1. Clinical DT-MRI Estimation, Smoothing and Fiber Tracking with Log-Euclidean Metrics
- Author
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Pierre Fillard, Vincent Arsigny, N. Ayache, Xavier Pennec, Analysis and Simulation of Biomedical Images (ASCLEPIOS), Inria Sophia Antipolis - Méditerranée (CRISAM), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria), Analysis and Simulation of Biomedical Images ( ASCLEPIOS ), Inria Sophia Antipolis - Méditerranée ( CRISAM ), Institut National de Recherche en Informatique et en Automatique ( Inria ) -Institut National de Recherche en Informatique et en Automatique ( Inria ), Laboratoire de Neuroimagerie Assistée par Ordinateur ( LNAO ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ), Modelling brain structure, function and variability based on high-field MRI data ( PARIETAL ), Service NEUROSPIN ( NEUROSPIN ), Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) ( DRF (CEA) ), Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Commissariat à l'énergie atomique et aux énergies alternatives ( CEA ) -Université Paris-Saclay-Inria Saclay - Ile de France, Laboratoire de Neuroimagerie Assistée par Ordinateur (LNAO), Commissariat à l'énergie atomique et aux énergies alternatives (CEA), Modelling brain structure, function and variability based on high-field MRI data (PARIETAL), Inria Saclay - Ile de France, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Service NEUROSPIN (NEUROSPIN), Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Université Paris-Saclay, Service NEUROSPIN (NEUROSPIN), Université Paris-Saclay-Direction de Recherche Fondamentale (CEA) (DRF (CEA)), and Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Commissariat à l'énergie atomique et aux énergies alternatives (CEA)-Inria Saclay - Ile de France
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[SDV.IB.IMA]Life Sciences [q-bio]/Bioengineering/Imaging ,Image quality ,DT-MRI ,Physics::Medical Physics ,[ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing ,Nerve Fibers, Myelinated ,Regularization (mathematics) ,Pattern Recognition, Automated ,030218 nuclear medicine & medical imaging ,Signal-to-noise ratio ,0302 clinical medicine ,[INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing ,Computer vision ,[ SDV.IB.IMA ] Life Sciences [q-bio]/Bioengineering/Imaging ,Mathematics ,[ INFO.INFO-IM ] Computer Science [cs]/Medical Imaging ,Radiological and Ultrasound Technology ,Computer Science Applications ,symbols ,[SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing ,Algorithms ,Smoothing ,[ INFO.INFO-MO ] Computer Science [cs]/Modeling and Simulation ,[ INFO.INFO-TS ] Computer Science [cs]/Signal and Image Processing ,Noise reduction ,Models, Neurological ,[SDV.IB.MN]Life Sciences [q-bio]/Bioengineering/Nuclear medicine ,Sensitivity and Specificity ,Log-Euclidean metrics ,Tensor field ,symbols.namesake ,03 medical and health sciences ,Imaging, Three-Dimensional ,tensor estimation ,Artificial Intelligence ,Rician fading ,Image Interpretation, Computer-Assisted ,[INFO.INFO-IM]Computer Science [cs]/Medical Imaging ,Maximum a posteriori estimation ,Humans ,Computer Simulation ,Tensor ,Riemannian geometry ,Electrical and Electronic Engineering ,Models, Statistical ,business.industry ,Reproducibility of Results ,Pattern recognition ,Image Enhancement ,[INFO.INFO-MO]Computer Science [cs]/Modeling and Simulation ,Noise ,Diffusion Magnetic Resonance Imaging ,Gaussian noise ,Artificial intelligence ,business ,Software ,030217 neurology & neurosurgery ,Diffusion MRI - Abstract
International audience; Diffusion tensor magnetic resonance imaging (DT-MRI or DTI) is an imaging modality that is gaining importance in clinical applications. However, in a clinical environment, data have to be acquired rapidly, often at the expense of the image quality. This often results in DTI datasets that are not suitable for complex postprocessing like fiber tracking. We propose a new variational framework to improve the estimation of DT-MRI in this clinical context. Most of the existing estimation methods rely on a log-Gaussian noise (Gaussian noise on the image logarithms), or a Gaussian noise, that do not reflect the Rician nature of the noise in MR images with a low signal-to-noise ratio (SNR). With these methods, the Rician noise induces a shrinking effect: the tensor volume is underestimated when other noise models are used for the estimation. In this paper, we propose a maximum likelihood strategy that fully exploits the assumption of a Rician noise. To further reduce the influence of the noise, we optimally exploit the spatial correlation by coupling the estimation with an anisotropic prior previously proposed on the spatial regularity of the tensor field itself, which results in a maximum a posteriori estimation. Optimizing such a nonlinear criterion requires adapted tools for tensor computing. We show that Riemannian metrics for tensors, and more specifically the log-Euclidean metrics, are a good candidate and that this criterion can be efficiently optimized. Experiments on synthetic data show that our method correctly handles the shrinking effect even with very low SNR, and that the positive definiteness of tensors is always ensured. Results on real clinical data demonstrate the truthfulness of the proposed approach and show promising improvements of fiber tracking in the brain and the spinal cord.
- Published
- 2006