Computed inverse resonance imaging for magnetic susceptibility map reconstruction

Zikuan Chen, Vince Calhoun

Research output: Contribution to journalArticle

Abstract

OBJECTIVE: This article reports a computed inverse magnetic resonance imaging (CIMRI) model for reconstructing the magnetic susceptibility source from MRI data using a 2-step computational approach. METHODS: The forward T2*-weighted MRI (T2*MRI) process is broken down into 2 steps: (1) from magnetic susceptibility source to field map establishment via magnetization in the main field and (2) from field map to MR image formation by intravoxel dephasing average. The proposed CIMRI model includes 2 inverse steps to reverse the T2*MRI procedure: field map calculation from MR-phase image and susceptibility source calculation from the field map. The inverse step from field map to susceptibility map is a 3-dimensional ill-posed deconvolution problem, which can be solved with 3 kinds of approaches: the Tikhonov-regularized matrix inverse, inverse filtering with a truncated filter, and total variation (TV) iteration. By numerical simulation, we validate the CIMRI model by comparing the reconstructed susceptibility maps for a predefined susceptibility source. RESULTS: Numerical simulations of CIMRI show that the split Bregman TV iteration solver can reconstruct the susceptibility map from an MR-phase image with high fidelity (spatial correlation ≈ 0.99). The split Bregman TV iteration solver includes noise reduction, edge preservation, and image energy conservation. For applications to brain susceptibility reconstruction, it is important to calibrate the TV iteration program by selecting suitable values of the regularization parameter. CONCLUSIONS: The proposed CIMRI model can reconstruct the magnetic susceptibility source of T2*MRI by 2 computational steps: calculating the field map from the phase image and reconstructing the susceptibility map from the field map. The crux of CIMRI lies in an ill-posed 3-dimensional deconvolution problem, which can be effectively solved by the split Bregman TV iteration algorithm.

Original languageEnglish (US)
Pages (from-to)265-274
Number of pages10
JournalJournal of computer assisted tomography
Volume36
Issue number2
DOIs
StatePublished - Mar 1 2012
Externally publishedYes

Keywords

  • 3-dimensional deconvolution
  • T*-weighted MRI
  • computed inverse MRI
  • filter truncation
  • matrix inverse
  • split Bregman iteration
  • susceptibility mapping
  • total variation regularization

ASJC Scopus subject areas

  • Radiology Nuclear Medicine and imaging

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