Fan Lam1,2,3, Bradley P Sutton1,2,3. 1. Department of Bioengineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA. 2. Beckman Institute for Advanced Science and Technology, University of Illinois at Urbana-Champaign, Champaign, IL, USA. 3. Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, Champaign, IL, USA.
Abstract
PURPOSE: To present a general and efficient method for macroscopic intravoxel B 0 inhomogeneity corrected reconstruction from multi-TE acquisitions. THEORY AND METHODS: A signal encoding model for multi-TE gradient echo (GRE) acquisitions that incorporates 3D intravoxel B 0 field variations is derived, and a low-rank approximation to the encoding operator is introduced under piecewise linear B 0 assumption. The low-rank approximation enables very efficient computation and memory usage, and allows the proposed signal model to be integrated into general inverse problem formulations that are compatible with multi-coil and undersampling acquisitions as well as different regularization functions. RESULTS: Experimental multi-echo GRE data were acquired to evaluate the proposed method. Effective reduction of macroscopic intravoxel B 0 inhomogeneity induced artifacts was demonstrated. Improved R 2 ∗ estimation from the corrected reconstruction over standard Fourier reconstruction has also been obtained. CONCLUSIONS: The proposed method can effectively correct the effects of intravoxel B 0 inhomogeneity, and can be useful for various imaging applications involving GRE-based acquisitions, including fMRI, quantitative R 2 ∗ and susceptibility mapping, and MR spectroscopic imaging.
PURPOSE: To present a general and efficient method for macroscopic intravoxel B 0 inhomogeneity corrected reconstruction from multi-TE acquisitions. THEORY AND METHODS: A signal encoding model for multi-TE gradient echo (GRE) acquisitions that incorporates 3D intravoxel B 0 field variations is derived, and a low-rank approximation to the encoding operator is introduced under piecewise linear B 0 assumption. The low-rank approximation enables very efficient computation and memory usage, and allows the proposed signal model to be integrated into general inverse problem formulations that are compatible with multi-coil and undersampling acquisitions as well as different regularization functions. RESULTS: Experimental multi-echo GRE data were acquired to evaluate the proposed method. Effective reduction of macroscopic intravoxel B 0 inhomogeneity induced artifacts was demonstrated. Improved R 2 ∗ estimation from the corrected reconstruction over standard Fourier reconstruction has also been obtained. CONCLUSIONS: The proposed method can effectively correct the effects of intravoxel B 0 inhomogeneity, and can be useful for various imaging applications involving GRE-based acquisitions, including fMRI, quantitative R 2 ∗ and susceptibility mapping, and MR spectroscopic imaging.