BSLIM: Spectral Localization by Imaging With Explicit $B_{0}$ Field Inhomogeneity Compensation

Abstract

Abstract-Magnetic resonance spectroscopy imaging (MRSI) is an attractive tool for medical imaging. However, its practical use is often limited by the intrinsic low spatial resolution and long acquisition time. Spectral localization by imaging (SLIM) has been proposed as a non-Fourier reconstruction algorithm that incorporates spatial a priori information about spectroscopically uniform compartments. Unfortunately, the influence of the magnetic field inhomogeneity-in particular, the susceptibility effects at tissues' boundaries-undermines the validity of the compartmental model. Therefore, we propose BSLIM as an extension of SLIM with field inhomogeneity compensation. A 0 -field inhomogeneity map, which can be acquired rapidly and at high resolution, is used by the new algorithm as additional a priori information. We show that the proposed method is distinct from the generalized SLIM (GSLIM) framework. Experimental results of a two-compartment phantom demonstrate the feasibility of the method and the importance of inhomogeneity compensation.Index Terms-Chemical shift imaging, constrained reconstruction, magnetic field inhomogeneity, magnetic resonance spectroscopy imaging (MRSI).

B0-adjusted and sensitivity-encoded spectral localization by imaging (BASE-SLIM) in the human brain in vivo

“…This issue has been partially addressed by various extensions of SLIM, including GSLIM (Liang and Lauterbur, 1991), SLOOP (Vonkienlin and Mejia, 1991), SPLASH (An et al, 2011), BSLIM (Khalidov et al, 2007), NL-CSI (Bashir and Yablonskiy, 2006), and SPREAD (Dong and Peterson, 2009). Among proposed extensions of SLIM, only a few have demonstrated its application to the human brain in a limited scope, with no demonstration of a compartmental separation between GM and WM to date.…”

Imaging based magnetic resonance spectroscopy (MRS) localization for quantitative neurochemical analysis and cerebral metabolism studies

“…Various approaches have been proposed to overcome the limitation of SLIM and extend its capabilities. These approaches include GSLIM [51], SLOOP [52], BSLIM [53], SPLASH [54], SLAM [55, 56], starSLIM [57], and BASE-SLIM [58, 59]. …”

Data-Driven MRSI Spectral Localization Via Low-Rank Component Analysis

“…Recently, numerous techniques have been proposed which address this issue, for example, by considering higher-order functionals [32]- [35]. In this work, we adopt the method of [32], referred to as the total generalized variation of second order ( ), which offers a compromise between penalizing on the first and second derivatives, where (14) or its discrete analog (15) and denotes the symmetrized derivative. In our experiments, the discretized gradient operator is realized using forward finite differences with Neumann boundary conditions.…”

“…Once the compartment geometry has been specified, normally with the aid of additional high-resolution anatomical images, finding the original spectra amounts to solving a linear least squares (LS) problem. A number of extensions to SLIM have since been proposed, including [13], which extends the basic SLIM model as a generalized series, and BSLIM [14], which seeks to compensate the estimated spectra for local variations in the static magnetic field due to tissue susceptibility. Other recent model-based approaches include [15], [16], which further exploit notions such as sparsity and more flexible basis sets.…”