Magnetic Resonance Spectroscopy (MRS) can be applied to probe noninvasively the concentrations and distribution of metabolites of human tissue in vivo. As the improving of hardware and localization techniques, MRS becomes more and more important in clinical applications. Furthermore, some post-processing software, like LCModel, provide a graphical user interface for efficient and convenient analysis of MR spectroscopic imaging and thus increase the value of MRS applications. Although LCModel provides an efficient analysis and produces stable results, it can not provide metabolite map to observe the distribution of metabolite concentrations. For this reason our study processes the output data of LCModel and Digital Imaging and Communications in Medicine (DICOM) format MR images for 2D/3D metabolite map displaying. Users can use this software to observe the metabolic distribution in AP, SI and RL slice of brain tissue. In the meanwhile, as the absolute quantification of MRS has played more and more important role in clinical applications, this study also provides the LCModel end users an easy way for interpretation.

Magnetic resonance (MR) has been developed and applied to clinical analysis studies due to its non-invasive properties. Because of the increasing interest of applying magnetic resonance spectroscopy imaging (MRSI) to clinical application, some post-processing softwares, like LCModel, provide a graphical user interface for convenient and efficient analysis. However, the features of combining MR imaging (MRI) with MRS information and browsing all analyzed results are not provided by LCModel. Our study proposed a method to implement the architecture for processing General Electric (GE), Siemens MRSI data sets and provides features including interactive display, selection and analysis of full 2D slices. For multi-console analysis, our tool also provides the combination of MRS, MRI, and data sets generated by LCModel, such as the projection of three planes and metabolite/spectra map, and therefore the three formats of data sets could be obtained from scanners of various manufactures. Especially, it is more complicated when processing GE data sets, so some mechanisms for processing are proposed, like the transformation, the three plane loc images detection and MRSI detection, etc. Additionally, our tool also has the advantage of the compatibility of further extended functionalities, which would be more flexible and useful for clinical applications.