Literature DB >> 29700796

Susceptibility difference weighted imaging in vertical-field MRI.

Ryota Sato1, Toru Shirai2, Yo Taniguchi2, Takenori Murase3, Yoshitaka Bito3, Yoshihisa Soutome2, Hisaaki Ochi2.   

Abstract

To realize susceptibility-weighted imaging in vertical-field magnetic resonance imaging (MRI), we developed an image-processing method called "susceptibility difference weighted imaging" (SDWI). In SDWI, contrasts are enhanced using a susceptibility map calculated by using a weighted least-square algorithm with a small iteration number. Experiments were performed on human volunteers to compare image contrast obtained from the conventional method (SWI) and SDWI. In horizontal-field MRI, SDWI results show that veins and deep-gray-matter nuclei were visualized as well as those with SWI. In vertical-field MRI, SDWI visualized veins and deep-gray-matter nuclei without severe streaking artifacts, while SWI did not. In our experiments, the time taken to calculate the susceptibility map in SDWI was less than 10 s. The results indicate that susceptibility-weighted imaging is feasible in vertical-field MRI using SDWI.

Entities:  

Keywords:  Quantitative susceptibility mapping; Susceptibility difference weighted imaging; Susceptibility-weighted imaging; Vertical-field MRI

Mesh:

Year:  2018        PMID: 29700796     DOI: 10.1007/s12194-018-0458-1

Source DB:  PubMed          Journal:  Radiol Phys Technol        ISSN: 1865-0333


  12 in total

1.  Susceptibility weighted imaging (SWI).

Authors:  E Mark Haacke; Yingbiao Xu; Yu-Chung N Cheng; Jürgen R Reichenbach
Journal:  Magn Reson Med       Date:  2004-09       Impact factor: 4.668

2.  The role of voxel aspect ratio in determining apparent vascular phase behavior in susceptibility weighted imaging.

Authors:  Yingbiao Xu; E Mark Haacke
Journal:  Magn Reson Imaging       Date:  2005-12-27       Impact factor: 2.546

3.  Susceptibility map-weighted imaging (SMWI) for neuroimaging.

Authors:  Sung-Min Gho; Chunlei Liu; Wei Li; Ung Jang; Eung Yeop Kim; Dosik Hwang; Dong-Hyun Kim
Journal:  Magn Reson Med       Date:  2013-09-04       Impact factor: 4.668

4.  Improved MR venography using quantitative susceptibility-weighted imaging.

Authors:  Saifeng Liu; Karen Mok; Jaladhar Neelavalli; Yu-Chung N Cheng; Jin Tang; Yongquan Ye; E Mark Haacke
Journal:  J Magn Reson Imaging       Date:  2013-10-31       Impact factor: 4.813

Review 5.  Susceptibility-weighted imaging: current status and future directions.

Authors:  Saifeng Liu; Sagar Buch; Yongsheng Chen; Hyun-Seok Choi; Yongming Dai; Charbel Habib; Jiani Hu; Joon-Yong Jung; Yu Luo; David Utriainen; Meiyun Wang; Dongmei Wu; Shuang Xia; E Mark Haacke
Journal:  NMR Biomed       Date:  2016-05-18       Impact factor: 4.044

Review 6.  Susceptibility-weighted imaging: technical aspects and clinical applications, part 2.

Authors:  S Mittal; Z Wu; J Neelavalli; E M Haacke
Journal:  AJNR Am J Neuroradiol       Date:  2009-01-08       Impact factor: 3.825

Review 7.  Susceptibility-weighted imaging: technical aspects and clinical applications, part 1.

Authors:  E M Haacke; S Mittal; Z Wu; J Neelavalli; Y-C N Cheng
Journal:  AJNR Am J Neuroradiol       Date:  2008-11-27       Impact factor: 3.825

8.  Nonlinear regularization for per voxel estimation of magnetic susceptibility distributions from MRI field maps.

Authors:  Bryan Kressler; Ludovic de Rochefort; Tian Liu; Pascal Spincemaille; Quan Jiang; Yi Wang
Journal:  IEEE Trans Med Imaging       Date:  2009-06-05       Impact factor: 10.048

9.  Quantitative Susceptibility Mapping Using the Multiple Dipole-inversion Combination with k-space Segmentation Method.

Authors:  Ryota Sato; Toru Shirai; Yo Taniguchi; Takenori Murase; Yoshitaka Bito; Hisaaki Ochi
Journal:  Magn Reson Med Sci       Date:  2017-03-27       Impact factor: 2.471

10.  Noise Effects in Various Quantitative Susceptibility Mapping Methods.

Authors:  Shuai Wang; Tian Liu; Weiwei Chen; Pascal Spincemaille; Cynthia Wisnieff; A John Tsiouris; Wenzhen Zhu; Chu Pan; Lingyun Zhao; Yi Wang
Journal:  IEEE Trans Biomed Eng       Date:  2013-06-07       Impact factor: 4.538

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