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Proceedings Paper

Sparse methods for Quantitative Susceptibility Mapping
Author(s): Berkin Bilgic; Itthi Chatnuntawech; Christian Langkammer; Kawin Setsompop
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Paper Abstract

Quantitative Susceptibility Mapping (QSM) aims to estimate the tissue susceptibility distribution that gives rise to subtle changes in the main magnetic field, which are captured by the image phase in a gradient echo (GRE) experiment. The underlying susceptibility distribution is related to the acquired tissue phase through an ill-posed linear system. To facilitate its inversion, spatial regularization that imposes sparsity or smoothness assumptions can be employed. This paper focuses on efficient algorithms for regularized QSM reconstruction. Fast solvers that enforce sparsity under Total Variation (TV) and Total Generalized Variation (TGV) constraints are developed using Alternating Direction Method of Multipliers (ADMM). Through variable splitting that permits closed-form iterations, the computation efficiency of these solvers are dramatically improved. An alternative approach to improve the conditioning of the ill-posed inversion is to acquire multiple GRE volumes at different head orientations relative to the main magnetic field. The phase information from such multi-orientation acquisition can be combined to yield exquisite susceptibility maps and obviate the need for regularized reconstruction, albeit at the cost of increased data acquisition time.

Paper Details

Date Published: 11 September 2015
PDF: 10 pages
Proc. SPIE 9597, Wavelets and Sparsity XVI, 959711 (11 September 2015); doi: 10.1117/12.2188535
Show Author Affiliations
Berkin Bilgic, Martinos Ctr. for Biomedical Imaging (United States)
Itthi Chatnuntawech, Massachusetts Institute of Technology (United States)
Christian Langkammer, Medical Univ. of Graz (Austria)
Kawin Setsompop, Martinos Ctr. for Biomedical Imaging (United States)


Published in SPIE Proceedings Vol. 9597:
Wavelets and Sparsity XVI
Manos Papadakis; Vivek K. Goyal; Dimitri Van De Ville, Editor(s)

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