quantification and classification of mr spectra from human brain 量化的mr光谱从人类大脑的分类

Quantification and Classification of MR spectra from human brain tumors using metabolite concentrations as input,Andy Devos, Leentje Vanhamme, Sabine Van Huffel, Lukas LukasDepartment of Electrical EngineeringKULeuven,Overview,Quantification of long echo proton MRS signals Classification of brain tumors Discussion points,PR Nijmegen 2001,ESAT-SISTA/COSIC, KULeuven,Quantification of long echo proton MRS signals,The dataset used Modeling a MRS signal Prior knowledge Strategy: spectral quantification using MRUI,PR Nijmegen 2001,ESAT-SISTA/COSIC, KULeuven,The dataset used,Centre: IDI, Barcelona (Philips data) 2 types of tumors: Glioblastomas= group 1 Meningiomas= group 2 20 data of group 123 data of group 2,PR Nijmegen 2001,ESAT-SISTA/COSIC, KULeuven,Modeling a MRS signal,Theoretical model function,PR Nijmegen 2001,Prior knowledge,7 peaks; Cho, Cr, NAA and the doublets of Ala and Lac,Cho, Cr, NAAIn phase jCho=jCr=jNAA=0Equal damping dCho=dCr=dNAA Ala Inverted jAla1=jAla2=180Equal damping dAla1=dAla2Frequency shift: 7 Hz,Lac: same as Ala,PR Nijmegen 2001,ESAT-SISTA/COSIC, KULeuven,Strategy: spectral quantification using MRUI,Preprocessinganalyse spectra in batch formatphase correction with Kloses methodalignment of the spectrafiltering with Lanczos HSVD: 25 components, +/-35 Hz Quantificationprior knowledgeAMARES: model signal in the time domainLorentzian/Gaussian lineshape,PR Nijmegen 2001,ESAT-SISTA/COSIC, KULeuven,Classification of brain tumors,Approaches to classification LS-SVM as a classification algorithm Comparison of two approaches peak pattern peak areas,PR Nijmegen 2001,ESAT-SISTA/COSIC, KULeuven,Classification based on quantification results: quantify peaks in spectrum Classification directly based on part of the spectraGoal: assist the doctors in making their diagnoses,Approaches to classification,PR Nijmegen 2001,ESAT-SISTA/COSIC, KULeuven,LS-SVM as a classification algorithm,ESAT-SISTA/COSIC, KULeuven,PR Nijmegen 2001,Classification based on the raw spectra,data magnitude spectrum range 4.17 0.94 ppm (or -34 -240 Hz): 107 pointsscaling of the dataregularisation: determine LS-SVM hyperparameters s, gclassification with LS-SVM,ESAT-SISTA/COSIC, KULeuven,PR Nijmegen 2001,ESAT-SISTA/COSIC, KULeuven,PR Nijmegen 2001,Classification based on estimated amplitudes,ESAT-SISTA/COSIC, KULeuven,ppm,PR Nijmegen 2001,Classification based on estimated amplitudes,ESAT-SISTA/COSIC, KULeuven,PR Nijmegen 2001,Classification based on estimated amplitudes,ESAT-SISTA/COSIC, KULeuven,PR Nijmegen 2001,ESAT-SISTA/COSIC, KULeuven,PR Nijmegen 2001,At present: no significant difference Future work: discrimination among more classes other feature extraction methods,Discussion points,Clinical information available?To what extent do the spectra belong to the assigned classes? e.g. tumor assigned as glioblastomas, while a large part of it is necrosis?information available?is this visible on MR images?how to handle this additional information?,PR Nijmegen 2001,ESAT-SISTA/COSIC, KULeuven,Discussion points,Combination of data from different centra?introduce additional categorical variable into classification method to correct for differences in centree.g. CDP 1SGHMS3IDI2Nijmegen4 do not scale these categorical variables,PR Nijmegen 2001,ESAT-SISTA/COSIC, KULeuven,Adjusting the performance crit