Literature DB >> 33759238

A theoretical framework for retrospective T 2 correction to the arterial input function in quantitative myocardial perfusion MRI.

Lexiaozi Fan1,2, Bradley D Allen1, Austin E Culver3, Li-Yueh Hsu4, Kyungpyo Hong1, Brandon C Benefield3, James C Carr1, Daniel C Lee3, Daniel Kim1,2.   

Abstract

PURPOSE: To develop and evaluate a flexible, Bloch-equation based framework for retrospective T 2 ∗ correction to the arterial input function (AIF) obtained with quantitative cardiac perfusion pulse sequences.
METHODS: Our framework initially calculates the gadolinium concentration [Gd] based on T1 measurements alone. Next, T 2 ∗ is estimated from this initial calculation of [Gd] while assuming fast water exchange and using the literature native T2 and static magnetic field variation (ΔB0 ) values. Finally, the [Gd] is recalculated after performing T 2 ∗ correction to the Bloch equation signal model. Using this approach, we performed T 2 ∗ correction to historical phantom and in vivo, dual-imaging perfusion data sets from 3 different patient groups obtained using different pulse sequences and imaging parameters. Images were processed to quantify both the AIF and resting myocardial blood flow (MBF). We also performed a sensitivity analysis of our T 2 ∗ correction to ±20% variations in native T2 and ΔB0 .
RESULTS: Compared with the ground truth [Gd] of phantom, the normalized root-means-square-error (NRMSE) in measured [Gd] was 5.1%, 1.3%, and 0.6% for uncorrected, our corrected, and Kellman's corrected, respectively. For in vivo data, both the peak AIF (7.0 ± 3.0 mM vs. 8.6 ± 7.1 mM, 7.2 ± 0.9 mM vs. 8.6 ± 1.7 mM, 7.7 ± 1.8 mM vs. 10.3 ± 5.1 mM, P < .001) and resting MBF (1.3 ± 0.1 mL/g/min vs. 1.1 ± 0.1 mL/g/min, 1.3 ± 0.1 mL/g/min vs. 1.1 ± 0.1 mL/g/min, 1.2 ± 0.1 mL/g/min vs. 0.9 ± 0.1 mL/g/min, P < .001) values were significantly different between uncorrected and corrected for all 3 patient groups. Both the peak AIF and resting MBF values varied by <5% over the said variations in native T2 and ΔB0 .
CONCLUSION: Our theoretical framework enables retrospective T 2 ∗ correction to the AIF obtained with dual-imaging, cardiac perfusion pulse sequences.
© 2021 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  zzm321990 zzm321990 zzm321990 Tzzm321990 2zzm321990 zzm321990 zzm321990 zzm321990 correction; arterial input function; myocardial blood flow; perfusion

Mesh:

Substances:

Year:  2021        PMID: 33759238      PMCID: PMC8194096          DOI: 10.1002/mrm.28760

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   3.737


  26 in total

1.  Absolute myocardial perfusion in canines measured by using dual-bolus first-pass MR imaging.

Authors:  Timothy F Christian; Dan W Rettmann; Anthony H Aletras; Steve L Liao; Joni L Taylor; Robert S Balaban; Andrew E Arai
Journal:  Radiology       Date:  2004-07-29       Impact factor: 11.105

2.  Theory-based signal calibration with single-point T1 measurements for first-pass quantitative perfusion MRI studies.

Authors:  Alexandru Cernicanu; Leon Axel
Journal:  Acad Radiol       Date:  2006-06       Impact factor: 3.173

3.  Sparse MRI: The application of compressed sensing for rapid MR imaging.

Authors:  Michael Lustig; David Donoho; John M Pauly
Journal:  Magn Reson Med       Date:  2007-12       Impact factor: 4.668

4.  Golden ratio sparse MRI using tiny golden angles.

Authors:  Stefan Wundrak; Jan Paul; Johannes Ulrici; Erich Hell; Margrit-Ann Geibel; Peter Bernhardt; Wolfgang Rottbauer; Volker Rasche
Journal:  Magn Reson Med       Date:  2015-07-07       Impact factor: 4.668

5.  Assessment of advanced coronary artery disease: advantages of quantitative cardiac magnetic resonance perfusion analysis.

Authors:  Amit R Patel; Patrick F Antkowiak; Kiran R Nandalur; Amy M West; Michael Salerno; Vishal Arora; John Christopher; Frederick H Epstein; Christopher M Kramer
Journal:  J Am Coll Cardiol       Date:  2010-08-10       Impact factor: 24.094

6.  MR-IMPACT II: Magnetic Resonance Imaging for Myocardial Perfusion Assessment in Coronary artery disease Trial: perfusion-cardiac magnetic resonance vs. single-photon emission computed tomography for the detection of coronary artery disease: a comparative multicentre, multivendor trial.

Authors:  Juerg Schwitter; Christian M Wacker; Norbert Wilke; Nidal Al-Saadi; Ekkehart Sauer; Kalman Huettle; Stefan O Schönberg; Andreas Luchner; Oliver Strohm; Hakan Ahlstrom; Thorsten Dill; Nadja Hoebel; Tamas Simor
Journal:  Eur Heart J       Date:  2012-03-04       Impact factor: 29.983

7.  Magnetic resonance quantification of the myocardial perfusion reserve with a Fermi function model for constrained deconvolution.

Authors:  M Jerosch-Herold; N Wilke; A E Stillman
Journal:  Med Phys       Date:  1998-01       Impact factor: 4.071

8.  Diagnostic accuracy of stress perfusion CMR in comparison with quantitative coronary angiography: fully quantitative, semiquantitative, and qualitative assessment.

Authors:  Federico E Mordini; Tariq Haddad; Li-Yueh Hsu; Peter Kellman; Tracy B Lowrey; Anthony H Aletras; W Patricia Bandettini; Andrew E Arai
Journal:  JACC Cardiovasc Imaging       Date:  2014-01

9.  MR-IMPACT: comparison of perfusion-cardiac magnetic resonance with single-photon emission computed tomography for the detection of coronary artery disease in a multicentre, multivendor, randomized trial.

Authors:  Juerg Schwitter; Christian M Wacker; Albert C van Rossum; Massimo Lombardi; Nidal Al-Saadi; Hakan Ahlstrom; Thorsten Dill; Henrik B W Larsson; Scott D Flamm; Moritz Marquardt; Lars Johansson
Journal:  Eur Heart J       Date:  2008-01-21       Impact factor: 29.983

10.  Myocardial perfusion cardiovascular magnetic resonance: optimized dual sequence and reconstruction for quantification.

Authors:  Peter Kellman; Michael S Hansen; Sonia Nielles-Vallespin; Jannike Nickander; Raquel Themudo; Martin Ugander; Hui Xue
Journal:  J Cardiovasc Magn Reson       Date:  2017-04-07       Impact factor: 5.364

View more
  1 in total

1.  Optimal saturation recovery time for minimizing the underestimation of arterial input function in quantitative cardiac perfusion MRI.

Authors:  Lexiaozi Fan; Kyungpyo Hong; Li-Yueh Hsu; James C Carr; Bradley D Allen; Daniel C Lee; Daniel Kim
Journal:  Magn Reson Med       Date:  2022-04-04       Impact factor: 3.737

  1 in total

北京卡尤迪生物科技股份有限公司 © 2022-2023.