TENSOR Based Tumor Tissue Differentiation Using Magnetic Resonance Spectroscopic Imaging

Transcription

1 TENSOR Based Tumor Tissue Differentiation Using Magnetic Resonance Spectroscopic Imaging HN Bharath, DM Sima, N Sauwen, U Himmelreich, L De Lathauwer, Sabine Van Huffel IEEE EMBC 2015 Milano, Italy August 25-29, 2015

2 Contents Overview Brain Tumor Tissue type differentiation using MRSI NMF Hierarchical(h) NMF Non-negative Tensor Factorization Conclusions and new directions

3 Metabolite quantification for MR Spectroscopy (MRS) Single-voxel MRS MRS quantification Metabolite concentrations

4 Metabolite quantification for MRS Imaging (MRSI) Multi-voxel MRS MRS quantification using spatial information NAA Myo Cr PCho Glu Lac Lip1 Lip2 Ala Glc Tau Metabolite concentrations = biomarkers of disease Metabolite maps

5 Unsupervised Brain Tumor Diagnosis using NMF MRSI Y = matrix of spectra, Y W H min Y - W H such that W 0, H 0 non-negative matrix factorization W = tissue-specific spectral patterns: H = spatial distribution of tissue types: normal normal tissue active tumor necrosis tumor necrosis glioblastoma multiforme patient

6 MRI features MRI features Non-negative matrix factorization (NMF) Y W H voxels voxels (tissue) abundance (tissue) source Non-negativity constraint: Y i,j, W i,j, H i,j 0, i,j Unsupervised: applicable patient-by-patient, tissue classes not a priori known NMF: 1. integrate ALL features into one vector 2. use NMF

7 Validation Based on manual segmentation by radiologist (only pathological tissue types) 1) Dice-scores (based on H) area( A B) Dice 2x area ( A ) area ( B ) A B 2) Correlation coefficients (based on W). ab a :tissue source vector Corr a. b b :average feature vector over corresponding tissue region

8 Hierarchical NMF (hnmf) Improved results on MRSI data only (Li et al., NMR in BioMed. 2013) Y = Coregistered MP-MRI data NMF (2 sources) W source1, H source1 (tumour, necrosis,...) W source2, H source2 (white matter, CSF,...) NMF (# tissues source1) Mask needed! NMF (# tissues source2) W tumor, H tumor W necrosis, H necrosis W WM, H WM W GM, H GM...

9 Case study: single stage NMF vs hnmf Single stage NMF hnmf Dice tumor = 71% Dice tumor+necrosis = 83% Dice complete tumor = 75% Corr tumor = 0.60 Corr necrosis = 0.97 Corr edema = 0.93 Dice tumor = 81% Dice tumor+necrosis = 92% Dice complete tumor = 83% Corr tumor = 0.78 Corr necrosis = 0.98 Corr edema = 0.97

10 Spatial Tensor Representation (MRSI only) Frontal slices (XY T ) representing the spatial distribution of a tissue type does not have low rank structure. Difficult to find the rank L R for a particular tissue type distribution. 10

11 XX T based Tensor Representation (MRSI only) L X(i) = j=1 S ij 2 Spectra reduced in length and denoised without losing vital information, Peaks get higher weights, 11 Peaks coupled because of XX T in the frontal slices HN Bharath et al, Proc. IEEE-EMB Symposium, Milan, Italy, Aug 2015, to appear

12 Non-negative CPD for Tumor Differentiation Constraints: 1. Non-negativity in all modes 2. Symmetry in frontal slices 3. Sparsity in H-factor apply Non-negative CPD with L 1 regularization using Tensorlab 1 [S, H ] = min S 0,H 0 K T r=1 S :, r о S :, r о H(:, r), Using H*, recover tissue-specific spectra W from Y via LS 2 2 +λ Vec H 1 Using W, recover tissue-type spatial distributions H from Y via NN-LS 12 1 Laurent Sorber, Marc Van Barel and Lieven De Lathauwer. Tensorlab v2.0, Available online, January URL:

13 Result: Patient-2 13

14 Source Correlation: Algorithm vs Expert labeling (MRSI only) W NCPD Single stage NMF hnmf Grade PATIENT-2 T 0.99 X X High PATIENT-2 N High Median/MAD T 0.98 / / / High Median/MAD N / / / Using 10 high-grade glioma (HGG) patients with MRSI dataset Abundance map Correlation: Algorithm vs Expert labeling H NCPD Single stage NMF hnmf Grade PATIENT-2 T 0.80 X X High PATIENT-2 N High Median/MAD T 0.79 / / / High Median/MAD N / / /

15 Contents Overview Brain Tumor Tissue type differentiation using MRSI Conclusions and new directions

16 o o Conclusions and new directions Many BSS problems in Smart Diagnostics are low rank solve via matrix or tensor factorization plus constraints Successful examples shown in brain tissue typing o Other BSS applications: bioinformatics (O. Alter, E. Acar), BCI (Cichocki, Mørup, Martinez-Montes), mobile EEG, multichannel ECG, EEG-fMRI, etc. New directions? o o Automate rank & structure estimation Extend to biomedical data fusion by adding other MR modalities such as anatomical MRI, DWI, PWI, DTI, DKI exploit full potential of Tensor toolbox

17 Acknowledgment University Hospitals Leuven Gasthuisberg ZNA Middelheim, Queen Paola Children s hospital EMC Rotterdam KU Leuven, Dept. Electrical Engineering-ESAT, division STADIUS & MICAS Ghent University, Dept. Telecommunication and Information Processing, TELIN-IPI Eindhoven University of Technology ERC advanced grant BIOTENSORS in collaboration with L. De Lathauwer and group Thank you!

18 TDA KU Leuven Workshop on Tensor Decompositions and Applications January 18-22, 2016, Leuven, Belgium Local Organisers: Sabine Van Huffel and Lieven De Lathauwer Confirmed Speakers Orly Alter Pierre Comon Eva Ceulemans Harm Derksen Nicolas Gilllis Daniel Kressner Lek-Heng Lim Ivan Markovsky Morten Mørup Nikos Sidiropoulos Bart Vandereycken Frank Verstraete