Literature DB >> 27699883

Susceptibility underestimation in a high-susceptibility phantom: Dependence on imaging resolution, magnitude contrast, and other parameters.

Dong Zhou1, Junghun Cho2, Jingwei Zhang2, Pascal Spincemaille1, Yi Wang1,2.   

Abstract

PURPOSE: We assessed the accuracy of quantitative susceptibility mapping in a gadolinium balloon phantom with a large range of susceptibility values and imaging resolutions at 1.5 and 3 Tesla (T). THEORY AND METHODS: The phantom contained sources with susceptibility values of 0.4, 0.8, 1.6, and 3.2 ppm and was imaged at isotropic resolutions of 0.7, 0.8, 1.2, and 1.8 mm. Numerical simulations were performed to match the experimental findings. Voxel sensitivity effects were used to explain the susceptibility underestimations.
RESULTS: Both phantom data and simulation demonstrated that systematic underestimation of the susceptibility values increased with voxel size, field strength, and object susceptibility.
CONCLUSION: The underestimation originates from the signal formation in a voxel, which can be described by the voxel sensitivity function. The amount of underestimation is thus affected by imaging resolution, magnitude contrast, image filtering, and details of the susceptibility inclusions such as the susceptibility value and geometry. High-resolution imaging is therefore needed for accurate reconstruction of QSM values, especially at higher susceptibilities. Magn Reson Med 78:1080-1086, 2017.
© 2016 International Society for Magnetic Resonance in Medicine. © 2016 International Society for Magnetic Resonance in Medicine.

Entities:  

Keywords:  imaging resolution; k-space filter; magnetic susceptibility; sample orientation; underestimation; voxel sensitivity function

Mesh:

Year:  2016        PMID: 27699883     DOI: 10.1002/mrm.26475

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


  12 in total

1.  Cerebral metabolic rate of oxygen (CMRO2 ) mapping by combining quantitative susceptibility mapping (QSM) and quantitative blood oxygenation level-dependent imaging (qBOLD).

Authors:  Junghun Cho; Youngwook Kee; Pascal Spincemaille; Thanh D Nguyen; Jingwei Zhang; Ajay Gupta; Shun Zhang; Yi Wang
Journal:  Magn Reson Med       Date:  2018-03-07       Impact factor: 4.668

2.  Cardiac quantitative susceptibility mapping (QSM) for heart chamber oxygenation.

Authors:  Yan Wen; Thanh D Nguyen; Zhe Liu; Pascal Spincemaille; Dong Zhou; Alexey Dimov; Youngwook Kee; Kofi Deh; Jiwon Kim; Jonathan W Weinsaft; Yi Wang
Journal:  Magn Reson Med       Date:  2017-06-26       Impact factor: 4.668

3.  Phantom validation of quantitative susceptibility and dynamic contrast-enhanced permeability MR sequences across instruments and sites.

Authors:  Nicholas Hobson; Sean P Polster; Ying Cao; Kelly Flemming; Yunhong Shu; John Huston; Chandra Y Gerrard; Reed Selwyn; Marc Mabray; Atif Zafar; Romuald Girard; Julián Carrión-Penagos; Yu Fen Chen; Todd Parrish; Xiaohong Joe Zhou; James I Koenig; Robert Shenkar; Agnieszka Stadnik; Janne Koskimäki; Alexey Dimov; Dallas Turley; Timothy Carroll; Issam A Awad
Journal:  J Magn Reson Imaging       Date:  2019-09-12       Impact factor: 4.813

4.  Serial assessment of iron in the motor cortex in limb-onset amyotrophic lateral sclerosis using quantitative susceptibility mapping.

Authors:  Anjan Bhattarai; Zhaolin Chen; Phillip G D Ward; Paul Talman; Susan Mathers; Thanh G Phan; Caron Chapman; James Howe; Sarah Lee; Yennie Lie; Gary F Egan; Phyllis Chua
Journal:  Quant Imaging Med Surg       Date:  2020-07

5.  QSMxT: Robust masking and artifact reduction for quantitative susceptibility mapping.

Authors:  Ashley Wilton Stewart; Simon Daniel Robinson; Kieran O'Brien; Jin Jin; Georg Widhalm; Gilbert Hangel; Angela Walls; Jonathan Goodwin; Korbinian Eckstein; Monique Tourell; Catherine Morgan; Aswin Narayanan; Markus Barth; Steffen Bollmann
Journal:  Magn Reson Med       Date:  2021-10-22       Impact factor: 4.668

6.  Quantitative Susceptibility Mapping: MRI at 7T versus 3T.

Authors:  Pascal Spincemaille; Julie Anderson; Gaohong Wu; Baolian Yang; Maggie Fung; Ke Li; Shaojun Li; Ilhami Kovanlikaya; Ajay Gupta; Douglas Kelley; Nissim Benhamo; Yi Wang
Journal:  J Neuroimaging       Date:  2019-10-18       Impact factor: 2.486

7.  Magnetic resonance quantitative susceptibility mapping in the evaluation of hepatic fibrosis in chronic liver disease: a feasibility study.

Authors:  Zheng Qu; Shuohui Yang; Feng Xing; Rui Tong; Chenyao Yang; Rongfang Guo; Jiling Huang; Fang Lu; Caixia Fu; Xu Yan; Stefanie Hectors; Kelly Gillen; Yi Wang; Chenghai Liu; Songhua Zhan; Jianqi Li
Journal:  Quant Imaging Med Surg       Date:  2021-04

8.  Ex-vivo quantitative susceptibility mapping of human brain hemispheres.

Authors:  Arnold M Evia; Aikaterini Kotrotsou; Ashish A Tamhane; Robert J Dawe; Alifiya Kapasi; Sue E Leurgans; Julie A Schneider; David A Bennett; Konstantinos Arfanakis
Journal:  PLoS One       Date:  2017-12-20       Impact factor: 3.240

9.  MRI-Based Quantification of Magnetic Susceptibility in Gel Phantoms: Assessment of Measurement and Calculation Accuracy.

Authors:  Emma Olsson; Ronnie Wirestam; Emelie Lind
Journal:  Radiol Res Pract       Date:  2018-07-30

10.  The effect of low resolution and coverage on the accuracy of susceptibility mapping.

Authors:  Anita Karsa; Shonit Punwani; Karin Shmueli
Journal:  Magn Reson Med       Date:  2018-10-19       Impact factor: 4.668

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