Data analysis and tissue type assignment for glioblastoma multiforme.
Glioblastoma multiforme (GBM) is characterized by high infiltration. The interpretation of MRSI data, especially for GBMs, is still challenging. Unsupervised methods based on NMF by Li et al. (2013, NMR in Biomedicine) and Li et al. (2013, IEEE Transactions on Biomedical Engineering) have been proposed for glioma recognition, but the tissue types is still not well interpreted. As an extension of the previous work, a tissue type assignment method is proposed for GBMs based on the analysis of MRSI data and tissue distribution information. The tissue type assignment method uses the values from the distribution maps of all three tissue types to interpret all the information in one new map and color encodes each voxel to indicate the tissue type. Experiments carried out on in vivo MRSI data show the feasibility of the proposed method. This method provides an efficient way for GBM tissue type assignment and helps to display information of MRSI data in a way that is easy to interpret.
School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Yuqian
Y
Li
School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Yiming
Y
Pi
School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Xin
X
Liu
School of Electronic Engineering, University of Electronic Science and Technology of China, Chengdu 611731, China.
Yuhan
Y
Liu
Department of Radiology and Department of Imaging and Pathology, University Hospitals of Leuven, 3001 Leuven, Belgium.
Sofie
S
Van Cauter
D001932:Brain Neoplasms / Q000378:metabolism* / Q000473:pathology*; D005260:Female; D005909:Glioblastoma / Q000378:metabolism* / Q000473:pathology*; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D009682:Magnetic Resonance Spectroscopy / Q000295:instrumentation / Q000379:methods*; D008297:Male