri, Amedeo

N1 - 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.

PY - 2022/12

Y1 - 2022/12

N2 - 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.

AB - 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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U2 - 10.1002/jmri.28180

DO - 10.1002/jmri.28180

M3 - Article

C2 - 35338754

SN - 1522-2586

VL - 56

SP - 1707

EP - 1719

JO - Journal of Magnetic Resonance Imaging

JF - Journal of Magnetic Resonance Imaging

IS - 6

ER -

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

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