Hierarchical non‐negative matrix factorization applied to three‐dimensional 3 T MRSI data for automatic tissue characterization of the prostate |
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| Authors: | Teresa Laudadio Anca R. Croitor Sava Diana M. Sima Alan J. Wright Arend Heerschap Nicola Mastronardi Sabine Van Huffel |
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| Affiliation: | 1. Istituto per le Applicazioni del Calcolo ‘M. Picone’ (IAC), Consiglio Nazionale delle Ricerche, Bari, Italy;2. Department of Electrical Engineering (ESAT), STADIUS Center for Dynamical Systems, Signal Processing and Data Analytics, KU Leuven, Leuven, Belgium;3. iMinds Medical Information Technologies, Leuven, Belgium;4. Cancer Institute CRUK, University of Cambridge, Li Ka Shing Centre, Cambridge, UK;5. Department of Radiology, Radboud University Nijmegen Medical Center, Nijmegen, Netherlands |
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| Abstract: | In this study non‐negative matrix factorization (NMF) was hierarchically applied to simulated and in vivo three‐dimensional 3 T MRSI data of the prostate to extract patterns for tumour and benign tissue and to visualize their spatial distribution. Our studies show that the hierarchical scheme provides more reliable tissue patterns than those obtained by performing only one NMF level. We compared the performance of three different NMF implementations in terms of pattern detection accuracy and efficiency when embedded into the same kind of hierarchical scheme. The simulation and in vivo results show that the three implementations perform similarly, although one of them is more robust and better pinpoints the most aggressive tumour voxel(s) in the dataset. Furthermore, they are able to detect tumour and benign tissue patterns even in spectra with lipid artefacts. Copyright © 2016 John Wiley & Sons, Ltd. |
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| Keywords: | MRSI blind source separation non‐negative matrix factorization prostate cancer nosologic imaging |
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