Natural linewidth chemical shift imaging (NL‐CSI)

Abstract

The discrete Fourier transform (FT) is a conventional method for spatial reconstruction of chemical shifting imaging (CSI) data. Due to point spread function (PSF) effects, FT reconstruction leads to intervoxel signal leakage (Gibbs ringing). Spectral localization by imaging (SLIM) reconstruction was previously proposed to overcome this intervoxel signal contamination. However, the existence of magnetic field inhomogeneities creates an additional source of intervoxel signal leakage. It is demonstrated herein that even small field inhomogeneities substantially amplify intervoxel signal leakage in both FT and SLIM reconstruction approaches. A new CSI data acquisition strategy and reconstruction algorithm (natural linewidth (NL) CSI) is presented that eliminates effects of magnetic field inhomogeneity-induced intervoxel signal leakage and intravoxel phase dispersion on acquired data. The approach is based on acquired CSI data, high-resolution images, and magnetic field maps. The data are reconstructed based on the imaged object structure (as in the SLIM approach) and a reconstruction matrix that takes into account the inhomogeneous field distribution inside anatomically homogeneous compartments. Phantom and in vivo results show that the new method allows field inhomogeneity effects from the acquired MR signal to be removed so that the signal decay is determined only by the "natural" R 2 relaxation rate constant (hence the term "natural linewidth" CSI). Magn Reson Med 56:7-18, 2006.

Image‐guided spatial localization of heterogeneous compartments for magnetic resonance

“…The compartmental spectra can be obtained by a generalized least square solution given in Eqs. (8) and (9). The unfolding matrix F is independent of frequency ν and thus only needs to be computed once for all resonance frequencies.…”

“…The optimal λ/λ 0 value was used in Eq. (9) to compute compartmental spectra for 200 noise realizations. Thereafter, the mean relative error and the relative error for the averaged compartmental spectra were computed.…”

“…7 Several strategies have been proposed to incorporate intracompartment heterogeneity in SLIM reconstruction using multiple phase encoding steps. [8][9][10] In this work, we introduce Tikhonov regularization 11 to address the issue of intracompartment heterogeneity and demonstrate its effectiveness in SPLASH. This regularized SPLASH method allows rapid spatial localization of irregularly shaped anatomical compartments with heterogeneous biological signal distribution without using any phase encoding gradients.…”

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

“…The NL-CSI method of Yablonskiy et al [29] is based on the same equation (6). However, the authors suggest a different experimental problem setting (1-D CSI voxel sequence, a significant number of compartments each corresponding to a voxel), which leads to a different approach that involves regularization.…”

“…In the last equation, we introduced the local spectral shift as 1 A preliminary version of this work was presented at MedIm 2006 [28]. During the first review cycle of the present manuscript, a paper that describes a similar approach appeared in press [29]. There, the low-resolution voxels are considered as compartments and regularization is used to make the inverse problem well-posed.…”