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 of 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.
基金：
Tsinghua National Laboratory for Information Science and Technology(TNList) Cross-discipline Foundation
Dou Weibei,Dong Aoyan,Chi Ping,et al.Glioma Tissue Modeling by Combing the Information of MRI and in vivo Multivoxel MRS[J].2010 4TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING (ICBBE 2010).2010,-.
APA：
Dou, Weibei,Dong, Aoyan,Chi, Ping,Li, Shaowu&Constans, Jean-Marc.(2010).Glioma Tissue Modeling by Combing the Information of MRI and in vivo Multivoxel MRS.2010 4TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING (ICBBE 2010),,
MLA：
Dou, Weibei,et al."Glioma Tissue Modeling by Combing the Information of MRI and in vivo Multivoxel MRS".2010 4TH INTERNATIONAL CONFERENCE ON BIOINFORMATICS AND BIOMEDICAL ENGINEERING (ICBBE 2010) .(2010):-
