Xu Li1, Peter C M van Zijl. 1. F.M. Kirby Research Center for Functional Brain Imaging, Kennedy Krieger Institute, Baltimore, Maryland, USA; Department of Radiology, The Johns Hopkins University School of Medicine, Baltimore, Maryland, USA.
Abstract
PURPOSE: An increasing number of studies show that magnetic susceptibility in white matter fibers is anisotropic and may be described by a tensor. However, the limited head rotation possible for in vivo human studies leads to an ill-conditioned inverse problem in susceptibility tensor imaging (STI). Here we suggest the combined use of limiting the susceptibility anisotropy to white matter and imposing morphology constraints on the mean magnetic susceptibility (MMS) for regularizing the STI inverse problem. METHODS: The proposed MMS regularized STI (MMSR-STI) method was tested using computer simulations and in vivo human data collected at 3T. The fiber orientation estimated from both the STI and MMSR-STI methods was compared to that from diffusion tensor imaging (DTI). RESULTS: Computer simulations show that the MMSR-STI method provides a more accurate estimation of the susceptibility tensor than the conventional STI approach. Similarly, in vivo data show that use of the MMSR-STI method leads to a smaller difference between the fiber orientation estimated from STI and DTI for most selected white matter fibers. CONCLUSION: The proposed regularization strategy for STI can improve estimation of the susceptibility tensor in white matter.
PURPOSE: An increasing number of studies show that magnetic susceptibility in white matter fibers is anisotropic and may be described by a tensor. However, the limited head rotation possible for in vivo human studies leads to an ill-conditioned inverse problem in susceptibility tensor imaging (STI). Here we suggest the combined use of limiting the susceptibility anisotropy to white matter and imposing morphology constraints on the mean magnetic susceptibility (MMS) for regularizing the STI inverse problem. METHODS: The proposed MMS regularized STI (MMSR-STI) method was tested using computer simulations and in vivo human data collected at 3T. The fiber orientation estimated from both the STI and MMSR-STI methods was compared to that from diffusion tensor imaging (DTI). RESULTS: Computer simulations show that the MMSR-STI method provides a more accurate estimation of the susceptibility tensor than the conventional STI approach. Similarly, in vivo data show that use of the MMSR-STI method leads to a smaller difference between the fiber orientation estimated from STI and DTI for most selected white matter fibers. CONCLUSION: The proposed regularization strategy for STI can improve estimation of the susceptibility tensor in white matter.
Keywords:
MMS; MSA; diffusion tensor imaging; fiber orientation; magnetic susceptibility anisotropy; mean magnetic susceptibility; susceptibility tensor imaging; white matter
Authors: Jeff H Duyn; Peter van Gelderen; Tie-Qiang Li; Jacco A de Zwart; Alan P Koretsky; Masaki Fukunaga Journal: Proc Natl Acad Sci U S A Date: 2007-06-22 Impact factor: 11.205
Authors: Tian Liu; Jing Liu; Ludovic de Rochefort; Pascal Spincemaille; Ildar Khalidov; James Robert Ledoux; Yi Wang Journal: Magn Reson Med Date: 2011-04-04 Impact factor: 4.668
Authors: Jongho Lee; Peter van Gelderen; Li-Wei Kuo; Hellmut Merkle; Afonso C Silva; Jeff H Duyn Journal: Neuroimage Date: 2011-04-22 Impact factor: 6.556
Authors: Berkin Bilgic; Luke Xie; Russell Dibb; Christian Langkammer; Aysegul Mutluay; Huihui Ye; Jonathan R Polimeni; Jean Augustinack; Chunlei Liu; Lawrence L Wald; Kawin Setsompop Journal: Neuroimage Date: 2015-08-12 Impact factor: 6.556
Authors: Xu Li; Daniel M Harrison; Hongjun Liu; Craig K Jones; Jiwon Oh; Peter A Calabresi; Peter C M van Zijl Journal: J Magn Reson Imaging Date: 2015-06-14 Impact factor: 4.813
Authors: Hongjiang Wei; Eric Gibbs; Peida Zhao; Nian Wang; Gary P Cofer; Yuyao Zhang; G Allan Johnson; Chunlei Liu Journal: Magn Reson Med Date: 2017-08-30 Impact factor: 4.668
Authors: Hongjiang Wei; Luke Xie; Russell Dibb; Wei Li; Kyle Decker; Yuyao Zhang; G Allan Johnson; Chunlei Liu Journal: Neuroimage Date: 2016-05-12 Impact factor: 6.556