Share：
Share this content in WeChat
X
[Chinese][PDF]813210
• Technical Article •
Field map-based rectification of susceptibility distortion and signal compensation in diffusion tensor imaging
KANG Tai-shan  YANG Tian-he  LIN Jian-zhong  ZHANG Jia-xing 

DOI:10.12015/issn.1674-8034.2017.08.010.


[Abstract] Objective: This study was designed to employ the voxel-based field map to rectify the geometric deformation and compensate the signal loss of diffusion tensor imaging (DTI), and thus facilitate the studies and clinical applications of DTI.Materials and Methods: Brain field maps from 29 healthy persons were first used to get B1 field heterogeneous signals, and then the compensation and phase disconsolation of signals were performed. Finally, the geometry deformations of DTI were rectified and registered to 3D images. Magnetic sensitive bilateral temporal lobes and frontal lobes were selected as regions of interesting and meanwhile, magnetic insensitive thalamus was selected as control area.Results: Geometry deformations of DTI produced by different susceptibilities between specific tissues were completely rectified, and thus the signal loss was compensated and the accuracy of DTI was significantly enhanced.Conclusion: Signal compensations and deformation rectifications can be well achieved using field map, which may improve the applications of DTI in neurosurgery.
[Keywords] Diffusion tensor imaging；Deformation rectification；Field map；Signal compensation

KANG Tai-shan Department of MRI, Zhongshan Affiliated Hospital of Xiamen University, Xiamen 361004, China

YANG Tian-he* Department of MRI, Zhongshan Affiliated Hospital of Xiamen University, Xiamen 361004, China

LIN Jian-zhong Department of MRI, Zhongshan Affiliated Hospital of Xiamen University, Xiamen 361004, China

ZHANG Jia-xing Institute of Brain Diseases and Cognition, Medical College of Xiamen University, Xiamen 361102, China

*Correspondence to: Yang TH, E-mail: yth13606916211@163.com

Conflicts of interest   None.

ACKNOWLEDGMENTS  This work was part of General Program of National Natural Science Foundation of China No.81471630
Received  2017-04-14
Accepted  2017-06-25
DOI: 10.12015/issn.1674-8034.2017.08.010
DOI:10.12015/issn.1674-8034.2017.08.010.

[1]
Shahar T, Rozovski U, Marko NF, et al. Preoperative imaging to predict intraoperative changes in tumor-to-corticospinal tract distance: an analysis of 45 cases using high-field intraoperative magnetic resonance imaging. Neurosurgery, 2014, 75(1): 23-30.
[2]
Kockro RA, Reisch R, Serra L, et al. Image-guided neurosurgery with 3-dimensional multimodal imaging data on a stereoscopic monitor. Neurosurgery, 2013, 72(Suppl 1): 78-88.
[3]
Yin JZ. Common artifacts in 3.0 T high-field MRI systems: principle, appearance and remedy. Chin J Magn Reson Imaging, 2010, 1(4): 291-294.尹建忠．3.0 T高场磁共振设备的常见伪影：原理、表现与对策．磁共振成像，2010，1(4)：291-294．
[4]
Irfanoglu MO, Walker L, Sarlls J, et al. Effects of image distortions originating from susceptibility variations and concomitant fields on diffusion MRI tractography results. Neuroimage, 2012, 61(1): 275-288.
[5]
Robson MD, Gore JC, Constable RT. Measurement of the point spread function in MRI using constant time imaging. Magn Reson Med, 1997, 38(5): 733-740.
[6]
Jezzard P, Balaban RS. Correction for geometric distortion in echo planar images from B0 field variations. Magn Reson Med, 1995, 34(1): 65-73.
[7]
Hutton C, Bork A, Josephs O, et al. Image distortion correction in fMRI: A quantitative evaluation. Neuroimage, 2002, 16(1): 217-240.
[8]
Rorden C, Bonilha L, Fridriksson J, et al. Age-specific CT and MRI templates for spatial normalization. Neuroimage, 2012, 61(4): 957-965.
[9]
Huang Q, Zhang R, Hu X, et al. Disturbed small-world networks and neurocognitive function in frontal lobe low-grade glioma patients. PloS One, 2014, 9(4): 94095.
[10]
Huang YT, He CM. Evaluation methodology of MRI SNR. Chin J Magn Reson Imaging, 2012, 3(2): 149-152.黄艳图，何超明．磁共振成像信噪比的评价方法．磁共振成像，2012，3(2)：149-152．
[11]
Jones DK, Leemans A. Diffusion tensor imaging. Methods Mol Biol,2011, 711(711): 127.
[12]
Han T, Cui SM, Tong XG, et al. Three-dimensional visualization of functional brain tissue and functional magnetic resonance imaging-integrated neuronavigation in the resection of brain tumor adjacent to motor cortex. Int J Med Radiol, 2011, 34(3): 205-210.韩彤，崔世民，佟小光，等．大脑功能组织可视化及fMRI术中导航在脑肿瘤切除术中的应用．国际医学放射学杂志，2011，34(3)：205-210．
[13]
Gupta A, Escolar M, Dietrich C, et al. 3D Tensor normalization for improved accuracy in DTI tensor registration methods//biomedical image registration. Berlin: Springer Berlin Heidelberg, 2012: 170-179.
[14]
Wang HY, Zhao B, Yu FH, et al. The principle of diffusion tensor imaging techniques and comparison. J Med Imaging, 2006, 16(4): 402-404.王海燕，赵斌，于富华，等．DTI常用扫描序列原理及比较．医学影像学杂志，2006，16(4)：402-404．
[15]
Wang Y, Xie K, Zhou YJ, et al. Graph cut based algorithm for corpus callosum segmentation from diffusion tensor images. J Beijing Polytechnic University, 2014, 40(3): 473-480.王毅，谢琨，周艳娟，等．基于图割的扩散张量磁共振图像胼胝体分割算法．北京工业大学学报，2014，40(3)：473-480．
[16]
He XX. Imaging study of focal cortical dysplasia based on magnetic resonance imaging. Hefei: University of Science and Technology of China, 2016.何晓璇．基于磁共振结构像的局灶性皮质发育不良的影像学研究．合肥：中国科学技术大学，2016．

PREV Preparation of folate-mediated superparamagnetic iron oxide-dopamine-folate and its in vitro MR imaging
NEXT Application and sequence optimization of MRI subtraction in showing trigeminal nerve and blood vessels
  


LINK


Tel & Fax: +8610-67113815    E-mail: editor@cjmri.cn