Comprehensive processing, display and analysis for in vivo MR spectroscopic imaging

Abstract

Image reconstruction for magnetic resonance spectroscopic imaging (MRSI) requires specialized spatial and spectral data processing methods and benefits from the use of several sources of prior information that are not commonly available, including MRI-derived tissue segmentation, morphological analysis and spectral characteristics of the observed metabolites. In addition, incorporating information obtained from MRI data can enhance the display of low-resolution metabolite images and multiparametric and regional statistical analysis methods can improve detection of altered metabolite distributions. As a result, full MRSI processing and analysis can involve multiple processing steps and several different data types. In this paper, a processing environment is described that integrates and automates these data processing and analysis functions for imaging of proton metabolite distributions in the normal human brain. The capabilities include normalization of metabolite signal intensities and transformation into a common spatial reference frame, thereby allowing the formation of a database of MR-measured human metabolite values as a function of acquisition, spatial and subject parameters. This development is carried out under the MIDAS project (Metabolite Imaging and Data Analysis System), which provides an integrated set of MRI and MRSI processing functions. It is anticipated that further development and distribution of these capabilities will facilitate more widespread use of MRSI for diagnostic imaging, encourage the development of standardized MRSI acquisition, processing and analysis methods and enable improved mapping of metabolite distributions in the human brain.

Copyright 2006 John Wiley & Sons, Ltd.

Figures

Figure 1

Illustration of the steps and interconnections between MRSI and MRI data types required for full processing and analysis of a single-subject MRSI data set

Figure 2

Detailed flow diagram of the processing steps used for the metabolite and the water-reference 1H MRSI data sets

Figure 3

Illustration of the tissue contribution image processing. In (a) and (b) are shown T1- and T2-weighted MRIs at a single slice and the corresponding tissue segmentation images for CSF, WM and GM in (c), (d) and (e). These tissue maps are convolved with the 3D MRSI spatial response function to form the corresponding images shown in (f), (g) and (h)

Figure 4

Maps of the average metabolite levels from 14 subjects selected from the three-dimensional volume, showing the (a) NAA, (b) creatine and (c) choline distributions along three orthogonal directions. The metabolite images were CSF corrected and intensity normalized, and are shown in color superimposed on the reference MRI, with red indicating high data values, yellow intermediate and green–blue low. The color scales were adjusted separately for the best visualization of each set of metabolite images

Figure 5

Illustration of the anatomical atlas with each lobar region indicated in color and overlaid on a rendering of the template brain

Figure 6

Example screen shot of the MIDAS Viewer. Any acquired or derived data can be displayed, including conventional MR images with T1-, T2- and proton density-weighting (a–c), tissue content images (e.g. GM) (e), spectroscopic images of NAA, choline and creatine signal area (f, g, i), the B0 image (j) and the Quality map image (k). The metabolite spectrum (d) and the water signal obtained from the reference acquisition (h) can be displayed from any user-selected voxel, in addition to the corresponding study information and numerical results (e.g. area of choline, creatine and NAA signals). Image window and level adjustments can be made independently of each other and a slider (bottom) moves all images through the third dimension