Unmixing dynamic PET images with a PALM algorithm. Cavalcanti, Y. C., Oberlin, T., Dobigeon, N., & Tauber, C. In 2017 25th European Signal Processing Conference (EUSIPCO), pages 425-429, Aug, 2017.
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Paper doi abstract bibtex
Paper doi abstract bibtex 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.
@InProceedings{8081242,
author = {Y. C. Cavalcanti and T. Oberlin and N. Dobigeon and C. Tauber},
booktitle = {2017 25th European Signal Processing Conference (EUSIPCO)},
title = {Unmixing dynamic PET images with a PALM algorithm},
year = {2017},
pages = {425-429},
abstract = {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.},
keywords = {biological tissues;brain;medical image processing;positron emission tomography;unmixing dynamic PET images;PALM algorithm;ubiquitous task;hyperspectral image analysis;dynamic positron emission tomography images;tissues;brain imaging;time-activity curve estimation;positron emission tomography;Signal processing algorithms;Positron emission tomography;Heuristic algorithms;Principal component analysis;Algorithm design and analysis;Optimization;Europe},
doi = {10.23919/EUSIPCO.2017.8081242},
issn = {2076-1465},
month = {Aug},
url = {https://www.eurasip.org/proceedings/eusipco/eusipco2017/papers/1570342829.pdf},
}Downloads: 0
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