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Reconstructing brain magnetic susceptibility distributions from T2* phase images by TV-regularized 2-subproblem split Bregman iterations

Authors Chen Z, Calhoun V

Received 14 September 2013

Accepted for publication 4 November 2013

Published 24 March 2014 Volume 2014:7 Pages 41—53

DOI https://doi.org/10.2147/RMI.S54514

Checked for plagiarism Yes

Review by Single-blind

Peer reviewer comments 5


Zikuan Chen,1 Vince D Calhoun1,2

1The Mind Research Network and LBERI, Albuquerque, NM, USA; 2University of New Mexico, ECE Dept, Albuquerque, NM, USA

Abstract: The underlying source of brain imaging by T2*-weighted magnetic resonance imaging (T2*MRI) is mainly due to the intracranial inhomogeneous magnetic susceptibility distribution (denoted by χ). We can reconstruct the source χ by two computational steps: first, calculate a fieldmap from a T2* phase image and then second, calculate a χ map from the fieldmap. The internal χ distribution reconstruction from observed T2* phase images is termed χ tomography, which connotes the digital source reproduction with spatial conformance by solving inverse problems in the context of medical imaging. In the small phase angle regime, the T2* phase image remains unwrapped (−π<phase angle<π) and it is linearly related to the fieldmap by a scaling factor. However, the second inverse step (calculating a χ map from a fieldmap) is a severely ill-posed 3D deconvolution problem due to an unusual bipolar-valued kernel (dipole field kernel). We have reported on a 3-subproblem split Bregman iteration algorithm for total variation-regularized 3D χ reconstruction; in this paper, we report on a 2-subproblem split Bregman iteration algorithm with easy implementation. We validate the 3D χ tomography algorithms by numerical simulations and phantom experiments. We also demonstrate the feasibility of 3D χ tomography for obtaining in vivo brain χ states at 2 mm spatial resolution.

Keywords: T2*-weighted MRI (T2*MRI), magnetic susceptibility tomography (χ tomography), dipole effect, 3D deconvolution, filter truncation, total variation (TV), split Bregman iteration, computed inverse magnetic resonance imaging (CIMRI)

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