An unsupervised spatio-temporal regularization for perfusion MRI deconvolution in acute stroke
| Autor: | David Rousseau, Carole Frindel, Mathilde Giacalone |
|---|---|
| Přispěvatelé: | Centre de Recherche en Acquisition et Traitement de l'Image pour la Santé (CREATIS), Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), Institut National des Sciences Appliquées (INSA)-Université de Lyon-Institut National des Sciences Appliquées (INSA)-Université de Lyon-Université Claude Bernard Lyon 1 (UCBL), Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM), Images et Modèles, Université de Lyon-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Université Jean Monnet [Saint-Étienne] (UJM)-Hospices Civils de Lyon (HCL)-Institut National des Sciences Appliquées de Lyon (INSA Lyon), ANR-11-LABX-0063,PRIMES,Physique, Radiobiologie, Imagerie Médicale et Simulation(2011) |
| Jazyk: | angličtina |
| Rok vydání: | 2016 |
| Předmět: |
Blind deconvolution
[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging Quantitative Biology::Tissues and Organs Physics::Medical Physics Deconvolution computer.software_genre Regularization (mathematics) 030218 nuclear medicine & medical imaging 03 medical and health sciences 0302 clinical medicine Magnetic resonance imaging Voxel medicine Computer vision Spurious relationship Mathematics medicine.diagnostic_test business.industry Shape Inverse problem Cost function Piecewise Lesions Artificial intelligence business Signal processing algorithms computer [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing 030217 neurology & neurosurgery |
| Zdroj: | 24th European Signal Processing Conference (EUSIPCO), 2016 24th European Signal Processing Conference (EUSIPCO), 2016, Aug 2016, Budapest, Hungary. ⟨10.1109/EUSIPCO.2016.7760540⟩ EUSIPCO |
| DOI: | 10.1109/EUSIPCO.2016.7760540⟩ |
| Popis: | International audience; We consider the ill-posed inverse problem encountered in perfusion magnetic resonance imaging (MRI) analysis due to the necessity of eliminating, via a deconvolution process, the imprint of the arterial input function on the MR signals. Until recently, this deconvolution process was realized independently voxel by voxel with a sole temporal regularization despite the knowledge that the ischemic lesion in acute stroke can reasonably be considered piecewise continuous. A new promising algorithm incorporating a spatial regularization to avoid spurious spatial artifacts and preserve the shape of the lesion was introduced [1]. So far, the optimization of the spatio-temporal regularization parameters of the deconvolution algorithm was supervised. In this communication, we evaluate the potential of the L-hypersurface method in selecting the spatio-temporal regularization parameters in an unsupervised way and discuss the possibility of automating this method. This is demonstrated quantitatively with an in silico approach using digital phantoms simulated with realistic lesion shapes. |
| Databáze: | OpenAIRE |
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