Literature DB >> 25761550

Quantitative framework for prospective motion correction evaluation.

Nicolas A Pannetier1,2, Theano Stavrinos1,2, Peter Ng1,2, Michael Herbst3,4, Maxim Zaitsev3, Karl Young1,2, Gerald Matson1,2, Norbert Schuff1,2.   

Abstract

PURPOSE: Establishing a framework to evaluate performances of prospective motion correction (PMC) MRI considering motion variability between MRI scans.
METHODS: A framework was developed to obtain quantitative comparisons between different motion correction setups, considering that varying intrinsic motion patterns between acquisitions can induce bias. Intrinsic motion was considered by replaying in a phantom experiment the recorded motion trajectories from subjects. T1-weighted MRI on five volunteers and two different marker fixations (mouth guard and nose bridge fixations) were used to test the framework. Two metrics were investigated to quantify the improvement of the image quality with PMC.
RESULTS: Motion patterns vary between subjects as well as between repeated scans within a subject. This variability can be approximated by replaying the motion in a distinct phantom experiment and used as a covariate in models comparing motion corrections. We show that considering the intrinsic motion alters the statistical significance in comparing marker fixations. As an example, two marker fixations, a mouth guard and a nose bridge, were evaluated in terms of their effectiveness for PMC. A mouth guard achieved better PMC performance.
CONCLUSION: Intrinsic motion patterns can bias comparisons between PMC configurations and must be considered for robust evaluations. A framework for evaluating intrinsic motion patterns in PMC is presented.
© 2015 Wiley Periodicals, Inc.

Entities:  

Keywords:  PMC; average edge strength; haralick texture; marker fixation; motion correction; prospective motion correction

Mesh:

Year:  2015        PMID: 25761550      PMCID: PMC4567538          DOI: 10.1002/mrm.25580

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN: 0740-3194            Impact factor:   4.668


  21 in total

1.  Image metric-based correction (autocorrection) of motion effects: analysis of image metrics.

Authors:  K P McGee; A Manduca; J P Felmlee; S J Riederer; R L Ehman
Journal:  J Magn Reson Imaging       Date:  2000-02       Impact factor: 4.813

Review 2.  Motion artifact suppression: a review of post-processing techniques.

Authors:  M Hedley; H Yan
Journal:  Magn Reson Imaging       Date:  1992       Impact factor: 2.546

3.  Nonrigid motion correction in 3D using autofocusing with localized linear translations.

Authors:  Joseph Y Cheng; Marcus T Alley; Charles H Cunningham; Shreyas S Vasanawala; John M Pauly; Michael Lustig
Journal:  Magn Reson Med       Date:  2012-02-03       Impact factor: 4.668

4.  Summarizing complexity in high dimensions.

Authors:  Karl Young; Yue Chen; John Kornak; Gerald B Matson; Norbert Schuff
Journal:  Phys Rev Lett       Date:  2005-03-08       Impact factor: 9.161

5.  Measuring structural complexity in brain images.

Authors:  Karl Young; Norbert Schuff
Journal:  Neuroimage       Date:  2007-11-12       Impact factor: 6.556

6.  Medical image analysis of 3D CT images based on extension of Haralick texture features.

Authors:  Ludvík Tesar; Akinobu Shimizu; Daniel Smutek; Hidefumi Kobatake; Shigeru Nawano
Journal:  Comput Med Imaging Graph       Date:  2008-07-09       Impact factor: 4.790

7.  A computational approach to edge detection.

Authors:  J Canny
Journal:  IEEE Trans Pattern Anal Mach Intell       Date:  1986-06       Impact factor: 6.226

8.  Blind retrospective motion correction of MR images.

Authors:  Alexander Loktyushin; Hannes Nickisch; Rolf Pohmann; Bernhard Schölkopf
Journal:  Magn Reson Med       Date:  2013-02-11       Impact factor: 4.668

9.  Echo-planar imaging with prospective slice-by-slice motion correction using active markers.

Authors:  Melvyn B Ooi; Sascha Krueger; Jordan Muraskin; William J Thomas; Truman R Brown
Journal:  Magn Reson Med       Date:  2011-02-24       Impact factor: 4.668

10.  Measurement and correction of microscopic head motion during magnetic resonance imaging of the brain.

Authors:  Julian Maclaren; Brian S R Armstrong; Robert T Barrows; K A Danishad; Thomas Ernst; Colin L Foster; Kazim Gumus; Michael Herbst; Ilja Y Kadashevich; Todd P Kusik; Qiaotian Li; Cris Lovell-Smith; Thomas Prieto; Peter Schulze; Oliver Speck; Daniel Stucht; Maxim Zaitsev
Journal:  PLoS One       Date:  2012-11-07       Impact factor: 3.240

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  5 in total

1.  Motion correction for diffusion weighted SMS imaging.

Authors:  M Herbst; B A Poser; A Singh; W Deng; B Knowles; M Zaitsev; V A Stenger; T Ernst
Journal:  Magn Reson Imaging       Date:  2016-12-15       Impact factor: 2.546

2.  Prospective motion correction enables highest resolution time-of-flight angiography at 7T.

Authors:  Hendrik Mattern; Alessandro Sciarra; Frank Godenschweger; Daniel Stucht; Falk Lüsebrink; Georg Rose; Oliver Speck
Journal:  Magn Reson Med       Date:  2017-12-11       Impact factor: 4.668

3.  Optical tracking with two markers for robust prospective motion correction for brain imaging.

Authors:  Aditya Singh; Benjamin Zahneisen; Brian Keating; Michael Herbst; Linda Chang; Maxim Zaitsev; Thomas Ernst
Journal:  MAGMA       Date:  2015-06-30       Impact factor: 2.310

4.  Quantitative evaluation of prospective motion correction in healthy subjects at 7T MRI.

Authors:  Alessandro Sciarra; Hendrik Mattern; Renat Yakupov; Soumick Chatterjee; Daniel Stucht; Steffen Oeltze-Jafra; Frank Godenschweger; Oliver Speck
Journal:  Magn Reson Med       Date:  2021-08-31       Impact factor: 4.668

5.  Prospective motion correction using coil-mounted cameras: Cross-calibration considerations.

Authors:  Julian Maclaren; Murat Aksoy; Melvyn B Ooi; Benjamin Zahneisen; Roland Bammer
Journal:  Magn Reson Med       Date:  2017-07-19       Impact factor: 4.668

  5 in total

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