Glioma Tissue Modeling by Combing the Information of MRI and in vivo Multivoxel MRS

This paper presents a glioma modelization method and a regression-like model to create a gradually glioma image (GlioIm). Multimodal signal, images of magnetic resonance imaging (MRI) and in vivo multivoxel MR spectroscopy (MRS) are combined by the regression-like model with spatial resolution regis...

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Bibliographic Details
Main Authors: Weibei Dou, Aoyan Dong, Ping Chi, Shaowu Li, Constans, J
Format: Conference Proceeding
Language:English
Subjects:
Brain modeling
Chemicals
Hospitals
In vivo
Laboratories
Magnetic resonance
Magnetic resonance imaging
Neoplasms
Spatial resolution
Spectroscopy
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Summary:This paper presents a glioma modelization method and a regression-like model to create a gradually glioma image (GlioIm). Multimodal signal, images of magnetic resonance imaging (MRI) and in vivo multivoxel MR spectroscopy (MRS) are combined by the regression-like model with spatial resolution registration. This modeling method consists of feature models of glioma such as the signal intensity of MR image and the metabolite changes of MRS, the correlation model noted as metabolites ratio (MetaR) and the combined regression-like model. The estimated GlioIm includes both brain structure and glioma grade information. A nonlinear model is proposed and validated in this paper. The testing data is acquired by Siemens TrioTim (3T) and Syngo MR B15 at Beijing Tiantan hospital (China). The MRS of three glioma patients, two affected by astrocytoma and one by glioma, and the chemical shift imaging (CSI) reference T2 images were considered in our validation experiment. The resulting GlioIms are compared with ground truth provided by neuroradiologists of Tiantan and verified with their pathology report. They report that our method and model are very efficient.
ISSN:2151-7614
2151-7622
DOI:10.1109/ICBBE.2010.5517478