Literature DB >> 30193953

Exploring the sensitivity of magnetic resonance fingerprinting to motion.

Zidan Yu1, Tiejun Zhao2, Jakob Assländer3, Riccardo Lattanzi4, Daniel K Sodickson4, Martijn A Cloos4.   

Abstract

PURPOSE: To explore the motion sensitivity of magnetic resonance fingerprinting (MRF), we performed experiments with different types of motion at various time intervals during multiple scans. Additionally, we investigated the possibility to correct the motion artifacts based on redundancy in MRF data.
METHODS: A radial version of the FISP-MRF sequence was used to acquire one transverse slice through the brain. Three subjects were instructed to move in different patterns (in-plane rotation, through-plane wiggle, complex movements, adjust head position, and pretend itch) during different time intervals. The potential to correct motion artifacts in MRF by removing motion-corrupted data points from the fingerprints and dictionary was evaluated.
RESULTS: Morphological structures were well preserved in multi-parametric maps despite subject motion. Although the bulk T1 values were not significantly affected by motion, fine structures were blurred when in-plane motion was present during the first part of the scan. On the other hand, T2 values showed a considerable deviation from the motion-free results, especially when through-plane motion was present in the middle of the scan (-44% on average). Explicitly removing the motion-corrupted data from the scan partially restored the T2 values (-10% on average).
CONCLUSION: Our experimental results showed that different kinds of motion have distinct effects on the precision and effective resolution of the parametric maps measured with MRF. Although MRF-based acquisitions can be relatively robust to motion effects occurring at the beginning or end of the sequence, relying on redundancy in the data alone is not sufficient to assure the accuracy of the multi-parametric maps in all cases.
Copyright © 2018 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  MRI; Magnetic resonance fingerprinting; Motion; Quantitative imaging

Mesh:

Year:  2018        PMID: 30193953      PMCID: PMC6215476          DOI: 10.1016/j.mri.2018.09.002

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


  25 in total

1.  Motion correction with PROPELLER MRI: application to head motion and free-breathing cardiac imaging.

Authors:  J G Pipe
Journal:  Magn Reson Med       Date:  1999-11       Impact factor: 4.668

2.  Image reconstruction algorithm for motion insensitive MR Fingerprinting (MRF): MORF.

Authors:  Bhairav Bipin Mehta; Dan Ma; Eric Yann Pierre; Yun Jiang; Simone Coppo; Mark Alan Griswold
Journal:  Magn Reson Med       Date:  2018-05-06       Impact factor: 4.668

3.  Magnetic resonance imaging of freely moving objects: prospective real-time motion correction using an external optical motion tracking system.

Authors:  M Zaitsev; C Dold; G Sakas; J Hennig; O Speck
Journal:  Neuroimage       Date:  2006-04-05       Impact factor: 6.556

4.  An optimal radial profile order based on the Golden Ratio for time-resolved MRI.

Authors:  Stefanie Winkelmann; Tobias Schaeffter; Thomas Koehler; Holger Eggers; Olaf Doessel
Journal:  IEEE Trans Med Imaging       Date:  2007-01       Impact factor: 10.048

Review 5.  Prospective motion correction in brain imaging: a review.

Authors:  Julian Maclaren; Michael Herbst; Oliver Speck; Maxim Zaitsev
Journal:  Magn Reson Med       Date:  2012-05-08       Impact factor: 4.668

6.  MR fingerprinting using fast imaging with steady state precession (FISP) with spiral readout.

Authors:  Yun Jiang; Dan Ma; Nicole Seiberlich; Vikas Gulani; Mark A Griswold
Journal:  Magn Reson Med       Date:  2014-12-09       Impact factor: 4.668

7.  Slice profile and B1 corrections in 2D magnetic resonance fingerprinting.

Authors:  Dan Ma; Simone Coppo; Yong Chen; Debra F McGivney; Yun Jiang; Shivani Pahwa; Vikas Gulani; Mark A Griswold
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8.  Performance of QRS detection for cardiac magnetic resonance imaging with a novel vectorcardiographic triggering method.

Authors:  J M Chia; S E Fischer; S A Wickline; C H Lorenz
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9.  XD-GRASP: Golden-angle radial MRI with reconstruction of extra motion-state dimensions using compressed sensing.

Authors:  Li Feng; Leon Axel; Hersh Chandarana; Kai Tobias Block; Daniel K Sodickson; Ricardo Otazo
Journal:  Magn Reson Med       Date:  2015-03-25       Impact factor: 4.668

10.  Prospective head-movement correction for high-resolution MRI using an in-bore optical tracking system.

Authors:  Lei Qin; Peter van Gelderen; John Andrew Derbyshire; Fenghua Jin; Jongho Lee; Jacco A de Zwart; Yang Tao; Jeff H Duyn
Journal:  Magn Reson Med       Date:  2009-10       Impact factor: 4.668

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Authors:  Azadeh Sharafi; Marcelo V W Zibetti; Gregory Chang; Martijn Cloos; Ravinder R Regatte
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4.  Motion-robust quantitative multiparametric brain MRI with motion-resolved MR multitasking.

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5.  T1 and T2 quantification using magnetic resonance fingerprinting in mild traumatic brain injury.

Authors:  Teresa Gerhalter; Martijn Cloos; Anna M Chen; Seena Dehkharghani; Rosemary Peralta; James S Babb; Alejandro Zarate; Tamara Bushnik; Jonathan M Silver; Brian S Im; Stephen Wall; Steven Baete; Guillaume Madelin; Ivan I Kirov
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6.  Quantitative T1 and T2 mapping by magnetic resonance fingerprinting (MRF) of the placenta before and after maternal hyperoxia.

Authors:  Jeffrey N Stout; Congyu Liao; Borjan Gagoski; Esra Abaci Turk; Henry A Feldman; Carolina Bibbo; William H Barth; Scott A Shainker; Lawrence L Wald; P Ellen Grant; Elfar Adalsteinsson
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7.  Simultaneous bilateral T1 , T2 , and T relaxation mapping of the hip joint with magnetic resonance fingerprinting.

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8.  Free-breathing abdominal T1 mapping using an optimized MR fingerprinting sequence.

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9.  Sparsity and locally low rank regularization for MR fingerprinting.

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Journal:  Magn Reson Med       Date:  2019-02-05       Impact factor: 4.668

Review 10.  Variability and Standardization of Quantitative Imaging: Monoparametric to Multiparametric Quantification, Radiomics, and Artificial Intelligence.

Authors:  Akifumi Hagiwara; Shohei Fujita; Yoshiharu Ohno; Shigeki Aoki
Journal:  Invest Radiol       Date:  2020-09       Impact factor: 10.065

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