Equally as important as the pulse sequence used to record the data is the software used for data analysis. Ideally, fully automated spectral curve-fitting algorithms should be used to provide a quantitative estimate of each metabolite concentration. However, although such software exists and is often used in clinical research studies, it is little utilized in routine clinical practice, where visual interpretation (as in most radiological readings) is most common. Other commonly used metrics include ratios of metabolites such as NAA/Cho, NAA/Cr, Cho/Cr, and mI/Cr. While these can be helpful, they can also be ambiguous or difficult to interpret when both metabolites change at the same time. For this reason, a better approach is often to compare the metabolite level in the tumor with that of the same metabolite in a normal brain region, for instance the mirror image location in the contralateral hemisphere, if available.
These measurements are often referred to as “normalized.” Sometimes, unfortunately, a contralateral reference normal region is not available for comparison (e.g., midline lesions such as pontine gliomas), in which case a knowledge of normal age- and regional-related spectral variations in normal control subjects is essential for proper interpretation. The region of interest chosen for analysis will have a large influence on the results, since it allows metabolic heterogeneity to be evaluated, and the voxel with the maximum Cho signal to be chosen for analysis and/or targeted for biopsy. Several groups have investigated the use of pattern recognition of proton MRS or MRSI spectra to diagnose different tumor types. However, probably because of lesion heterogeneity, overlap between different tumor types, and also the dependence of the spectral appearance on data collection and analysis techniques, these methods have not entered into clinical practice.
Since MRSI can be performed at the same time as routine brain MRI, and does not require any specialized hardware, in principle it should be straightforward to incorporate MRSI into the routine evaluation of patients with brain tumors. There are several reasons for this, including the length of time required to perform, process, and interpret MRSI data; the lack of widespread technologist and radiologist training in MRSI; the lack of standardization of acquisition and analysis techniques; and the rather suboptimal commercially available protocols to perform MRSI (compared to current state-of-threat research MRSI techniques).
MRS analysis data are relatively complex and This complexity not only reflects the combined processing requirements of imaging and NMR spectroscopy, but that additional MRSI-specific processing techniques are available, which can enhance either the data reconstruction, analysis or display.
The reconstruction and analysis of both the spatial and spectral MRSI data dimensions can benefit from the incorporation of prior information, including, for example, knowledge of tissue distributions obtained from higher-resolution MRI, information on metabolite spectral patterns and knowledge of normal metabolite concentrations. An additional piece of information is an estimate of the ‘quality’ of the data at that location, which can be used to exclude spectra of inadequate quality from further analysis.
the spectral analysis must be able to deal with the variable quality of the data, which may include, for example, good-quality spectra with very low signal intensity in regions subject to significant CSF partial volume contribution, regions with unacceptably broadened line shapes and large residual water and lipid signals, particularly for the whole brain MRSI method used for this project. Spectra are also acquired from regions of no interest, such as outside of the brain, and should therefore be excluded from the time-consuming spectral analysis. The signal-to-noise ratios (SNRs) are typically lower than those commonly obtained with single voxel methods, requiring that spectral analysis methods be more robust, which may in turn require that a simpler spectral model be used.
The water reference MRSI data are used to address some of the issues associated with MRSI processing. These include the calculation of a B0 map from the frequency of the water resonance, and also masks generated by integrating over the water and lipid spectral ranges. The first of these defines the brain region and is used to limit the voxels selected for spectral analysis, while the mask generated from the lipid signal identifies the subcutaneous signal region and is used for lipid k-space extrapolation.
The automated parametric spectral analysis procedure, performs spectral fitting of the complex-value spectral data using a Lorentzian–Gaussian line shape model and incorporates prior spectral information. This analysis procedure also takes advantage of the spatial information in the MRSI data by modifying starting values based on local neighborhood information. Therefore, the spectral analysis is followed by a simple quality evaluation procedure that creates a ‘Quality Image’ that reflects areas of the image having resultant linewidths and signal intensities considered to be within limits for the acquired data type.
The ultimate goal for statistical analysis of MRSI data is assessment of metabolite abnormalities for individual subject diagnosis. The usefulness of MRSI for this purpose still remains to be demonstrated; however, a major obstacle towards achieving this goal has been the lack of uniform MRSI procedures and large MRSI databases that permit comparisons of individual MRSI data to normal values.
It is used to detect abnormalities in the brain’s biochemical processes.
Structural imaging techniques include angiography, CT, Doppler, MRI, and myelography. Functional imaging techniques include functional MRI (fMRI), MEG, MRS, PET, QEEG, and SPECT.
Magnetic resonance spectroscopy (MRS) is related to magnetic resonance imaging (MRI) in that it uses the same machinery; however, instead of measuring blood flow, MRS measures the concentration of specific chemicals, such as neurotransmitters.