Literature DB >> 26313885

Streaking artifact reduction for quantitative susceptibility mapping of sources with large dynamic range.

Hongjiang Wei1, Russell Dibb2, Yan Zhou3, Yawen Sun3, Jianrong Xu3, Nian Wang1, Chunlei Liu1,4.   

Abstract

Quantitative susceptibility mapping (QSM) is a novel MRI technique for the measurement of tissue magnetic susceptibility in three dimensions. Although numerous algorithms have been developed to solve this ill-posed inverse problem, the estimation of susceptibility maps with a wide range of values is still problematic. In cases such as large veins, contrast agent uptake and intracranial hemorrhages, extreme susceptibility values in focal areas cause severe streaking artifacts. To enable the reduction of these artifacts, whilst preserving subtle susceptibility contrast, a two-level QSM reconstruction algorithm (streaking artifact reduction for QSM, STAR-QSM) was developed in this study by tuning a regularization parameter to automatically reconstruct both large and small susceptibility values. Compared with current state-of-the-art QSM methods, such as the improved sparse linear equation and least-squares (iLSQR) algorithm, STAR-QSM significantly reduced the streaking artifacts, whilst preserving the sharp boundaries for blood vessels of mouse brains in vivo and fine anatomical details of high-resolution mouse brains ex vivo. Brain image data from patients with cerebral hematoma and multiple sclerosis further illustrated the superiority of this method in reducing streaking artifacts caused by large susceptibility sources, whilst maintaining sharp anatomical details. STAR-QSM is implemented in STI Suite, a comprehensive shareware for susceptibility imaging and quantification.
Copyright © 2015 John Wiley & Sons, Ltd.

Entities:  

Keywords:  cerebral hematoma; multiple sclerosis; quantitative susceptibility mapping; streaking artifact reduction

Mesh:

Year:  2015        PMID: 26313885      PMCID: PMC4572914          DOI: 10.1002/nbm.3383

Source DB:  PubMed          Journal:  NMR Biomed        ISSN: 0952-3480            Impact factor:   4.044


  34 in total

1.  Nonlinear formulation of the magnetic field to source relationship for robust quantitative susceptibility mapping.

Authors:  Tian Liu; Cynthia Wisnieff; Min Lou; Weiwei Chen; Pascal Spincemaille; Yi Wang
Journal:  Magn Reson Med       Date:  2012-04-09       Impact factor: 4.668

2.  Intracranial calcifications and hemorrhages: characterization with quantitative susceptibility mapping.

Authors:  Weiwei Chen; Wenzhen Zhu; Iihami Kovanlikaya; Arzu Kovanlikaya; Tian Liu; Shuai Wang; Carlo Salustri; Yi Wang
Journal:  Radiology       Date:  2013-10-28       Impact factor: 11.105

3.  Fast quantitative susceptibility mapping using 3D EPI and total generalized variation.

Authors:  Christian Langkammer; Kristian Bredies; Benedikt A Poser; Markus Barth; Gernot Reishofer; Audrey Peiwen Fan; Berkin Bilgic; Franz Fazekas; Caterina Mainero; Stefan Ropele
Journal:  Neuroimage       Date:  2015-02-27       Impact factor: 6.556

4.  Background field removal using spherical mean value filtering and Tikhonov regularization.

Authors:  Hongfu Sun; Alan H Wilman
Journal:  Magn Reson Med       Date:  2014-03       Impact factor: 4.668

5.  Improving susceptibility mapping using a threshold-based K-space/image domain iterative reconstruction approach.

Authors:  J Tang; S Liu; J Neelavalli; Y C N Cheng; S Buch; E M Haacke
Journal:  Magn Reson Med       Date:  2012-06-26       Impact factor: 4.668

6.  Microstructural origins of gadolinium-enhanced susceptibility contrast and anisotropy.

Authors:  Russell Dibb; Wei Li; Gary Cofer; Chunlei Liu
Journal:  Magn Reson Med       Date:  2014-01-17       Impact factor: 4.668

7.  Susceptibility-based analysis of dynamic gadolinium bolus perfusion MRI.

Authors:  David Bonekamp; Peter B Barker; Richard Leigh; Peter C M van Zijl; Xu Li
Journal:  Magn Reson Med       Date:  2014-02-25       Impact factor: 4.668

8.  Fast quantitative susceptibility mapping with L1-regularization and automatic parameter selection.

Authors:  Berkin Bilgic; Audrey P Fan; Jonathan R Polimeni; Stephen F Cauley; Marta Bianciardi; Elfar Adalsteinsson; Lawrence L Wald; Kawin Setsompop
Journal:  Magn Reson Med       Date:  2013-11-20       Impact factor: 4.668

9.  A method for estimating and removing streaking artifacts in quantitative susceptibility mapping.

Authors:  Wei Li; Nian Wang; Fang Yu; Hui Han; Wei Cao; Rebecca Romero; Bundhit Tantiwongkosi; Timothy Q Duong; Chunlei Liu
Journal:  Neuroimage       Date:  2014-12-20       Impact factor: 6.556

10.  Functional quantitative susceptibility mapping (fQSM).

Authors:  Dávid Z Balla; Rosa M Sanchez-Panchuelo; Samuel J Wharton; Gisela E Hagberg; Klaus Scheffler; Susan T Francis; Richard Bowtell
Journal:  Neuroimage       Date:  2014-06-17       Impact factor: 6.556

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  60 in total

Review 1.  Lorentzian effects in magnetic susceptibility mapping of anisotropic biological tissues.

Authors:  Dmitriy A Yablonskiy; Alexander L Sukstanskii
Journal:  J Magn Reson       Date:  2018-04-26       Impact factor: 2.229

2.  Nonlinear dipole inversion (NDI) enables robust quantitative susceptibility mapping (QSM).

Authors:  Daniel Polak; Itthi Chatnuntawech; Jaeyeon Yoon; Siddharth Srinivasan Iyer; Carlos Milovic; Jongho Lee; Peter Bachert; Elfar Adalsteinsson; Kawin Setsompop; Berkin Bilgic
Journal:  NMR Biomed       Date:  2020-02-20       Impact factor: 4.044

3.  Ultrashort echo time quantitative susceptibility mapping (UTE-QSM) for detection of hemosiderin deposition in hemophilic arthropathy: A feasibility study.

Authors:  Hyungseok Jang; Annette von Drygalski; Jonathan Wong; Jenny Y Zhou; Peter Aguero; Xing Lu; Xin Cheng; Scott T Ball; Yajun Ma; Eric Y Chang; Jiang Du
Journal:  Magn Reson Med       Date:  2020-07-14       Impact factor: 4.668

4.  Direct visualization of deep brain stimulation targets in patients with Parkinson's disease via 3-T quantitative susceptibility mapping.

Authors:  Kaijia Yu; Zhiwei Ren; Jianyu Li; Song Guo; Yongsheng Hu; Yongjie Li
Journal:  Acta Neurochir (Wien)       Date:  2021-02-11       Impact factor: 2.216

5.  Background field removal using a region adaptive kernel for quantitative susceptibility mapping of human brain.

Authors:  Jinsheng Fang; Lijun Bao; Xu Li; Peter C M van Zijl; Zhong Chen
Journal:  J Magn Reson       Date:  2017-05-10       Impact factor: 2.229

6.  Investigating magnetic susceptibility of human knee joint at 7 Tesla.

Authors:  Hongjiang Wei; Russell Dibb; Kyle Decker; Nian Wang; Yuyao Zhang; Xiaopeng Zong; Weili Lin; Daniel B Nissman; Chunlei Liu
Journal:  Magn Reson Med       Date:  2017-01-17       Impact factor: 4.668

7.  Quantitative susceptibility mapping of articular cartilage in patients with osteoarthritis at 3T.

Authors:  Hongjiang Wei; Huimin Lin; Le Qin; Steven Cao; Yuyao Zhang; Naying He; Weibo Chen; Fuhua Yan; Chunlei Liu
Journal:  J Magn Reson Imaging       Date:  2018-12-24       Impact factor: 4.813

8.  Multicenter reproducibility of quantitative susceptibility mapping in a gadolinium phantom using MEDI+0 automatic zero referencing.

Authors:  Kofi Deh; Keigo Kawaji; Marjolein Bulk; Louise Van Der Weerd; Emelie Lind; Pascal Spincemaille; Kelly McCabe Gillen; Johan Van Auderkerke; Yi Wang; Thanh D Nguyen
Journal:  Magn Reson Med       Date:  2018-10-04       Impact factor: 4.668

Review 9.  Magnetic susceptibility anisotropy outside the central nervous system.

Authors:  Russell Dibb; Luke Xie; Hongjiang Wei; Chunlei Liu
Journal:  NMR Biomed       Date:  2016-05-16       Impact factor: 4.044

10.  Preconditioned total field inversion (TFI) method for quantitative susceptibility mapping.

Authors:  Zhe Liu; Youngwook Kee; Dong Zhou; Yi Wang; Pascal Spincemaille
Journal:  Magn Reson Med       Date:  2016-07-28       Impact factor: 4.668

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