An Advanced MRI and MRSI Data Fusion Scheme for Enhancing Unsupervised Brain Tumor Differentiation

Abstract Proton Magnetic Resonance Spectroscopic Imaging (1 H MRSI) has shown great potential in tumor diagnosis since it provides localized biochemical information discriminating different tissue types, though it typically has low spatial resolution. Magnetic Resonance Imaging (MRI) is widely used...

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Bibliographic Details
Published in:Computers in biology and medicine 2017-02, Vol.81, p.121-129
Main Authors: Li, Yuqian, Liu, Xin, Wei, Feng, Sima, Diana M, Van Cauter, Sofie, Himmelreich, Uwe, Pi, Yiming, Hu, Guang, Yao, Yi, Van Huffel, Sabine
Format: Article
Language:English
Subjects:
Algorithms
Biomarkers, Tumor - analysis
Brain cancer
Brain Neoplasms - chemistry
Brain Neoplasms - diagnosis
Brain research
Data fusion
Decomposition
Glioma - chemistry
Glioma - diagnosis
Humans
Internal Medicine
Lipids
Magnetic resonance imaging (MRI)
Magnetic Resonance Imaging - methods
Magnetic resonance spectroscopic imaging (MRSI)
Magnetic Resonance Spectroscopy - methods
Metabolites
Methods
Molecular Imaging - methods
Multimodal Imaging - methods
NMR
Non-negative matrix factorization (NMF)
Nuclear magnetic resonance
Other
Patients
Pattern Recognition, Automated - methods
Reproducibility of Results
Sensitivity and Specificity
Studies
Tumors
Unsupervised Machine Learning
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Summary:Abstract Proton Magnetic Resonance Spectroscopic Imaging (1 H MRSI) has shown great potential in tumor diagnosis since it provides localized biochemical information discriminating different tissue types, though it typically has low spatial resolution. Magnetic Resonance Imaging (MRI) is widely used in tumor diagnosis as an in vivo tool due to its high resolution and excellent soft tissue discrimination. This paper presents an advanced data fusion scheme for brain tumor diagnosis using both MRSI and MRI data to improve the tumor differentiation accuracy of MRSI alone. Non-negative Matrix Factorization (NMF) of the spectral feature vectors from MRSI data and the image fusion with MRI based on wavelet analysis are implemented jointly. Hence, it takes advantage of the biochemical tissue discrimination of MRSI as well as the high resolution of MRI. The feasibility of the proposed frame work is validated by comparing with the expert delineations, giving mean correlation coefficients for the tumor source of 0.97 and the Dice score of tumor region overlap of 0.90. These results compare favorably against those obtained with a previously proposed NMF method where MRSI and MRI are integrated by stacking the MRSI and MRI features.
ISSN:0010-4825
1879-0534
DOI:10.1016/j.compbiomed.2016.12.017