1. Unmixing dynamic PET images with a PALM algorithm
- Author
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Thomas Oberlin, Clovis Tauber, Nicolas Dobigeon, Yanna Cruz Cavalcanti, Centre National de la Recherche Scientifique - CNRS (FRANCE), Institut National Polytechnique de Toulouse - Toulouse INP (FRANCE), Institut National de la Santé et de la Recherche Médicale - INSERM (FRANCE), Université Toulouse III - Paul Sabatier - UT3 (FRANCE), Université Toulouse - Jean Jaurès - UT2J (FRANCE), Université Toulouse 1 Capitole - UT1 (FRANCE), Université de Tours (FRANCE), Institut National Polytechnique de Toulouse - INPT (FRANCE), (OATAO), Open Archive Toulouse Archive Ouverte, Signal et Communications (IRIT-SC), Institut de recherche en informatique de Toulouse (IRIT), Université Toulouse Capitole (UT Capitole), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Centre National de la Recherche Scientifique (CNRS)-Institut National Polytechnique (Toulouse) (Toulouse INP), Université Fédérale Toulouse Midi-Pyrénées-Toulouse Mind & Brain Institut (TMBI), Université Toulouse - Jean Jaurès (UT2J)-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse III - Paul Sabatier (UT3), Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse Capitole (UT Capitole), Université Fédérale Toulouse Midi-Pyrénées, Institut National Polytechnique (Toulouse) (Toulouse INP), Institut National de la Santé et de la Recherche Médicale (INSERM), Université Toulouse 1 Capitole (UT1), and Université Fédérale Toulouse Midi-Pyrénées-Université Toulouse 1 Capitole (UT1)
- Subjects
[INFO.INFO-AI] Computer Science [cs]/Artificial Intelligence [cs.AI] ,Positron emission tomography ,Computer science ,[INFO.INFO-TS] Computer Science [cs]/Signal and Image Processing ,Physics::Medical Physics ,0211 other engineering and technologies ,[INFO.INFO-GR] Computer Science [cs]/Graphics [cs.GR] ,Principal component analysis ,02 engineering and technology ,computer.software_genre ,Image (mathematics) ,[INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI] ,Traitement des images ,[INFO.INFO-CV] Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,Voxel ,0202 electrical engineering, electronic engineering, information engineering ,Traitement du signal et de l'image ,Heuristic algorithms ,Synthèse d'image et réalité virtuelle ,021103 operations research ,Spectral signature ,Hyperspectral imaging ,[INFO.INFO-CV]Computer Science [cs]/Computer Vision and Pattern Recognition [cs.CV] ,020206 networking & telecommunications ,Vision par ordinateur et reconnaissance de formes ,Intelligence artificielle ,[INFO.INFO-GR]Computer Science [cs]/Graphics [cs.GR] ,[INFO.INFO-TI] Computer Science [cs]/Image Processing [eess.IV] ,[INFO.INFO-TI]Computer Science [cs]/Image Processing [eess.IV] ,Computer Science::Computer Vision and Pattern Recognition ,Spatial variability ,Signal processing algorithms ,Algorithm ,computer - Abstract
International audience; Unmixing is a ubiquitous task in hyperspectral image analysis which consists in jointly extracting typical spectral signatures and estimating their respective proportions in the voxels, providing an explicit spatial mapping of these elementary signatures over the observed scene. Inspired by this approach, this paper aims at proposing a new framework for analyzing dynamic positron emission tomography (PET) images. More precisely, a PET-dedicated mixing model and an associated unmixing algorithm are derived to jointly estimate time-activity curves (TAC) characterizing each type of tissues, and the proportions of those tissues in the voxels of the imaged brain. In particular, the TAC corresponding to the specific binding class is expected to be voxel-wise dependent. The proposed approach allows this intrinsic spatial variability to be properly modeled, mitigated and quantified. Finally, the main contributions of the paper are twofold: first, we demonstrate that the unmixing concept is an appropriate analysis tool for dynamic PET images; and second, we propose a novel unmixing algorithm allowing for variability, which significantly improves the analysis and interpretation of dynamic PET images when compared with state-of-the-art unmixing algorithms.
- Published
- 2017