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Your search keyword '"Département lmage et Traitement Information (IMT Atlantique - ITI)"' showing total 6 results
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6 results on '"Département lmage et Traitement Information (IMT Atlantique - ITI)"'

1. SPEAR-Net: Self-Prior Enhanced Artifact Removal Network for Limited-Angle DECT

Author

Kai Chen, Chunfeng Yang, Hui Tang, Xu Ji, Gouenou Coatrieux, Jean-Louis Coatrieux, Yang Chen, Southeast University, Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire Traitement du Signal et de l'Image (LTSI), Université de Rennes (UR)-Institut National de la Santé et de la Recherche Médicale (INSERM), Centre de Recherche en Information Biomédicale sino-français (CRIBS), Université de Rennes (UR)-Southeast University [Jiangsu]-Institut National de la Santé et de la Recherche Médicale (INSERM), National Key Research and Development Program of China [2022YFE0116700, 2022YFC2401600], Key Research and Development Programs in Jiangsu Province of China [BE2021703, BE2022768], and Jiangsu Province Science Foundation for Youths [BK20220825]

Subjects
iodine contrast agent quantification, dual-energy computed tomography (DECT), virtual noncontrast (VNC) imaging, Deep learning, IP networks, Imaging, Image reconstruction, Heuristic algorithms, Convolutional neural networks, [SDV.IB]Life Sciences [q-bio]/Bioengineering, limited-angle reconstruction, Electrical and Electronic Engineering, Computed tomography, Reconstruction algorithms, Instrumentation
Abstract

International audience; Dual-energy computed tomography (DECT) is a fully functional instrument for disease detection in clinical practice because of its ability to identify substances and quantify materials. In some practical applications, due to the limitation of scanning conditions, projection data can only be collected from a limited angle, and the consistency of measurement cannot be guaranteed. The existing DECT reconstruction methods fail to address well the severe artifacts and noise in DECT images caused by limited-angle scanning. In this article, we proposed a self-prior enhanced artifact removal network (SPEAR-Net) for limited-angle DECT, which can effectively combine the complementary information in the high- and low-energy domains and self-prior information to contribute positively to the reconstruction of high-quality DECT images. The SPEAR-Net consists of an image-domain self-prior network (IP-Net), two dual-energy image-domain self-prior networks (DIP-Nets), and a dual-energy sinogram-domain self-prior network (DSP-Net). Specifically, the IP-Net and DIP-Net are adopted to extract the features of the DECT reconstructed images under dual quarter scanning as prior information. The self-prior projection obtained from the forward projection of the prior computed tomography (CT) image is harnessed by DSP-Net to complete the dual-energy limited-angle projection data and to facilitate the performance of SPEAR-Net in removing artifacts in the reconstructed dual-energy images. Qualitative and quantitative analyses demonstrate the superior capability of SPEAR-Net in dual-energy limited-angle projection data complementation, detail preservation, and artifact removal. Two popular DECT applications, virtual noncontrast (VNC) imaging and iodine contrast agent quantification, reveal that images reconstructed by SPEAR-Net have promising clinical significance.

Published
2023

2. An Iterative Centroid Approach for Diffeomorphic Online Atlasing

Author

Antoine Legouhy, Francois Rousseau, Christian Barillot, Olivier Commowick, Neuroimagerie: méthodes et applications (EMPENN), Institut National de la Santé et de la Recherche Médicale (INSERM)-Inria Rennes – Bretagne Atlantique, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-SIGNAL, IMAGE ET LANGAGE (IRISA-D6), Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-Institut National de Recherche en Informatique et en Automatique (Inria)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Rennes (UR)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut de Recherche en Informatique et Systèmes Aléatoires (IRISA), Institut National des Sciences Appliquées (INSA)-Institut National des Sciences Appliquées (INSA)-Université de Bretagne Sud (UBS)-École normale supérieure - Rennes (ENS Rennes)-CentraleSupélec-Centre National de la Recherche Scientifique (CNRS)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique (IMT Atlantique), ANR-15-CE23-0009,MAIA,Analyse multiphysiques fondée sur l'imagerie pour la compréhension du développement cérébral des prématurés(2015), Commowick, Olivier, and Analyse multiphysiques fondée sur l'imagerie pour la compréhension du développement cérébral des prématurés - - MAIA2015 - ANR-15-CE23-0009 - AAPG2015 - VALID

Subjects
Iterative centroid, Radiological and Ultrasound Technology, Physics::Instrumentation and Detectors, [INFO.INFO-IM] Computer Science [cs]/Medical Imaging, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Atlasing, Online, Electrical and Electronic Engineering, Software, Computer Science Applications
Abstract

International audience; Online atlasing, i.e., incrementing an atlas with new images as they are acquired, is key when performing studies on very large, or still being gathered, databases. Regular approaches to atlasing however do not focus on this aspect and impose a complete reconstruction of the atlas when adding images. We propose instead a diffeomorphic online atlasing method that allows gradual updates to an atlas. In this iterative centroid approach, we integrate new subjects in the atlas in an iterative manner, gradually moving the centroid of the images towards its final position. This leads to a computationally cheap approach since it only necessitates one additional registration per new subject added. We validate our approach on several experiments with three main goals: 1-to evaluate atlas image quality of the obtained atlases with sharpness and overlap measures, 2-to assess the deviation in terms of transformations with respect to a conventional atlasing method and 3-to compare its computational time with regular approaches of the literature. We demonstrate that the transformations divergence with respect to a stateof-the-art atlas construction method is small and reaches a plateau, that the two construction methods have the same ability to map subject homologous regions onto a common space and produce images of equivalent quality. The computational time of our approach is also drastically reduced for regular updates. Finally, we also present a direct extension of our method to update spatio-temporal atlases, especially useful for developmental studies.

Published
2022

3. Characterization and Inference of Graph Diffusion Processes From Observations of Stationary Signals

Author

Bastien Pasdeloup, Dominique Pastor, Gregoire Mercier, Michael G. Rabbat, Vincent Gripon, Lab-STICC_TB_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance ( Lab-STICC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques ( IBNM ), Université de Brest ( UBO ) -Université européenne de Bretagne ( UEB ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ), Département Signal et Communications ( SC ), IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ), Département Electronique ( ELEC ), École Nationale d'Ingénieurs de Brest ( ENIB ) -Université de Bretagne Sud ( UBS ) -Université de Brest ( UBO ) -ENSTA Bretagne-Institut Mines-Télécom [Paris]-Centre National de la Recherche Scientifique ( CNRS ) -Université Bretagne Loire ( UBL ) -IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ), Lab-STICC_TB_CACS_IAS, Département lmage et Traitement Information ( ITI ), McGill University, Département Electronique (IMT Atlantique - ELEC), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Lab-STICC_IMTA_CACS_IAS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Département Image et Traitement Information (ITI), Université européenne de Bretagne - European University of Brittany (UEB)-Télécom Bretagne-Institut Mines-Télécom [Paris] (IMT), Département Signal et Communications (SC), Department of Electrical and Computer Engineering [Montréal], McGill University = Université McGill [Montréal, Canada], École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Département Signal et Communications (IMT Atlantique - SC), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL), and Département lmage et Traitement Information (IMT Atlantique - ITI)

Subjects
FOS: Computer and information sciences, Computer Networks and Communications, Computer science, Convex set, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, 02 engineering and technology, 01 natural sciences, [INFO.INFO-AI]Computer Science [cs]/Artificial Intelligence [cs.AI], 010104 statistics & probability, [INFO.INFO-LG]Computer Science [cs]/Machine Learning [cs.LG], [INFO.INFO-TS]Computer Science [cs]/Signal and Image Processing, Computer Science - Data Structures and Algorithms, Prior probability, 0202 electrical engineering, electronic engineering, information engineering, Symmetric matrix, Data Structures and Algorithms (cs.DS), 0101 mathematics, ComputingMilieux_MISCELLANEOUS, Eigenvalues and eigenvectors, Signal processing, [INFO.INFO-GT]Computer Science [cs]/Computer Science and Game Theory [cs.GT], Covariance matrix, Covariance matrice, diffusion processes, 020206 networking & telecommunications, eigenvalues ans eigenfuncions, [SPI.TRON]Engineering Sciences [physics]/Electronics, Fourier transforms, [ SPI.TRON ] Engineering Sciences [physics]/Electronics, Laple equation, Signal Processing, Symetric matrices, Topological graph theory, Laplacian matrix, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithm, Information Systems
Abstract

Many tools from the field of graph signal processing exploit knowledge of the underlying graph's structure (e.g., as encoded in the Laplacian matrix) to process signals on the graph. Therefore, in the case when no graph is available, graph signal processing tools cannot be used anymore. Researchers have proposed approaches to infer a graph topology from observations of signals on its nodes. Since the problem is ill-posed, these approaches make assumptions, such as smoothness of the signals on the graph, or sparsity priors. In this paper, we propose a characterization of the space of valid graphs, in the sense that they can explain stationary signals. To simplify the exposition in this paper, we focus here on the case where signals were i.i.d. at some point back in time and were observed after diffusion on a graph. We show that the set of graphs verifying this assumption has a strong connection with the eigenvectors of the covariance matrix, and forms a convex set. Along with a theoretical study in which these eigenvectors are assumed to be known, we consider the practical case when the observations are noisy, and experimentally observe how fast the set of valid graphs converges to the set obtained when the exact eigenvectors are known, as the number of observations grows. To illustrate how this characterization can be used for graph recovery, we present two methods for selecting a particular point in this set under chosen criteria, namely graph simplicity and sparsity. Additionally, we introduce a measure to evaluate how much a graph is adapted to signals under a stationarity assumption. Finally, we evaluate how state-of-the-art methods relate to this framework through experiments on a dataset of temperatures., Submitted to IEEE Transactions on Signal and Information Processing over Networks

Published
2018

4. Fishing Gear Identification From Vessel-Monitoring-System-Based Fishing Vessel Trajectories

Author

Marza Ihsan Marzuki, V. Kerbaol, Rene Garello, Philippe Gaspar, Ronan Fablet, Lab-STICC_TB_CID_TOMS, Laboratoire des sciences et techniques de l'information, de la communication et de la connaissance (Lab-STICC), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Télécom Bretagne-Institut Brestois du Numérique et des Mathématiques (IBNM), Université de Brest (UBO)-Université européenne de Bretagne - European University of Brittany (UEB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS), Département Signal et Communications (IMT Atlantique - SC), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), École Nationale d'Ingénieurs de Brest (ENIB)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Institut Mines-Télécom [Paris] (IMT)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT), Collecte Localisation Satellites (Entreprise) (CLS), Département lmage et Traitement Information (IMT Atlantique - ITI), Institut Mines-Télécom [Paris] (IMT)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-École Nationale d'Ingénieurs de Brest (ENIB)-École Nationale Supérieure de Techniques Avancées Bretagne (ENSTA Bretagne)-Université de Bretagne Sud (UBS)-Université de Brest (UBO)-Centre National de la Recherche Scientifique (CNRS)-Université Bretagne Loire (UBL)

Subjects
0106 biological sciences, Marine conservation, Engineering, Fishing, Feature extraction, Ocean Engineering, 02 engineering and technology, computer.software_genre, 01 natural sciences, GeneralLiterature_MISCELLANEOUS, Data modeling, Vessel monitoring system, 0202 electrical engineering, electronic engineering, information engineering, 14. Life underwater, Electrical and Electronic Engineering, ComputingMethodologies_COMPUTERGRAPHICS, business.industry, 010604 marine biology & hydrobiology, Mechanical Engineering, Random forest, Support vector machine, Identification (information), 020201 artificial intelligence & image processing, Data mining, [SDE.BE]Environmental Sciences/Biodiversity and Ecology, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, computer, Marine engineering
Abstract

International audience; The surveillance of illegal fishing activities is a critical issue for the management of marine resources. In this study, we investigate the space-based monitoring of fishing vessel activities using VMS (Vessel Monitoring System) trajectory data. Our specific objective is the automatic recognition of the employed fishing gear type from VMS data. The proposed approach combines the extraction of new VMS-derived features, issued from the non- supervised identification and characterization of gear-specific movement patterns, and supervised machine learning, namely RF (Random Forest) and SVM (Support Vector Machine). We explore the use of the proposed features jointly to more classical ones (e.g., mean position and sinuosity index). Overall, we reach recognition performance greater than 97% for the considered Indonesian fisheries and present an application to the detection of abnormal fishing vessel behaviors with respect to the registered fishing gear. We further discuss the relevance of the proposed approach and its potential for the operational monitoring of fishing vessel activities.

Published
2018

5. Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band Selection and Knowledge Extraction Techniques

Author

Basel Solaiman, Imed Riadh Farah, Mohamed Farah, Akrem Sellami, Ecole nationale supérieure des sciences de l'informatique ( ENSI Tunis ), Département lmage et Traitement Information ( ITI ), IMT Atlantique Bretagne-Pays de la Loire ( IMT Atlantique ), École Nationale des Sciences de l'Informatique [Manouba] (ENSI), Université de la Manouba [Tunisie] (UMA), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), and Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)

Subjects
Atmospheric Science, business.industry, Computer science, Semantic interpretation, Feature extraction, 0211 other engineering and technologies, Hyperspectral imaging, Pattern recognition, [ SPI.SIGNAL ] Engineering Sciences [physics]/Signal and Image processing, 02 engineering and technology, Spectral bands, Real image, Knowledge extraction, 0202 electrical engineering, electronic engineering, information engineering, 020201 artificial intelligence & image processing, Artificial intelligence, Computers in Earth Sciences, Projection (set theory), business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Selection (genetic algorithm), 021101 geological & geomatics engineering
Abstract

International audience; In this paper, we propose a novel adaptive band selection approach for hyperspectral image semantic interpretation. This approach is based on constrained band selection (CBS) method and extracted knowledge coming from tensor locality preserving projection. The extracted knowledge is presented as a set of rules which are used to evaluate the importance of spectral bands for classes discrimination. Based on these extracted rules and the CBS approach, relevant bands are selected to enhance the hyperspectral image semantic interpretation. The main advantage of the proposed adaptive band selection approach is to allow the automatic selection of discriminant, distinctive and informative spectral bands, and improve the semantic interpretation of hyperspectral images. Experimental results on real images show that the proposed band selection approach reaches competitive good performances, in terms of classification accuracy. Hyperspectral Imagery Semantic Interpretation Based on Adaptive Constrained Band... | Request PDF. Available from: https://www.researchgate.net/publication/323194459_Hyperspectral_Imagery_Semantic_Interpretation_Based_on_Adaptive_Constrained_Band_Selection_and_Knowledge_Extraction_Techniques [accessed Feb 16 2018].

Published
2018

6. A Unified Approach to Diffusion Direction Sensitive Slice Registration and 3-D DTI Reconstruction From Moving Fetal Brain Anatomy

Author

Mads Fogtmann, Teresa Chapman, Sharmishtaa Seshamani, Christopher D. Kroenke, Xi Cheng, Colin Studholme, Jakob Wilm, François Rousseau, Biomedical Image Computing Group, Department of Pediatrics, Bioengineering and Radiology (University of Washington) (BICG), Advanced Imaging Research Center, Portland, Department of Radiology (Seattle Children's Hospital), DTU Informatics, Technical University of Denmark [Lyngby] (DTU), Département lmage et Traitement Information (IMT Atlantique - ITI), IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT), Laboratoire de Traitement de l'Information Medicale (LaTIM), Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Institut Brestois Santé Agro Matière (IBSAM), Université de Brest (UBO), Rousseau, François, Danmarks Tekniske Universitet = Technical University of Denmark (DTU), IMT Atlantique (IMT Atlantique), and Université de Brest (UBO)-Institut National de la Santé et de la Recherche Médicale (INSERM)-Centre Hospitalier Régional Universitaire de Brest (CHRU Brest)-IMT Atlantique (IMT Atlantique)

Subjects
Point spread function, [SPI] Engineering Sciences [physics], Computer science, Image registration, Iterative reconstruction, Article, 030218 nuclear medicine & medical imaging, Fetal brain, White matter, [SPI]Engineering Sciences [physics], 03 medical and health sciences, Fetus, Imaging, Three-Dimensional, 0302 clinical medicine, Pregnancy, Prenatal Diagnosis, Motion estimation, medicine, Humans, Computer vision, Electrical and Electronic Engineering, Image resolution, ComputingMilieux_MISCELLANEOUS, [SPI.SIGNAL] Engineering Sciences [physics]/Signal and Image processing, Radiological and Ultrasound Technology, medicine.diagnostic_test, business.industry, Orientation (computer vision), Brain, Magnetic resonance imaging, Anatomy, Computer Science Applications, Diffusion Tensor Imaging, medicine.anatomical_structure, Female, Artificial intelligence, Deconvolution, business, [SPI.SIGNAL]Engineering Sciences [physics]/Signal and Image processing, Algorithms, 030217 neurology & neurosurgery, Software, Diffusion MRI, Interpolation
Abstract

This paper presents an approach to 3-D diffusion tensor image (DTI) reconstruction from multi-slice diffusion weighted (DW) magnetic resonance imaging acquisitions of the moving fetal brain. Motion scatters the slice measurements in the spatial and spherical diffusion domain with respect to the underlying anatomy. Previous image registration techniques have been described to estimate the between slice fetal head motion, allowing the reconstruction of 3D a diffusion estimate on a regular grid using interpolation. We propose Approach to Unified Diffusion Sensitive Slice Alignment and Reconstruction (AUDiSSAR) that explicitly formulates a process for diffusion direction sensitive DW-slice-to-DTI-volume alignment. This also incorporates image resolution modeling to iteratively deconvolve the effects of the imaging point spread function using the multiple views provided by thick slices acquired in different anatomical planes. The algorithm is implemented using a multi-resolution iterative scheme and multiple real and synthetic data are used to evaluate the performance of the technique. An accuracy experiment using synthetically created motion data of an adult head and an experiment using synthetic motion added to sedated fetal monkey dataset show a significant improvement in motion-trajectory estimation compared to current state-of-the-art approaches. The performance of the method is then evaluated on challenging but clinically typical in utero fetal scans of four different human cases, showing improved rendition of cortical anatomy and extraction of white matter tracts. While the experimental work focuses on DTI reconstruction (second-order tensor model), the proposed reconstruction framework can employ any 5-D diffusion volume model that can be represented by the spatial parameterizations of an orientation distribution function.

Published
2014

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