Improved Reconstruction for MR Spectroscopic Imaging

Abstract

Sensitivity limitations of in vivo magnetic resonance spectroscopic imaging (MRSI) require that the extent of spatial-frequency (k-space) sampling be limited, thereby reducing spatial resolution and increasing the effects of Gibbs ringing that is associated with the use of Fourier transform reconstruction. Additional problems occur in the spectral dimension, where quantitation of individual spectral components is made more difficult by the typically low signal-to-noise ratios, variable lineshapes, and baseline distortions, particularly in areas of significant magnetic field inhomogeneity. Given the potential of in vivo MRSI measurements for a number of clinical and biomedical research applications, there is considerable interest in improving the quality of the metabolite image reconstructions. In this report, a reconstruction method is described that makes use of parametric modeling and MRI-derived tissue distribution functions to enhance the MRSI spatial reconstruction. Additional preprocessing steps are also proposed to avoid difficulties associated with image regions containing spectra of inadequate quality, which are commonly present in the in vivo MRSI data. KeywordsBrain metabolite images; image reconstruction; magnetic resonance spectroscopic imaging (MRSI)

Patch-Based Super-Resolution of MR Spectroscopic Images: Application to Multiple Sclerosis

“…The second sub-group of methods are based on Bayesian theory and model the reconstruction in the k-space as a likelihood function where the anatomical information acts as prior knowledge to estimate the optimized model parameters via the expectation-maximization algorithm. For example in Bao and Maudsley ( 2007 ), the likelihood function consists of a combined spectral-spatial model where the tissue segmentations acts as prior information in estimating additional high frequencies in k-space. As an extension of this work, the likelihood model in Kornak et al ( 2010 ) also addresses the spectral fitting problems and additionally uses prior information on the relationship between tissue segmentation and spatial metabolite distribution.…”

In vivo quantitation of metabolites with an incomplete model function

“…The faster the decay, the broader the lines. Under in vivo conditions, the dominant cause of decay is often tissue heterogeneity in the patient under investigation, especially at high magnetic fields; see examples in, e.g., [7][8][9][10][11][12][13][14][15][16][17]. Tissue heterogeneity, in turn, causes the magnetic field to be inhomogeneous.…”