Literature DB >> 29545215

Estimation of perfusion properties with MR Fingerprinting Arterial Spin Labeling.

Katherine L Wright1, Yun Jiang2, Dan Ma2, Douglas C Noll3, Mark A Griswold4, Vikas Gulani4, Luis Hernandez-Garcia3.   

Abstract

In this study, the acquisition of ASL data and quantification of multiple hemodynamic parameters was explored using a Magnetic Resonance Fingerprinting (MRF) approach. A pseudo-continuous ASL labeling scheme was used with pseudo-randomized timings to acquire the MRF ASL data in a 2.5 min acquisition. A large dictionary of MRF ASL signals was generated by combining a wide range of physical and hemodynamic properties with the pseudo-random MRF ASL sequence and a two-compartment model. The acquired signals were matched to the dictionary to provide simultaneous quantification of cerebral blood flow, tissue time-to-peak, cerebral blood volume, arterial time-to-peak, B1, and T1. A study in seven healthy volunteers resulted in the following values across the population in grey matter (mean ± standard deviation): cerebral blood flow of 69.1 ± 6.1 ml/min/100 g, arterial time-to-peak of 1.5 ± 0.1 s, tissue time-to-peak of 1.5 ± 0.1 s, T1 of 1634 ms, cerebral blood volume of 0.0048 ± 0.0005. The CBF measurements were compared to standard pCASL CBF estimates using a one-compartment model, and a Bland-Altman analysis showed good agreement with a minor bias. Repeatability was tested in five volunteers in the same exam session, and no statistical difference was seen. In addition to this validation, the MRF ASL acquisition's sensitivity to the physical and physiological parameters of interest was studied numerically.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Arterial spin labeling; MR fingerprinting; Perfusion

Mesh:

Substances:

Year:  2018        PMID: 29545215      PMCID: PMC5970985          DOI: 10.1016/j.mri.2018.03.011

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  40 in total

1.  Blind detection of vascular sources and territories using random vessel encoded arterial spin labeling.

Authors:  Eric C Wong; Jia Guo
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2.  Quantification of perfusion fMRI using a numerical model of arterial spin labeling that accounts for dynamic transit time effects.

Authors:  Luis Hernandez-Garcia; Gregory R Lee; Alberto L Vazquez; Chun-Yu Yip; Douglas C Noll
Journal:  Magn Reson Med       Date:  2005-10       Impact factor: 4.668

3.  A theoretical and experimental comparison of continuous and pulsed arterial spin labeling techniques for quantitative perfusion imaging.

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Journal:  Magn Reson Med       Date:  1998-09       Impact factor: 4.668

Review 4.  Applications of arterial spin labeled MRI in the brain.

Authors:  John A Detre; Hengyi Rao; Danny J J Wang; Yu Fen Chen; Ze Wang
Journal:  J Magn Reson Imaging       Date:  2012-01-13       Impact factor: 4.813

5.  Multiparametric estimation of brain hemodynamics with MR fingerprinting ASL.

Authors:  Pan Su; Deng Mao; Peiying Liu; Yang Li; Marco C Pinho; Babu G Welch; Hanzhang Lu
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6.  Incidence of nephrogenic systemic fibrosis after adoption of restrictive gadolinium-based contrast agent guidelines.

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Review 8.  Arterial Spin Labeling Perfusion of the Brain: Emerging Clinical Applications.

Authors:  Sven Haller; Greg Zaharchuk; David L Thomas; Karl-Olof Lovblad; Frederik Barkhof; Xavier Golay
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Authors:  Danny J J Wang; Jeffry R Alger; Joe X Qiao; Matthias Gunther; Whitney B Pope; Jeffrey L Saver; Noriko Salamon; David S Liebeskind
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10.  Magnetic resonance fingerprinting.

Authors:  Dan Ma; Vikas Gulani; Nicole Seiberlich; Kecheng Liu; Jeffrey L Sunshine; Jeffrey L Duerk; Mark A Griswold
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  13 in total

Review 1.  Magnetic resonance fingerprinting: an overview.

Authors:  Charit Tippareddy; Walter Zhao; Jeffrey L Sunshine; Mark Griswold; Dan Ma; Chaitra Badve
Journal:  Eur J Nucl Med Mol Imaging       Date:  2021-05-26       Impact factor: 9.236

2.  ASL-MRICloud: An online tool for the processing of ASL MRI data.

Authors:  Yang Li; Peiying Liu; Yue Li; Hongli Fan; Pan Su; Shin-Lei Peng; Denise C Park; Karen M Rodrigue; Hangyi Jiang; Andreia V Faria; Can Ceritoglu; Michael Miller; Susumu Mori; Hanzhang Lu
Journal:  NMR Biomed       Date:  2018-12-26       Impact factor: 4.044

3.  Numerical approximation to the general kinetic model for ASL quantification.

Authors:  Nam G Lee; Ahsan Javed; Terrence R Jao; Krishna S Nayak
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Review 4.  Magnetic resonance fingerprinting review part 2: Technique and directions.

Authors:  Debra F McGivney; Rasim Boyacıoğlu; Yun Jiang; Megan E Poorman; Nicole Seiberlich; Vikas Gulani; Kathryn E Keenan; Mark A Griswold; Dan Ma
Journal:  J Magn Reson Imaging       Date:  2019-07-25       Impact factor: 4.813

5.  MR fingerprinting ASL: Sequence characterization and comparison with dynamic susceptibility contrast (DSC) MRI.

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Journal:  NMR Biomed       Date:  2019-11-04       Impact factor: 4.044

6.  Non-contrast hemodynamic imaging of Moyamoya disease with MR fingerprinting ASL: A feasibility study.

Authors:  Pan Su; Peiying Liu; Marco C Pinho; Binu P Thomas; Ye Qiao; Judy Huang; Babu G Welch; Hanzhang Lu
Journal:  Magn Reson Imaging       Date:  2022-02-18       Impact factor: 2.546

7.  Simultaneous Hemodynamic and Structural Imaging of Ischemic Stroke With Magnetic Resonance Fingerprinting Arterial Spin Labeling.

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8.  Improved MR fingerprinting for relaxation measurement in the presence of semisolid magnetization transfer.

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Review 9.  Cardiac magnetic resonance fingerprinting: Trends in technical development and potential clinical applications.

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10.  Optimizing MRF-ASL scan design for precise quantification of brain hemodynamics using neural network regression.

Authors:  Anish Lahiri; Jeffrey A Fessler; Luis Hernandez-Garcia
Journal:  Magn Reson Med       Date:  2019-11-21       Impact factor: 3.737

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