Distributions of proton MR-detected metabolites have been mapped throughout the brain in a group of normal subjects using a volumetric MR spectroscopic imaging (MRSI) acquisition with an interleaved water reference. Data were processed with intensity and spatial normalization to enable voxel-based analysis methods to be applied across a group of subjects. Results demonstrate significant regional, tissue, and genderdependent variations of brain metabolite concentrations, and variations of these distributions with normal aging. The greatest alteration of metabolites with age was observed for whitematter choline and creatine. An example of the utility of the normative metabolic reference information is then demonstrated for analysis of data acquired from a subject who suffered a traumatic brain injury. This study demonstrates the ability to obtain proton spectra from a wide region of the brain and to apply fully automated processing methods. Proton MR spectroscopy (MRS) enables the detection of a number of tissue metabolites that provide sensitive markers of disease or injury, making these techniques of considerable interest for clinical diagnostic purposes and particularly for studies in the brain. The acquisition and analysis of MRS data have several technical challenges that compromise the spatial resolution and accuracy of the resultant metabolite values. Furthermore, metabolic changes with disease and injury can frequently be subtle and diffuse, with the result that metabolite images may not be visually interpretable in the sense of a structural MRI. Therefore, the analysis of MRS data greatly benefits from comparison against a known reference signal.Since in vivo MRS measurements are dependent on the acquisition method used, the reference data must be acquired in an identical manner to that of the data under analysis. It is also necessary to take into account normal variations in metabolite concentrations; for example, variations in metabolite concentrations between tissue type, across different brain regions, and changes with age are well documented (1-14). Other reports have indicated differences in metabolite concentrations with gender, lateralization (15), intelligence quotient (IQ) (16), and associations with smoking and alcohol consumption (17). To account for these factors, results are commonly compared against data obtained from the same location in a group of control subjects matched to the study group under investigation. However, although many studies have reported metabolite values from normal control subjects, these values can rarely be used as the reference information for other investigations. For example, published values using single-voxel spectroscopy (SVS) measurements are limited to only a few brain regions, and acquisition parameters, analysis methods, and subject selection criteria, such as age, vary considerably, making it unlikely that existing data can be used as reference information for a new investigation. Additionally, a sufficient number of measurements must be acquired to accoun...

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.

In this study, the feasibility of using magnetic resonance imaging (MRI) to detect tissue necrosis induced by focussed ultrasound beams was investigated. It was shown that lesions produced in dog's thigh muscle in vivo were clearly visible in T2-weighted images and that the lesion dimensions measured from the images correlated with the postmortem measurements of the visible tissue damage. It was also shown that the sonications can be done in the magnet and that the lesions are visible immediately after the sonications with increasing image contrast as a function of time. These results showed that MRI can be used to direct and monitor on-line noninvasive ultrasound surgery. This may have a major impact in future patient treatments.

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A fast, accurate and fully automatic method of segmenting magnetic resonance images of the human brain is introduced. The approach scales well allowing fast segmentations of fine resolution images. The approach is based on modifications of the soft clustering algorithm, fuzzy c-means, that enable it to scale to large data sets. Two types of modifications to create incremental versions of fuzzy c-means are discussed. They are much faster when compared to fuzzy c-means for medium to extremely large data sets because they work on successive subsets of the data. They are comparable in quality to application of fuzzy c-means to all of the data. The clustering algorithms coupled with inhomogeneity correction and smoothing are used to create a framework for automatically segmenting magnetic resonance images of the human brain. The framework is applied to a set of normal human brain volumes acquired from different magnetic resonance scanners using different head coils, acquisition parameters and field strengths. Results are compared to those from two widely used magnetic resonance image segmentation programs, Statistical Parametric Mapping and the FMRIB Software Library (FSL). The results are comparable to FSL while providing significant speed-up and better scalability to larger volumes of data.