Search

Your search keyword '"EPSRC EP/H046410/1 EP/K005278"' showing total 2 results
Start Over Author "EPSRC EP/H046410/1 EP/K005278"
2 results on '"EPSRC EP/H046410/1 EP/K005278"'

1. Spatiotemporal analysis for detection of pre-symptomatic shape changes in neurodegenerative diseases: Initial application to the GENFI cohort

Author

Cury, Claire, Durrleman, Stanley, Cash, David, Lorenzi, Marco, Nicholas, Jennifer, Bocchetta, Martina, Van Swieten, John, Borroni, Barbara, Galimberti, Daniela, Masellis, Mario, Tartaglia, Maria Carmela, Rowe, James, Graff, Caroline, Tagliavini, Fabrizio, Frisoni, Giovanni, Laforce, Robert, Finger, Elizabeth, de Mendonça, Alexandre, Sorbi, Sandro, Ourselin, Sébastien, Rohrer, Jonathan, Modat, Marc, Andersson, Christin, Archetti, Silvana, Arighi, Andrea, Benussi, Luisa, Black, Sandra, Cosseddu, Maura, Fallstrm, Marie, Ferreira, Carlos, Fenoglio, Chiara, Fox, Nick, Freedman, Morris, Fumagalli, Giorgio, Gazzina, Stefano, Ghidoni, Robert, Grisoli, Marina, Jelic, Vesna, Jiskoot, Lize, Keren, Ron, Lombardi, Gemma, Maruta, Carolina, Meeter, Lieke, van Minkelen, Rick, Nacmias, Benedetta, Ijerstedt, Linn, Padovani, Alessandro, Panman, Jessica, Pievani, Michela, Polito, Cristina, Premi, Enrico, Prioni, Sara, Rademakers, Rosa, Redaelli, Veronica, Rogaeva, Ekaterina, Rossi, Giacomina, Rossor, Martin, Scarpini, Elio, Tang-Wai, David, Thonberg, Hakan, Tiraboschi, Pietro, Verdelho, Ana, Warren, Jason, Department of Medical Physics and Biomedical Engineering (UCL), University College of London [London] (UCL), Dementia Research Centre [London] (DRC), 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)-SIGNAUX ET IMAGES NUMÉRIQUES, ROBOTIQUE (IRISA-D5), 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), Algorithms, models and methods for images and signals of the human brain (ARAMIS), Sorbonne Université (SU)-Inria de Paris, Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau = Paris Brain Institute (ICM), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), E-Patient : Images, données & mOdèles pour la médeciNe numériquE (EPIONE), 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), London School of Hygiene and Tropical Medicine (LSHTM), Erasmus University Medical Center [Rotterdam] (Erasmus MC), University of Brescia, Centro Dino Ferrari [Milano], Università degli Studi di Milano = University of Milan (UNIMI)-Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Sunnybrook Research Institute [Toronto] (SRI), Sunnybrook Health Sciences Centre, Tanz Center Research in Neurodegenerative Diseases [Toronto], University of Toronto, University of Cambridge [UK] (CAM), Karolinska Institutet [Stockholm], Karolinska University Hospital [Stockholm], Fondazione IRCCS Istituto Neurologico 'Carlo Besta', Centro San Giovanni di Dio, Fatebenefratelli, Brescia (IRCCS), Università degli Studi di Brescia = University of Brescia (UniBs), Université Laval [Québec] (ULaval), University of Western Ontario (UWO), Faculdade de Medicina [Lisboa], Universidade de Lisboa = University of Lisbon (ULISBOA), Università degli Studi di Firenze = University of Florence (UniFI), Imaging Sciences and Biomedical Engineering Division [London], Guy's and St Thomas' Hospital [London]-King‘s College London, Civic Hospital of Brescia, Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, IRCCS Fatebenefratelli - Brescia, Institut de Recherche en Génie Civil et Mécanique (GeM), Université de Nantes - UFR des Sciences et des Techniques (UN UFR ST), Université de Nantes (UN)-Université de Nantes (UN)-École Centrale de Nantes (ECN)-Centre National de la Recherche Scientifique (CNRS), UCL, Institute of Neurology [London], Toronto Western Hospital, Neuroimaging and Telemedicine (LENITEM), Mayo Clinic [Jacksonville], Marco Lorenzi received funding from the EPSRC (EP/J020990/1). Jennifer Nicholas is supported by UK Medical Research Council (grant MR/M023664/1). David Cash is supported by grants from the Alzheimer Society(AS-PG-15-025), Alzheimers Research UK (ARUK-PG2014-1946) and Medical Research Council UK (MR/M023664/1). JBR is supported by the Wellcome Trust (103838). Jonathan D. Rohrer is an MRC Clinician Scientist and has received funding from the NIHR Rare Diseases Translational Research Collaboration. Se361 bastien Ourselin receives funding from the EPSRC (EP/H046410/1, EP/K005278), the MRC (MR/J01107X/1), the NIHR Biomedical Research Unit (Dementia) at UCL and the National Institute for Health Research University College London Hospitals Biomedical Research Centre (NIHR BRC UCLH/UCL High Impact Initiative- BW.mn.BRC10269). Marc Modat is supported by the UCL Leonard Wolfson Experimental Neurology Centre, ANR-10-IAHU-0006,IHU-A-ICM,Institut de Neurosciences Translationnelles de Paris(2010), ANR-15-IDEX-0001,UCA JEDI,Idex UCA JEDI(2015), European Project: 601055,EC:FP7:ICT,FP7-ICT-2011-9,VPH-DARE@IT(2013), European Project: 666992,H2020 Pilier Societal Challenges,H2020-PHC-2015-two-stage,EuroPOND(2016), European Project: 678304,H2020 ERC,ERC-2015-STG,LEASP(2016), Cury, Claire, Institut de Neurosciences Translationnelles de Paris - - IHU-A-ICM2010 - ANR-10-IAHU-0006 - IAHU - VALID, Idex UCA JEDI - - UCA JEDI2015 - ANR-15-IDEX-0001 - IDEX - VALID, VPH Dementia Research Enabled by IT - VPH-DARE@IT - - EC:FP7:ICT2013-04-01 - 2017-03-31 - 601055 - VALID, Data-driven models for Progression Of Neurological Disease - EuroPOND - - H2020 Pilier Societal Challenges2016-01-01 - 2019-12-31 - 666992 - VALID, Learning spatiotemporal patterns in longitudinal image data sets of the aging brain - LEASP - - H2020 ERC2016-09-01 - 2021-08-31 - 678304 - VALID, Neurology, Empenn, Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National de Recherche en Informatique et en Automatique (Inria)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Université de Rennes (UNIV-RENNES)-CentraleSupélec-IMT Atlantique Bretagne-Pays de la Loire (IMT Atlantique), Institut Mines-Télécom [Paris] (IMT)-Institut Mines-Télécom [Paris] (IMT)-Université de Bretagne Sud (UBS)-Institut National des Sciences Appliquées - Rennes (INSA Rennes), Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-Institut National des Sciences Appliquées (INSA)-Université de Rennes (UNIV-RENNES)-École normale supérieure - Rennes (ENS Rennes)-Centre National de la Recherche Scientifique (CNRS)-Université de Rennes 1 (UR1), Institut National de Recherche en Informatique et en Automatique (Inria)-Institut National de Recherche en Informatique et en Automatique (Inria)-Institut du Cerveau et de la Moëlle Epinière = Brain and Spine Institute (ICM), Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS), Università degli Studi di Milano [Milano] (UNIMI)-Fondazione IRCCS Ca' Granda - Ospedale Maggiore Policlinico, Università degli Studi di Brescia [Brescia], Universidade de Lisboa (ULISBOA), Università degli Studi di Firenze = University of Florence [Firenze] (UNIFI), Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)-Institut National de la Santé et de la Recherche Médicale (INSERM)-CHU Pitié-Salpêtrière [AP-HP], and Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Assistance publique - Hôpitaux de Paris (AP-HP) (AP-HP)-Sorbonne Université (SU)-Centre National de la Recherche Scientifique (CNRS)

Subjects
Male, Prodromal Symptoms, Neuroimaging, Middle Aged, Magnetic Resonance Imaging, Article, Shape analysis, Clustering, Cohort Studies, Computational anatomy, Spatio-Temporal Analysis, Thalamus, Frontotemporal Dementia, ddc:618.97, Parallel transport, Spatiotemporal geodesic regression, Humans, [SDV.NEU]Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC], Female, [SDV.NEU] Life Sciences [q-bio]/Neurons and Cognition [q-bio.NC]
Abstract

Brain atrophy as measured from structural MR images, is one of the primary imaging biomarkers used to track neurodegenerative disease progression. In diseases such as frontotemporal dementia or Alzheimer's disease, atrophy can be observed in key brain structures years before any clinical symptoms are present. Atrophy is most commonly captured as volume change of key structures and the shape changes of these structures are typically not analysed despite being potentially more sensitive than summary volume statistics over the entire structure. In this paper we propose a spatiotemporal analysis pipeline based on Large Diffeomorphic Deformation Metric Mapping (LDDMM) to detect shape changes from volumetric MRI scans. We applied our framework to a cohort of individuals with genetic variants of frontotemporal dementia and healthy controls from the Genetic FTD Initiative (GENFI) study. Our method, take full advantage of the LDDMM framework, and relies on the creation of a population specific average spatiotemporal trajectory of a relevant brain structure of interest, the thalamus in our case. The residuals from each patient data to the average spatiotemporal trajectory are then clustered and studied to assess when presymptomatic mutation carriers differ from healthy control subjects. We found statistical differences in shape in the anterior region of the thalamus at least five years before the mutation carrier subjects develop any clinical symptoms. This region of the thalamus has been shown to be predominantly connected to the frontal lobe, consistent with the pattern of cortical atrophy seen in the disease., Graphical abstract Image 1, Highlights • Clustering shape parametrisation allows local shape analysis. • Thalamic shape changes appear 5 years before onset of fronto temporal dementia. • Shape changes seem to occur before volume changes. • Pre-symptomatic shape changes in thalamus are dorsofrontal, where connecting to temporal lobes.

Published
2019
Full Text
View/download PDF

2. Iterative framework for the joint segmentation and CT synthesis of MR images: application to MRI-only radiotherapy treatment planning

Author

Burgos, Ninon, Guerreiro, Filipa, McClelland, Jamie, Presles, Benoit, Modat, Marc, Nill, Simeon, Dearnaley, David, deSouza, Nandita, Oelfke, Uwe, Knopf, Antje-Christin, Ourselin, Sebastien, Cardoso, M. Jorge, Translational imaging group [London] (TIG), Department of Medical Physics and Biomedical Engineering (UCL), University College of London [London] (UCL)-University College of London [London] (UCL)-Centre for Medical Image Computing (CMIC), University College of London [London] (UCL), University Medical Center [Utrecht], Centre for Medical Image Computing (CMIC), Dementia Research Centre [London] (DRC), Royal Marsden NHS Foundation Trust & Institute of Cancer Research, Joint Department of Physics, Institute of Cancer Research (ICR)-Royal Marsden NHS Foundation Trust, Academic Urology Unit, Institute of Cancer Research and Royal Marsden NHS, CRUK Center of Cancer Imaging, ICR RMH, Institute of Cancer Research and Royal Marsden Hospital, Department of Medical Oncology, University Medical Center Groningen, University of Groningen [Groningen], CMIC-EPSRC EP/M020533/1, EPSRC EP/H046410/1 EP/K005278, NIHR Biomedical Research Centre UCLH/UCL High Impact Initiative BW.mn.BRC10269, CRUK 'ART-NET' project A21993, UCL Leonard Wolfson Experimental Neurology Centre PR/ylr/18575, NHS, MRC, and Department of Health C1060/A10334 C1060/A16464

Subjects
Paper, Registration, MRI-only RTP, Generation, Attenuation Correction, pseudo CT, Electron-Density, atlas-based methods, [INFO.INFO-IM]Computer Science [cs]/Medical Imaging, Image Processing, Computer-Assisted, Pseudo-Ct, Fusion, Radiometry, Special section: Recent Progress in Applications of Computing to Radiotherapy, Radiotherapy Planning, Computer-Assisted, segmentation, Prostate, Brain, Radiation-Therapy, Magnetic Resonance Imaging, [PHYS.PHYS.PHYS-MED-PH]Physics [physics]/Physics [physics]/Medical Physics [physics.med-ph], [SDV.IB]Life Sciences [q-bio]/Bioengineering, Joints, Atlas, image synthesis, Tomography, X-Ray Computed
Abstract

To tackle the problem of magnetic resonance imaging (MRI)-only radiotherapy treatment planning (RTP), we propose a multi-atlas information propagation scheme that jointly segments organs and generates pseudo x-ray computed tomography (CT) data from structural MR images (T1-weighted and T2-weighted). As the performance of the method strongly depends on the quality of the atlas database composed of multiple sets of aligned MR, CT and segmented images, we also propose a robust way of registering atlas MR and CT images, which combines structure-guided registration, and CT and MR image synthesis. We first evaluated the proposed framework in terms of segmentation and CT synthesis accuracy on 15 subjects with prostate cancer. The segmentations obtained with the proposed method were compared using the Dice score coefficient (DSC) to the manual segmentations. Mean DSCs of 0.73, 0.90, 0.77 and 0.90 were obtained for the prostate, bladder, rectum and femur heads, respectively. The mean absolute error (MAE) and the mean error (ME) were computed between the reference CTs (non-rigidly aligned to the MRs) and the pseudo CTs generated with the proposed method. The MAE was on average \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$45.7\pm 4.6$ \end{document}45.7±4.6 HU and the ME \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$-1.6\pm 7.7$ \end{document}−1.6±7.7 HU. We then performed a dosimetric evaluation by re-calculating plans on the pseudo CTs and comparing them to the plans optimised on the reference CTs. We compared the cumulative dose volume histograms (DVH) obtained for the pseudo CTs to the DVH obtained for the reference CTs in the planning target volume (PTV) located in the prostate, and in the organs at risk at different DVH points. We obtained average differences of \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$-0.14 \% $ \end{document}−0.14% in the PTV for \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${{D}_{98 \% }}$ \end{document}D98%, and between \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}$-0.14 \% $ \end{document}−0.14% and 0.05% in the PTV, bladder, rectum and femur heads for Dmean and \documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{upgreek} \usepackage{mathrsfs} \setlength{\oddsidemargin}{-69pt} \begin{document} }{}${{D}_{2 \% }}$ \end{document}D2%. Overall, we demonstrate that the proposed framework is able to automatically generate accurate pseudo CT images and segmentations in the pelvic region, potentially bypassing the need for CT scan for accurate RTP.

Published
2017
Full Text
View/download PDF

Catalog

Books, media, physical & digital resources
See catalog results