Natural Linewidth Chemical Shift Imaging (NL-CSI)

Magnetic resonance spectroscopy (MRS) has proved to be of considerable value in the detection and management of several diseases, including cancer, multiple sclerosis, and psychiatric disorders (1-5). The direct biochemical information that is provided by MRS augments our understanding of the underlying disease mechanisms. There are two major factors that limit the usefulness of in vivo spectroscopy: 1) the signal-to-noise ratio (SNR) and 2) the quality and reliability of spectral localization. In the chemical shift imaging (CSI) method, a set of free induction decay (FID) signals is acquired and phase encoding is performed in the relevant spatial dimensions (6). Two issues limit the spatial resolution in CSI experiments: First, the clinically relevant metabolites occur in low concentrations in vivo and cannot be discriminated from noise when a conventional MR image resolution is used. Second, the need to phase encode in two dimensions makes the data acquisition time excessively long if data are obtained at high spatial resolution (7). Therefore, to improve SNR and keep experimental time within reasonable limits, MR spectroscopic images are often collected and reconstructed from a small set of low spatial frequencies, e.g., a typical 2D (two spatial dimensions and one spectral dimension) CSI experiment can have a spatial matrix of 16 × 16 or 32 × 32. The most common approach to spatially reconstruct phase-encoded CSI data is the discrete Fourier transform (FT); however, it is well known that the FT is inadequate for truncated data (8). FT reconstruction of spectroscopic imaging data leads to a sinc-like point spread function (PSF) that causes the signal to “ring” away from the main central lobe and contaminate pixels quite distant from the actual source. One way to reduce the ringing caused by truncation is to apodize the k-space data prior to FT. This approach causes blurring, degrades the spatial resolution, and reduces the SNR (per unit time) from optimally achievable levels. Another strategy is to employ a FT series windowing approach whereby a varying number of excitations are acquired based on a filter function, usually Hanning or Blackman functions, and the resulting PSF is free of side lobes (9-12). These windowing functions also change the effective spatial resolution and blur the image, i.e., the spectrum assigned to each voxel is a weighted average of the spectra from neighboring voxels. Several alternatives to filtering have been reported to cope with cross-voxel contamination in MR spectroscopic imaging (MRSI). One such technique, spectral localization by imaging (SLIM), proposed by Hu et al. (13), is used to improve scanning efficiency and eliminate intervoxel signal leakage. This technique uses model-based reconstruction whereby the spatial coordinates of the boundaries of several compartments of interest with arbitrary shape are determined based on a high-resolution MR image. This information is used to reconstruct the compartmental spectra from the spectroscopic signals obtained with a phase-encoding pulse sequence. A modified version, called generalized SLIM (GSLIM), was introduced to reduce the spectral leakage arising from compartmental inhomogeneity errors in SLIM (14). In a similar vein, Von Kienlin and Mejia (15) developed a spatial localization method, termed spectral localization with optimal point spread function (SLOOP), which like SLIM reconstruction also uses prior information available on high-resolution anatomical MR images to reduce spectral leakage. Similarly, Plevritis and Macovski (16,17) showed that by using the Papoulis-Gerchberg algorithm and anatomic information derived from a proton image, one can produce a spectroscopic image that is better resolved than a zero-filled FT image. Various other groups have also tried to address the problem of spectral leakage. For example, Hu et al. (18,19) developed a method in which phase-encoded data are acquired with various resolutions and degrees of signal averaging, i.e., a standard MRS data set is acquired with low spatial resolution and high SNR (many averages), and high-spatial-resolution data are acquired with low SNR (few averages). They developed algorithms to combine these two data sets to improve spectral localization and preserve the SNR in the MRS data sets. Using these approaches, Hu et al. (18,19) showed that they could eliminate the leakage of extracranial lipid signal into brain voxels. However, since the final spectroscopic image is generated by FT, this method and other similar approaches do not address the issue of signal leakage between more closely adjacent brain voxels. Non-Fourier encoding methods, such as wavelet encoding (20), have also been suggested to reduce the ringing artifact associated with Fourier encoding, but their application and use have been limited to date. While the above-mentioned techniques have significantly contributed to improving the quality of CSI results, they do not address another source of spectral leakage and spectral distortion in CSI experiments that, as we will show later, is associated with the magnetic field inhomogeneities present in any MRI or MRS experiment. If a subject is placed in the scanner, magnetic susceptibility variations within tissues and at air/tissue interfaces cause distortions in the magnetic field and introduce inhomogeneities. Some of these inhomogeneities can be removed by shimming the magnetic field, which involves applying small corrective field gradients using a set of specially designed coils for that purpose (21,22). While these coils can partially correct for relatively coarse, low-spatial-frequency field inhomogeneities, they cannot completely eliminate magnetic field inhomogeneities at the interfaces of air, tissue, and bone, where the susceptibility changes rapidly over short distances. MRS studies are increasingly being performed on high-field magnets to improve the amount, quality, and/or specificity of the information content resulting from the increased SNR and chemical shift dispersion of the signals. However, if the increased static magnetic field inhomogeneities experienced at higher fields are not taken into account, they can offset or cancel the SNR and chemical shift dispersion advantages. Given the inherent low resolution (large voxel size) of spectroscopic imaging, it should be recognized that even after careful shimming these magnetic field variations may be substantial over the extent of the voxel dimensions. In this paper we show that the presence of magnetic field inhomogeneities can cause severe spectral leakage and distortion in a CSI experiment. We also demonstrate a new method that eliminates both these artifacts. First, we outline the theory and demonstrate by simulations the spectral leakage effect due to the field inhomogeneities in a CSI experiment. Importantly, we demonstrate that such spectral leakage is present in both FT reconstruction and non-Fourier-based SLIM reconstruction. We then introduce a new method to eliminate the effect of magnetic field inhomogeneity-induced spectral leakage on spectra from all selected voxels. We show through simulations using this new approach that ideal (noncontaminated) spectra can be reconstructed in all selected voxels. Phantom and in vivo human experiments are also described that demonstrate the effect of spectral leakage and distortion of spectra due to magnetic field inhomogeneities, and confirm the feasibility of this newly proposed method to reduce this effect. To simplify the presentation and analysis, all simulations and results are presented for 1D CSI (one spatial dimension); however, the technique can be easily extended to higher dimensions.