1
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Renton AI, Dao TT, Johnstone T, Civier O, Sullivan RP, White DJ, Lyons P, Slade BM, Abbott DF, Amos TJ, Bollmann S, Botting A, Campbell MEJ, Chang J, Close TG, Dörig M, Eckstein K, Egan GF, Evas S, Flandin G, Garner KG, Garrido MI, Ghosh SS, Grignard M, Halchenko YO, Hannan AJ, Heinsfeld AS, Huber L, Hughes ME, Kaczmarzyk JR, Kasper L, Kuhlmann L, Lou K, Mantilla-Ramos YJ, Mattingley JB, Meier ML, Morris J, Narayanan A, Pestilli F, Puce A, Ribeiro FL, Rogasch NC, Rorden C, Schira MM, Shaw TB, Sowman PF, Spitz G, Stewart AW, Ye X, Zhu JD, Narayanan A, Bollmann S. Neurodesk: an accessible, flexible and portable data analysis environment for reproducible neuroimaging. Nat Methods 2024; 21:804-808. [PMID: 38191935 PMCID: PMC11180540 DOI: 10.1038/s41592-023-02145-x] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 03/02/2023] [Accepted: 11/27/2023] [Indexed: 01/10/2024]
Abstract
Neuroimaging research requires purpose-built analysis software, which is challenging to install and may produce different results across computing environments. The community-oriented, open-source Neurodesk platform ( https://www.neurodesk.org/ ) harnesses a comprehensive and growing suite of neuroimaging software containers. Neurodesk includes a browser-accessible virtual desktop, command-line interface and computational notebook compatibility, allowing for accessible, flexible, portable and fully reproducible neuroimaging analysis on personal workstations, high-performance computers and the cloud.
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Affiliation(s)
- Angela I Renton
- The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia.
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia.
| | - Thuy T Dao
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Tom Johnstone
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Oren Civier
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Ryan P Sullivan
- The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
| | - David J White
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Paris Lyons
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Benjamin M Slade
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - David F Abbott
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Toluwani J Amos
- School of Life Science and Technology, University of Electronic Science and Technology, Chengdu, China
| | - Saskia Bollmann
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Andy Botting
- Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
| | - Megan E J Campbell
- School of Psychological Sciences, University of Newcastle, Newcastle, New South Wales, Australia
- Hunter Medical Research Institute Imaging Centre, Newcastle, New South Wales, Australia
| | - Jeryn Chang
- The University of Queensland, School of Biomedical Sciences, St Lucia, Brisbane, Queensland, Australia
| | - Thomas G Close
- The University of Sydney, School of Biomedical Engineering, Sydney, New South Wales, Australia
| | - Monika Dörig
- Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Korbinian Eckstein
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Gary F Egan
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Melbourne, Victoria, Australia
| | - Stefanie Evas
- School of Psychology, University of Adelaide, Adelaide, South Australia, Australia
- Human Health, Health & Biosecurity, CSIRO, Adelaide, South Australia, Australia
| | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Kelly G Garner
- School of Psychology, University of New South Wales, Sydney, New South Wales, Australia
- The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
| | - Marta I Garrido
- Melbourne School of Psychological Sciences, he University of Melbourne, Melbourne, Victoria, Australia
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne, Melbourne, Victoria, Australia
| | - Satrajit S Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA, USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Martin Grignard
- GIGA CRC In-Vivo Imaging, University of Liège, Liège, Belgium
| | - Yaroslav O Halchenko
- Center for Open Neuroscience, Department of Psychological and Brain Sciences, Dartmouth College, Hanover, NH, USA
| | - Anthony J Hannan
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Melbourne, Victoria, Australia
| | - Anibal S Heinsfeld
- Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
| | - Laurentius Huber
- National Institute of Mental Health (NIMH), National Institutes Health, Bethesda, MD, USA
| | - Matthew E Hughes
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn, Victoria, Australia
| | - Jakub R Kaczmarzyk
- Department of Biomedical Informatics, Stony Brook University, New York, NY, USA
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, New York, NY, USA
| | - Lars Kasper
- BRAIN-TO Lab, Krembil Brain Institute, University Health Network, Toronto, Ontario, Canada
- Translational Neuromodeling Unit, Institute for Biomedical Engineering, University of Zurich and ETH Zurich, Zurich, Switzerland
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Melbourne, Victoria, Australia
| | - Kexin Lou
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- Department of Biomedical Engineering, Southern University of Science and Technology, Shenzhen, China
| | - Yorguin-Jose Mantilla-Ramos
- Grupo Neuropsicología y Conducta (GRUNECO), Facultad de Medicina, Universidad de Antioquia, Medellín, Colombia
| | - Jason B Mattingley
- The University of Queensland, Queensland Brain Institute, St Lucia, Brisbane, Queensland, Australia
- The University of Queensland, School of Psychology, St Lucia, Brisbane, Queensland, Australia
| | - Michael L Meier
- Integrative Spinal Research, Department of Chiropractic Medicine, Balgrist University Hospital, University of Zurich, Zurich, Switzerland
| | - Jo Morris
- Australian Research Data Commons (ARDC), Sydney, New South Wales, Australia
| | - Akshaiy Narayanan
- School of Computer Science, The University of Auckland, Auckland, New Zealand
| | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Institute for Neuroscience, Center For Learning and Memory, The University of Texas at Austin, Austin, TX, USA
| | - Aina Puce
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN, USA
| | - Fernanda L Ribeiro
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Nigel C Rogasch
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Adelaide, South Australia, Australia
- Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, South Australia, Australia
| | - Chris Rorden
- McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia, SC, USA
| | - Mark M Schira
- School of Psychology, University of Wollongong, Wollongong, New South Wales, Australia
| | - Thomas B Shaw
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Queensland, Australia
| | - Paul F Sowman
- Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
| | - Gershon Spitz
- Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Melbourne, Victoria, Australia
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Melbourne, Victoria, Australia
| | - Ashley W Stewart
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia
| | - Xincheng Ye
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia
| | - Judy D Zhu
- Macquarie University, School of Psychological Sciences, Sydney, New South Wales, Australia
| | - Aswin Narayanan
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia
| | - Steffen Bollmann
- The University of Queensland, School of Electrical Engineering and Computer Science, St Lucia, Brisbane, Queensland, Australia.
- The University of Queensland, Centre for Advanced Imaging, St Lucia, Brisbane, Queensland, Australia.
- ARC Training Centre for Innovation in Biomedical Imaging Technology, The University of Queensland, Brisbane, Queensland, Australia.
- Queensland Digital Health Centre, The University of Queensland, Brisbane, Queensland, Australia.
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2
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Herz C, Vergnet N, Tian S, Aly AH, Jolley MA, Tran N, Arenas G, Lasso A, Schwartz N, O’Neill KE, Yushkevich PA, Pouch AM. Open-source graphical user interface for the creation of synthetic skeletons for medical image analysis. J Med Imaging (Bellingham) 2024; 11:036001. [PMID: 38751729 PMCID: PMC11092146 DOI: 10.1117/1.jmi.11.3.036001] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/25/2023] [Revised: 04/01/2024] [Accepted: 04/18/2024] [Indexed: 05/18/2024] Open
Abstract
Purpose Deformable medial modeling is an inverse skeletonization approach to representing anatomy in medical images, which can be used for statistical shape analysis and assessment of patient-specific anatomical features such as locally varying thickness. It involves deforming a pre-defined synthetic skeleton, or template, to anatomical structures of the same class. The lack of software for creating such skeletons has been a limitation to more widespread use of deformable medial modeling. Therefore, the objective of this work is to present an open-source user interface (UI) for the creation of synthetic skeletons for a range of medial modeling applications in medical imaging. Approach A UI for interactive design of synthetic skeletons was implemented in 3D Slicer, an open-source medical image analysis application. The steps in synthetic skeleton design include importation and skeletonization of a 3D segmentation, followed by interactive 3D point placement and triangulation of the medial surface such that the desired branching configuration of the anatomical structure's medial axis is achieved. Synthetic skeleton design was evaluated in five clinical applications. Compatibility of the synthetic skeletons with open-source software for deformable medial modeling was tested, and representational accuracy of the deformed medial models was evaluated. Results Three users designed synthetic skeletons of anatomies with various topologies: the placenta, aortic root wall, mitral valve, cardiac ventricles, and the uterus. The skeletons were compatible with skeleton-first and boundary-first software for deformable medial modeling. The fitted medial models achieved good representational accuracy with respect to the 3D segmentations from which the synthetic skeletons were generated. Conclusions Synthetic skeleton design has been a practical challenge in leveraging deformable medial modeling for new clinical applications. This work demonstrates an open-source UI for user-friendly design of synthetic skeletons for anatomies with a wide range of topologies.
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Affiliation(s)
- Christian Herz
- Children’s Hospital of Philadelphia, Department of Anesthesiology and Critical Care Medicine, Philadelphia, Pennsylvania, United States
| | - Nicolas Vergnet
- University of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Sijie Tian
- University of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Abdullah H. Aly
- University of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Matthew A. Jolley
- Children’s Hospital of Philadelphia, Department of Anesthesiology and Critical Care Medicine, Philadelphia, Pennsylvania, United States
- Children’s Hospital of Philadelphia, Division of Cardiology, Philadelphia, Pennsylvania, United States
| | - Nathanael Tran
- Jefferson Einstein Hospital, Division of Cardiovascular Diseases, Philadelphia, Pennsylvania, United States
| | - Gabriel Arenas
- University of Pennsylvania, Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Philadelphia, Pennsylvania, United States
| | - Andras Lasso
- Queen’s University, School of Computing, Laboratory for Percutaneous Surgery, Kingston, Ontario, Canada
| | - Nadav Schwartz
- University of Pennsylvania, Division of Maternal Fetal Medicine, Department of Obstetrics and Gynecology, Philadelphia, Pennsylvania, United States
| | - Kathleen E. O’Neill
- University of Pennsylvania, Division of Reproductive Endocrinology and Infertility, Department of Obstetrics and Gynecology, Philadelphia, Pennsylvania, United States
| | - Paul A. Yushkevich
- University of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States
| | - Alison M. Pouch
- University of Pennsylvania, Penn Image Computing and Science Laboratory, Department of Radiology, Philadelphia, Pennsylvania, United States
- University of Pennsylvania, Department of Bioengineering, Philadelphia, Pennsylvania, United States
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3
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Vicory J, Han Y, Prieto JC, Allemang D, Leclercq M, Bowley C, Scheirich H, Fillion-Robin JC, Pizer S, Fishbaugh J, Gerig G, Styner M, Paniagua B. SlicerSALT: From Medical Images to Quantitative Insights of Anatomy. SHAPE IN MEDICAL IMAGING : INTERNATIONAL WORKSHOP, SHAPEMI 2023, HELD IN CONJUNCTION WITH MICCAI 2023, VANCOUVER, BC, CANADA, OCTOBER 8, 2023, PROCEEDINGS. SHAPEMI (WORKSHOP) (2023 : VANCOUVER, B.C.) 2023; 14350:201-210. [PMID: 38250732 PMCID: PMC10798161 DOI: 10.1007/978-3-031-46914-5_16] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/23/2024]
Abstract
Three-dimensional (3D) shape lies at the core of understanding the physical objects that surround us. In the biomedical field, shape analysis has been shown to be powerful in quantifying how anatomy changes with time and disease. The Shape AnaLysis Toolbox (SALT) was created as a vehicle for disseminating advanced shape methodology as an open source, free, and comprehensive software tool. We present new developments in our shape analysis software package, including easy-to-interpret statistical methods to better leverage the quantitative information contained in SALT's shape representations. We also show SlicerPipelines, a module to improve the usability of SALT by facilitating the analysis of large-scale data sets, automating workflows for non-expert users, and allowing the distribution of reproducible workflows.
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Affiliation(s)
| | - Ye Han
- Kitware Inc, Carrboro, NC 27510, USA
| | | | | | | | | | | | | | - Steve Pizer
- University of North Carolina, Chapel Hill, NC 27599, USA
| | | | - Guido Gerig
- New York University, Brooklyn, NY 11201, USA
| | - Martin Styner
- University of North Carolina, Chapel Hill, NC 27599, USA
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4
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Khargonkar N, Paniagua B, Vicory J. SKELETAL POINT REPRESENTATIONS WITH GEOMETRIC DEEP LEARNING. PROCEEDINGS. IEEE INTERNATIONAL SYMPOSIUM ON BIOMEDICAL IMAGING 2023; 2023:10.1109/isbi53787.2023.10230505. [PMID: 38226393 PMCID: PMC10788873 DOI: 10.1109/isbi53787.2023.10230505] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/17/2024]
Abstract
Skeletonization has been a popular shape analysis technique that models both the interior and exterior of an object. Existing template-based calculations of skeletal models from anatomical structures are a time-consuming manual process. Recently, learning-based methods have been used to extract skeletons from 3D shapes. In this work, we propose novel additional geometric terms for calculating skeletal structures of objects. The results are similar to traditional fitted s-reps but but are produced much more quickly. Evaluation on real clinical data shows that the learned model predicts accurate skeletal representations and shows the impact of proposed geometric losses along with using s-reps as weak supervision.
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Affiliation(s)
- Ninad Khargonkar
- The University of Texas at Dallas, Department of Computer Science, Richardson, Texas
| | | | - Jared Vicory
- Kitware, Inc, Medical Computing, Carrboro, North Carolina
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5
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Smith CM, Curthoys IS, Laitman JT. First evidence of the link between internal and external structure of the human inner ear otolith system using 3D morphometric modeling. Sci Rep 2023; 13:4840. [PMID: 36964237 PMCID: PMC10039035 DOI: 10.1038/s41598-023-31235-1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 11/29/2022] [Accepted: 03/08/2023] [Indexed: 03/26/2023] Open
Abstract
Our sense of balance is among the most central of our sensory systems, particularly in the evolution of human positional behavior. The peripheral vestibular system (PVS) comprises the organs responsible for this sense; the semicircular canals (detecting angular acceleration) and otolith organs (utricle and saccule; detecting linear acceleration, vibration, and head tilt). Reconstructing vestibular evolution in the human lineage, however, is problematic. In contrast to considerable study of the canals, relationships between external bone and internal membranous otolith organs (otolith system) remain largely unexplored. This limits our understanding of vestibular functional morphology. This study combines spherical harmonic modeling and landmark-based shape analyses to model the configuration of the human otolith system. Our approach serves two aims: (1) test the hypothesis that bony form covaries with internal membranous anatomy; and (2) create a 3D morphometric model visualizing bony and membranous structure. Results demonstrate significant associations between bony and membranous tissues of the otolith system. These data provide the first evidence that external structure of the human otolith system is directly related to internal anatomy, suggesting a basic biological relationship. Our results visualize this structural relationship, offering new avenues into vestibular biomechanical modeling and assessing the evolution of the human balance system.
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Affiliation(s)
- Christopher M Smith
- Department of Anthropology, The Graduate Center, City University of New York, New York, NY, 10016, USA.
- Center for Anatomy and Functional Morphology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA.
- New York Consortium in Evolutionary Primatology, New York, NY, 10016, USA.
| | - Ian S Curthoys
- Vestibular Research Laboratory, School of Psychology, University of Sydney, Sydney, NSW, 2006, Australia
| | - Jeffrey T Laitman
- Department of Anthropology, The Graduate Center, City University of New York, New York, NY, 10016, USA
- Center for Anatomy and Functional Morphology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
- New York Consortium in Evolutionary Primatology, New York, NY, 10016, USA
- Department of Otolaryngology, Icahn School of Medicine at Mount Sinai, New York, NY, 10029, USA
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6
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Renton AI, Dao TT, Johnstone T, Civier O, Sullivan RP, White DJ, Lyons P, Slade BM, Abbott DF, Amos TJ, Bollmann S, Botting A, Campbell MEJ, Chang J, Close TG, Eckstein K, Egan GF, Evas S, Flandin G, Garner KG, Garrido MI, Ghosh SS, Grignard M, Hannan AJ, Huber R, Kaczmarzyk JR, Kasper L, Kuhlmann L, Lou K, Mantilla-Ramos YJ, Mattingley JB, Morris J, Narayanan A, Pestilli F, Puce A, Ribeiro FL, Rogasch NC, Rorden C, Schira M, Shaw TB, Sowman PF, Spitz G, Stewart A, Ye X, Zhu JD, Hughes ME, Narayanan A, Bollmann S. Neurodesk: An accessible, flexible, and portable data analysis environment for reproducible neuroimaging. RESEARCH SQUARE 2023:rs.3.rs-2649734. [PMID: 36993557 PMCID: PMC10055538 DOI: 10.21203/rs.3.rs-2649734/v1] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Grants] [Track Full Text] [Figures] [Subscribe] [Scholar Register] [Indexed: 06/19/2023]
Abstract
Neuroimaging data analysis often requires purpose-built software, which can be challenging to install and may produce different results across computing environments. Beyond being a roadblock to neuroscientists, these issues of accessibility and portability can hamper the reproducibility of neuroimaging data analysis pipelines. Here, we introduce the Neurodesk platform, which harnesses software containers to support a comprehensive and growing suite of neuroimaging software (https://www.neurodesk.org/). Neurodesk includes a browser-accessible virtual desktop environment and a command line interface, mediating access to containerized neuroimaging software libraries on various computing platforms, including personal and high-performance computers, cloud computing and Jupyter Notebooks. This community-oriented, open-source platform enables a paradigm shift for neuroimaging data analysis, allowing for accessible, flexible, fully reproducible, and portable data analysis pipelines.
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Affiliation(s)
- Angela I. Renton
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Thuy T. Dao
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Tom Johnstone
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Oren Civier
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Ryan P. Sullivan
- The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - David J. White
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Paris Lyons
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Benjamin M. Slade
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - David F. Abbott
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Victoria, Australia
| | - Toluwani J. Amos
- School of Life Science and Technology, University of Electronic Science and Technology, China
| | - Saskia Bollmann
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Andy Botting
- Australian Research Data Commons (ARDC), Australia
| | - Megan E. J. Campbell
- School of Psychological Sciences, University of Newcastle, Australia
- Hunter Medical Research Institute Imaging Centre, Newcastle, Australia
| | - Jeryn Chang
- The University of Queensland, School of Biomedical Sciences, St Lucia 4072, Australia
| | - Thomas G. Close
- The University of Sydney, School of Biomedical Engineering, Sydney, Australia
| | - Korbinian Eckstein
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Gary F. Egan
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia
- Monash Biomedical Imaging, Monash University, Victoria, Australia
| | - Stefanie Evas
- School of Psychology, University of Adelaide, Adelaide, 5000, Australia
- Human Health, Health & Biosecurity, CSIRO, Adelaide, 5000, Australia
| | - Guillaume Flandin
- Wellcome Centre for Human Neuroimaging, University College London, London, UK
| | - Kelly G. Garner
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Psychology, St Lucia 4072, Australia
| | - Marta I. Garrido
- Melbourne School of Psychological Sciences, The University of Melbourne
- Graeme Clark Institute for Biomedical Engineering, The University of Melbourne
| | - Satrajit S. Ghosh
- McGovern Institute for Brain Research, Massachusetts Institute of Technology, Cambridge, MA 02139, USA
- Department of Otolaryngology - Head and Neck Surgery, Harvard Medical School, Boston, MA, USA
| | - Martin Grignard
- GIGA CRC In-Vivo Imaging, University of Liege, Liege, Belgium
| | - Anthony J. Hannan
- The Florey Institute of Neuroscience and Mental Health, The University of Melbourne, Victoria, Australia
| | - Renzo Huber
- Functional Magnetic Resonance Imaging Core Facility (FMRIF), National Institute of Mental Health (NIMH), USA
| | - Jakub R. Kaczmarzyk
- Medical Scientist Training Program, Stony Brook University, Stony Brook, NY, United States of America
- Simons Center for Quantitative Biology, Cold Spring Harbor Laboratory, Cold Spring Harbor, NY, United States of America
| | - Lars Kasper
- Techna Institute, University Health Network, Toronto, Canada
| | - Levin Kuhlmann
- Department of Data Science and AI, Faculty of Information Technology, Monash University, Clayton VIC 3800, Australia
| | - Kexin Lou
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | | | - Jason B. Mattingley
- The University of Queensland, Queensland Brain Institute, St Lucia 4072, Australia
- The University of Queensland, School of Psychology, St Lucia 4072, Australia
| | - Jo Morris
- Australian Research Data Commons (ARDC), Australia
| | | | - Franco Pestilli
- Department of Psychology, Center for Perceptual Systems, Center for Theoretical and Computational Neuroscience, Center on Aging and Population Sciences, Center for Learning and Memory, The University of Texas at Austin, 108 E Dean Keeton St, Austin, TX 78712, USA
| | - Aina Puce
- Department of Psychological and Brain Sciences, Indiana University, Bloomington, IN 47405, USA
| | - Fernanda L. Ribeiro
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Nigel C. Rogasch
- Discipline of Psychiatry, Adelaide Medical School, University of Adelaide, Australia
- Hopwood Centre for Neurobiology, Lifelong Health Theme, South Australian Health and Medical Research Institute (SAHMRI), Adelaide, SA, Australia
- The Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Victoria, Australia
| | - Chris Rorden
- McCausland Center for Brain Imaging, Department of Psychology, University of South Carolina, Columbia SC, 29208, USA
| | - Mark Schira
- School of Psychology, University of Wollongong, Wollongong, 2522, Australia
| | - Thomas B. Shaw
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
- The University of Queensland, Centre for Advanced Imaging, St Lucia 4072, Australia
- Department of Neurology, Royal Brisbane and Women's Hospital, Brisbane, Australia
| | - Paul F. Sowman
- Macquarie University, School of Psychological Sciences, North Ryde 2112, Australia
| | - Gershon Spitz
- Department of Neuroscience, Central Clinical School, Faculty of Medicine, Nursing and Health Sciences, Monash University, Australia
- Monash-Epworth Rehabilitation Research Centre, Turner Institute for Brain and Mental Health, School of Psychological Sciences, Monash University, Clayton, 3168, Australia
| | - Ashley Stewart
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Xincheng Ye
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
| | - Judy D. Zhu
- Macquarie University, School of Psychological Sciences, North Ryde 2112, Australia
| | - Matthew E. Hughes
- Centre for Mental Health & Brain Sciences, Swinburne University of Technology, Hawthorn 3122, Australia
| | - Aswin Narayanan
- The University of Queensland, Centre for Advanced Imaging, St Lucia 4072, Australia
| | - Steffen Bollmann
- The University of Queensland, School of Information Technology and Electrical Engineering, St Lucia 4072, Australia
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7
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Nam HH, Flynn M, Lasso A, Herz C, Sabin P, Wang Y, Cianciulli A, Vigil C, Huang J, Vicory J, Paniagua B, Allemang D, Goldberg DJ, Nuri M, Cohen MS, Fichtinger G, Jolley MA. Modeling of the Tricuspid Valve and Right Ventricle in Hypoplastic Left Heart Syndrome With a Fontan Circulation. Circ Cardiovasc Imaging 2023; 16:e014671. [PMID: 36866669 PMCID: PMC10026972 DOI: 10.1161/circimaging.122.014671] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Indexed: 03/04/2023]
Abstract
BACKGROUND In hypoplastic left heart syndrome, tricuspid regurgitation (TR) is associated with circulatory failure and death. We hypothesized that the tricuspid valve (TV) structure of patients with hypoplastic left heart syndrome with a Fontan circulation and moderate or greater TR differs from those with mild or less TR, and that right ventricle volume is associated with TV structure and dysfunction. METHODS TV of 100 patients with hypoplastic left heart syndrome and a Fontan circulation were modeled using transthoracic 3-dimensional echocardiograms and custom software in SlicerHeart. Associations of TV structure to TR grade and right ventricle function and volume were investigated. Shape parameterization and analysis was used to calculate the mean shape of the TV leaflets, their principal modes of variation, and to characterize associations of TV leaflet shape to TR. RESULTS In univariate modeling, patients with moderate or greater TR had larger TV annular diameters and area, greater annular distance between the anteroseptal commissure and anteroposterior commissure, greater leaflet billow volume, and more laterally directed anterior papillary muscle angles compared to valves with mild or less TR (all P<0.001). In multivariate modeling greater total billow volume, lower anterior papillary muscle angle, and greater distance between the anteroposterior commissure and anteroseptal commissure were associated with moderate or greater TR (P<0.001, C statistic=0.85). Larger right ventricle volumes were associated with moderate or greater TR (P<0.001). TV shape analysis revealed structural features associated with TR, but also highly heterogeneous TV leaflet structure. CONCLUSIONS Moderate or greater TR in patients with hypoplastic left heart syndrome with a Fontan circulation is associated with greater leaflet billow volume, a more laterally directed anterior papillary muscle angle, and greater annular distance between the anteroseptal commissure and anteroposterior commissure. However, there is significant heterogeneity of structure in the TV leaflets in regurgitant valves. Given this variability, an image-informed patient-specific approach to surgical planning may be needed to achieve optimal outcomes in this vulnerable and challenging population.
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Affiliation(s)
- Hannah H Nam
- Department of Anesthesiology and Critical Care Medicine (H.H.N., M.F., C.H., P.S., A.C., C.V., M.A.J.)
| | - Maura Flynn
- Department of Anesthesiology and Critical Care Medicine (H.H.N., M.F., C.H., P.S., A.C., C.V., M.A.J.)
| | - Andras Lasso
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, ON, Canada (A.L.)
| | - Christian Herz
- Department of Anesthesiology and Critical Care Medicine (H.H.N., M.F., C.H., P.S., A.C., C.V., M.A.J.)
| | - Patricia Sabin
- Department of Anesthesiology and Critical Care Medicine (H.H.N., M.F., C.H., P.S., A.C., C.V., M.A.J.)
| | - Yan Wang
- Division of Cardiology, Children's Hospital of Philadelphia, PA. (Y.W., D.J.G., M.S.C., M.A.J.)
| | - Alana Cianciulli
- Department of Anesthesiology and Critical Care Medicine (H.H.N., M.F., C.H., P.S., A.C., C.V., M.A.J.)
| | - Chad Vigil
- Department of Anesthesiology and Critical Care Medicine (H.H.N., M.F., C.H., P.S., A.C., C.V., M.A.J.)
| | - Jing Huang
- Department of Biostatistics, Epidemiology and Informatics, University of Pennsylvania and Department of Pediatrics, Children's Hospital of Philadelphia, PA. (J.H.)
| | | | | | | | - David J Goldberg
- Division of Cardiology, Children's Hospital of Philadelphia, PA. (Y.W., D.J.G., M.S.C., M.A.J.)
| | - Mohammed Nuri
- Division of Pediatric Cardiac Surgery, Children's Hospital of Philadelphia, PA. (M.N.)
| | - Meryl S Cohen
- Division of Cardiology, Children's Hospital of Philadelphia, PA. (Y.W., D.J.G., M.S.C., M.A.J.)
| | | | - Matthew A Jolley
- Department of Anesthesiology and Critical Care Medicine (H.H.N., M.F., C.H., P.S., A.C., C.V., M.A.J.)
- Division of Cardiology, Children's Hospital of Philadelphia, PA. (Y.W., D.J.G., M.S.C., M.A.J.)
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Zhao W, Guo Y, Xu C, Pei G, Basnet S, Pei Y, Su X. Distal Humerus Morphological Analysis of Chinese Individuals: A Statistical Shape Modeling Approach. Orthop Surg 2022; 14:2730-2740. [PMID: 36102259 PMCID: PMC9531077 DOI: 10.1111/os.13492] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 06/18/2022] [Revised: 08/08/2022] [Accepted: 08/11/2022] [Indexed: 11/29/2022] Open
Abstract
Objective A detailed analysis of the morphology of distal humeral articulation can help in the creation of anatomic prostheses of hemiarthroplasty. This study used statistical shape modeling to evaluate the 3D morphology of the distal humerus in healthy Chinese individuals and to investigate the proper articular morphology differences. Methods A statistical shape model (SSM) of the distal humerus was created using CT scans of 106 survey‐confirmed nonpathologic elbows. In addition, the articular components of each principal component (PC) were selected and fitted on the mean mode. The Euclidean point‐to‐mesh distance of articular modes was calculated as a measurement the proper change in the morphology of the articulation. Results The first seven PCs jointly accounted for 80.9% of the total variation (44.4%, 12.2%, 7.9%, 5.9%, 4.1%, 3.4% and 3%, respectively). In the mean model, the distance between the medial and lateral epicondyles was 57.4 mm, the width of the articulation was 42.1 mm, and the angle of the transepicondylar line (TEL) and C line was 4.8°. The articular surface differences of the first PC were significant (RMS: 1.43 mm in the −3 SD model and 2.38 mm in the +3 SD model), whereas under other conditions, the differences were not remarkable despite the maximum deformation not exceeding 1 mm. Conclusion A novel method (SSM) was used to evaluate the 3D morphology of the distal humerus in healthy Chinese individuals and investigate the proper articular shape differences. We found the proper shape of articular surface basically transformed into one variation pattern which was relevant to the bone size, even though the morphology of distal humerus possessed complicated variation modes. The findings of this study can be helpful to design the next generation of elbow hemiarthroplasty in the future.
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Affiliation(s)
- Wei Zhao
- School of Medicine Southern University of Science and Technology Shenzhen China
| | - Yao Guo
- School of Medicine Southern University of Science and Technology Shenzhen China
| | - Chuangye Xu
- School of Medicine Southern University of Science and Technology Shenzhen China
| | - Guoxian Pei
- School of Medicine Southern University of Science and Technology Shenzhen China
| | - Shiva Basnet
- School of Medicine Southern University of Science and Technology Shenzhen China
| | - Yanjun Pei
- Intelligent and Digital Surgery Innovation Center Southern University of Science and Technology Hospital Shenzhen China
| | - Xiuyun Su
- Intelligent and Digital Surgery Innovation Center Southern University of Science and Technology Hospital Shenzhen China
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Vicory J, Herz C, Allemang D, Nam HH, Cianciulli A, Vigil C, Han Y, Lasso A, Jolley MA, Paniagua B. Statistical shape analysis of the tricuspid valve in hypoplastic left heart sydrome. STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. STACOM (WORKSHOP) 2022; 13131:132-140. [PMID: 35088061 PMCID: PMC8788948 DOI: 10.1007/978-3-030-93722-5_15] [Citation(s) in RCA: 3] [Impact Index Per Article: 1.5] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/14/2023]
Abstract
Hypoplastic left heart syndrome (HLHS) is a congenital heart disease characterized by incomplete development of the left heart. Children with HLHS undergo a series of operations which result in the tricuspid valve (TV) becoming the only functional atrioventricular valve. Some of those patients develop tricuspid regurgitation which is associated with heart failure and death and necessitates further surgical intervention. Repair of the regurgitant TV, and understanding the connections between structure and function of this valve remains extremely challenging. Adult cardiac populations have used 3D echocardiography (3DE) combined with computational modeling to better understand cardiac conditions affecting the TV. However, these structure-function analyses rely on simplistic point-based techniques that do not capture the leaflet surface in detail, nor do they allow robust comparison of shapes across groups. We propose using statistical shape modeling and analysis of the TV using Spherical Harmonic Representation Point Distribution Models (SPHARM-PDM) in order to generate a reproducible representation, which in turn enables high dimensional low sample size statistical analysis techniques such as principal component analysis and distance weighted discrimination. Our initial results suggest that visualization of the differences in regurgitant vs. non-regurgitant valves can precisely locate populational structural differences as well as how an individual regurgitant valve differs from the mean shape of functional valves. We believe that these results will support the creation of modern image-based modeling tools, and ultimately increase the understanding of the relationship between valve structure and function needed to inform and improve surgical planning in HLHS.
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Affiliation(s)
| | - Christian Herz
- Children's Hospital of Philadelphia, Philadelphia, PA, 02115, USA
| | - David Allemang
- Children's Hospital of Philadelphia, Philadelphia, PA, 02115, USA
| | - Hannah H Nam
- Children's Hospital of Philadelphia, Philadelphia, PA, 02115, USA
| | - Alana Cianciulli
- Children's Hospital of Philadelphia, Philadelphia, PA, 02115, USA
| | - Chad Vigil
- Children's Hospital of Philadelphia, Philadelphia, PA, 02115, USA
| | - Ye Han
- Kitware Inc, North Carolina, USA
| | | | - Matthew A Jolley
- Children's Hospital of Philadelphia, Philadelphia, PA, 02115, USA
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Nam HH, Herz C, Lasso A, Cianciulli A, Flynn M, Huang J, Wang Z, Paniagua B, Vicory J, Kabir S, Simpson J, Harrild D, Marx G, Cohen MS, Glatz AC, Jolley MA. Visualization and Quantification of the Unrepaired Complete Atrioventricular Canal Valve Using Open-Source Software. J Am Soc Echocardiogr 2022; 35:985-996.e11. [PMID: 35537615 PMCID: PMC9452462 DOI: 10.1016/j.echo.2022.04.015] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 01/10/2022] [Revised: 04/20/2022] [Accepted: 04/24/2022] [Indexed: 10/18/2022]
Abstract
BACKGROUND Repair of complete atrioventricular canal (CAVC) is often complicated by residual left atrioventricular valve regurgitation. The structure of the mitral and tricuspid valves in biventricular hearts has previously been shown to be associated with valve dysfunction. However, the three-dimensional (3D) structure of the entire unrepaired CAVC valve has not been quantified. Understanding the 3D structure of the CAVC may inform optimized repair. METHODS Novel open-source work flows were created in SlicerHeart for the modeling and quantification of CAVC valves on the basis of 3D echocardiographic images. These methods were applied to model the annulus, leaflets, and papillary muscle (PM) structure of 35 patients (29 with trisomy 21) with CAVC using transthoracic 3D echocardiography. The mean leaflet and annular shapes were calculated and visualized using shape analysis. Metrics of the complete native CAVC valve structure were compared with those of normal mitral valves using the Mann-Whitney U test. Associations between CAVC structure and atrioventricular valve regurgitation were analyzed. RESULTS CAVC leaflet metrics varied throughout systole. Compared with normal mitral valves, the left CAVC PMs were more acutely angled in relation to the annular plane (P < .001). In addition, the anterolateral PM was laterally and inferiorly rotated in CAVC, while the posteromedial PM was more superiorly and laterally rotated, relative to normal mitral valves (P < .001). Lower native CAVC atrioventricular valve annular height and annular height-to-valve width ratio before repair were both associated with moderate or greater left atrioventricular valve regurgitation after repair (P < .01). CONCLUSIONS It is feasible to model and quantify 3D CAVC structure using 3D echocardiographic images. The results demonstrate significant variation in CAVC structure across the cohort and differences in annular, leaflet, and PM structure compared with the mitral valve. These tools may be used in future studies to catalyze future research intended to identify structural associations of valve dysfunction and to optimize repair in this vulnerable and complex population.
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Affiliation(s)
- Hannah H Nam
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Christian Herz
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Andras Lasso
- Laboratory for Percutaneous Surgery, Queen's University, Kingston, Ontario, Canada
| | - Alana Cianciulli
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Maura Flynn
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Jing Huang
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | - Zi Wang
- Department of Biostatistics, Epidemiology, and Informatics, University of Pennsylvania, Philadelphia, Pennsylvania
| | | | | | - Saleha Kabir
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, United Kingdom
| | - John Simpson
- Department of Congenital Heart Disease, Evelina London Children's Hospital, London, United Kingdom
| | - David Harrild
- Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts
| | - Gerald Marx
- Department of Cardiology, Boston Children's Hospital, Boston, Massachusetts
| | - Meryl S Cohen
- Division of Pediatric Cardiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Andrew C Glatz
- Division of Pediatric Cardiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Center for Pediatric Clinical Effectiveness, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania
| | - Matthew A Jolley
- Department of Anesthesiology and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania; Division of Pediatric Cardiology, Children's Hospital of Philadelphia, Philadelphia, Pennsylvania.
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11
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Vicory J, Herz C, Han Y, Allemang D, Flynn M, Cianciulli A, Nam HH, Sabin P, Lasso A, Jolley MA, Paniagua B. Skeletal model-based analysis of the tricuspid valve in hypoplastic left heart syndrome. STATISTICAL ATLASES AND COMPUTATIONAL MODELS OF THE HEART. STACOM (WORKSHOP) 2022; 13593:258-268. [PMID: 36848309 PMCID: PMC9949511 DOI: 10.1007/978-3-031-23443-9_24] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Grants] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 01/28/2023]
Abstract
Hypoplastic left heart syndrome (HLHS) is a congenital heart disease characterized by incomplete development of the left heart. Children with HLHS undergo a series of operations which result in the tricuspid valve (TV) becoming the only functional atrioventricular valve. Many HLHS patients develop tricuspid regurgitation and right ventricle enlargement which is associated with heart failure and death without surgical intervention on the valve. Understanding the connections between the geometry of the TV and its function remains extremely challenging and hinders TV repair planning. Traditional analysis methods rely on simple anatomical measures which do not capture information about valve geometry in detail. Recently, surface-based shape representations such as SPHARM-PDM have been shown to be useful for tasks such as discriminating between valves with normal or poor function. In this work we propose to use skeletal representations (s-reps), a more feature-rich geometric representation, for modeling the leaflets of the tricuspid valve. We propose an extension to previous s-rep fitting approaches to incorporate application-specific anatomical landmarks and population information to improve correspondence. We use several traditional statistical shape analysis techniques to evaluate the efficiency of this representation: using principal component analysis (PCA) we observe that it takes fewer modes of variation compared to boundary-based approaches to represent 90% of the population variation, while distance-weighted discrimination (DWD) shows that s-reps provide for more significant classification between valves with less regurgitation and those with more. These results show the power of using s-reps for modeling the relationship between structure and function of the tricuspid valve.
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Affiliation(s)
| | - Christian Herz
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 02115, USA
| | - Ye Han
- Kitware Inc, North Carolina, USA
| | | | - Maura Flynn
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 02115, USA
| | - Alana Cianciulli
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 02115, USA
| | - Hannah H Nam
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 02115, USA
| | - Patricia Sabin
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 02115, USA
| | | | - Matthew A Jolley
- Department of Anesthesia and Critical Care Medicine, Children's Hospital of Philadelphia, Philadelphia, PA, 02115, USA
- Division of Pediatric Cardiology, Children's Hospital of Philadelphia, Philadelphia, PA, 02115, USA
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12
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Devignes Q, Daoudi S, Viard R, Lopes R, Betrouni N, Kuchcinski G, Rolland AS, Moreau C, Defebvre L, Bardinet E, Bonnet M, Brefel-Courbon C, Delmaire C, El Mountassir F, Fluchère F, Fradet A, Giordana C, Hainque E, Houvenaghel JF, Jarraya B, Klinger H, Maltête D, Marques A, Meyer M, Rascol O, Rouaud T, Tir M, Wirth T, Corvol JC, Devos D, Dujardin K. Heterogeneity of PD-MCI in Candidates to Subthalamic Deep Brain Stimulation: Associated Cortical and Subcortical Modifications. JOURNAL OF PARKINSON'S DISEASE 2022; 12:1507-1526. [PMID: 35599498 DOI: 10.3233/jpd-223232] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Subscribe] [Scholar Register] [Indexed: 06/15/2023]
Abstract
BACKGROUND Parkinson's disease mild cognitive impairment (PD-MCI) is frequent and heterogenous. There is no consensus about its influence on subthalamic deep brain stimulation (STN-DBS) outcomes. OBJECTIVE To determine the prevalence of PD-MCI and its subtypes in candidates to STN-DBS. Secondarily, we sought to identify MRI structural markers associated with cognitive impairment in these subgroups. METHODS Baseline data from the French multicentric PREDISTIM cohort were used. Candidates to STN-DBS were classified according to their cognitive performance in normal cognition (PD-NC) or PD-MCI. The latter included frontostriatal (PD-FS) and posterior cortical (PD-PC) subtypes. Between-group comparisons were performed on demographical and clinical variables as well as on T1-weighted MRI sequences at the cortical and subcortical levels. RESULTS 320 patients were included: 167 (52%) PD-NC and 153 (48%) PD-MCI patients. The latter group included 123 (80%) PD-FS and 30 (20%) PD-PC patients. There was no between-group difference regarding demographic and clinical variables. PD-PC patients had significantly lower global efficiency than PD-FS patients and significantly worse performance on visuospatial functions, episodic memory, and language. Compared to PD-NC, PD-MCI patients had cortical thinning and radiomic-based changes in the left caudate nucleus and hippocampus. There were no significant differences between the PD-MCI subtypes. CONCLUSION Among the candidates to STN-DBS, a significant proportion has PD-MCI which is associated with cortical and subcortical alterations. Some PD-MCI patients have posterior cortical deficits, a subtype known to be at higher risk of dementia.
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Affiliation(s)
- Quentin Devignes
- Univ. Lille, Inserm, Lille Neurosciences and Cognition, CHU-Lille, Neurology and Movement Disorders department, NS-Park/F-CRIN, Lille, France
| | - Sami Daoudi
- Univ. Lille, Inserm, Lille Neurosciences and Cognition, CHU-Lille, Neurology and Movement Disorders department, NS-Park/F-CRIN, Lille, France
| | - Romain Viard
- Univ. Lille, CNRS, Inserm, US 41 - UMS 2014 - PLBS, CHU Lille, Lille Pasteur Institute, Lille, France
| | - Renaud Lopes
- Univ. Lille, CNRS, Inserm, US 41 - UMS 2014 - PLBS, CHU Lille, Lille Pasteur Institute, Lille, France
| | - Nacim Betrouni
- Univ. Lille, Inserm, Lille Neurosciences and Cognition, CHU-Lille, Neurology and Movement Disorders department, NS-Park/F-CRIN, Lille, France
| | - Gregory Kuchcinski
- Univ. Lille, CNRS, Inserm, US 41 - UMS 2014 - PLBS, CHU Lille, Lille Pasteur Institute, Lille, France
| | - Anne-Sophie Rolland
- Univ. Lille, Inserm, Lille Neurosciences and Cognition, CHU-Lille, Department of Medical Pharmacology, NS-Park/F-CRIN, Lille, France
| | - Caroline Moreau
- Univ. Lille, Inserm, Lille Neurosciences and Cognition, CHU-Lille, Neurology and Movement Disorders department, NS-Park/F-CRIN, Lille, France
| | - Luc Defebvre
- Univ. Lille, Inserm, Lille Neurosciences and Cognition, CHU-Lille, Neurology and Movement Disorders department, NS-Park/F-CRIN, Lille, France
| | - Eric Bardinet
- Institut du Cerveau (ICM), Centre de Neuro-Imagerie de Recherche (CENIR), UMR S 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
| | - Marie Bonnet
- Centre Expert Parkinson, NS-Park/F-CRIN, Centre Mémoire de Ressources et de Recherche, IMNc, Hôpital Pellegrin, CHU de Bordeaux, France
| | - Christine Brefel-Courbon
- Service de Neurologie B8, Centre Expert Parkinson, NS-Park/F-CRIN, Hôpital Pierre Paul Riquet, CHU Purpan, Toulouse, France
| | - Christine Delmaire
- Department of Radiology, NS-Park/F-CRIN, Hôpital Fondation A de Rothschild, Paris, France
| | - Fouzia El Mountassir
- Université Paris-Saclay, CEA, CNRS, Baobab, Neurospin, Gif-sur-Yvette, France and Institut du Cerveau (ICM), UMR S 1127, CNRS UMR 7225, Sorbonne Université, Paris, France
| | - Frédérique Fluchère
- Department of Neurology, NS-Park/F-CRIN, Assistance Publique - Hôpitaux de Marseille (APHM), Timone University Hospital and Institut de Neurosciences de la Timone, Marseille, France
| | - Anne Fradet
- Neurology Department, NS-Park/F-CRIN, University Hospital of Poitiers and INSERM, University of Poitiers, Centre d'Investigation Clinique CIC 1402, Poitiers, France
| | - Caroline Giordana
- Department of Neurology, NS-Park/F-CRIN, Centre Hospitalier Universitaire de Nice, Nice, France
| | - Elodie Hainque
- Sorbonne Université, Paris Brain Institute - ICM, NS-Park/F-CRIN, Assistance publique Hôpitaux de Paris, Inserm, CRNS, Hôpital Pitié-Salpêtrière, Department of Neurology, Paris, France
| | | | - Béchir Jarraya
- Neuroscience Pole, NS-Park/F-CRIN, Hôpital Foch, Suresnes, University of Versailles Paris-Saclay, INSERM-CEA NeuroSpin, Saclay, France
| | - Hélène Klinger
- Hospices Civils de Lyon, Hôpital Neurologique Pierre Wertheimer, Service de Neurologie C, NS-Park/F-CRIN, Lyon, France
| | - David Maltête
- Department of Neurology, NS-Park/F-CRIN, Rouen University Hospital and University of Rouen, France; INSERM U1239, Laboratory of Neuronal and Neuroendocrine Differentiation and Communication, Mont-Saint-Aignan, France
| | - Ana Marques
- Université Clermont Auvergne, CNRS, Clermont Auvergne INP, Institut Pascal, Clermont-Ferrand University Hospital, Neurology department, NS-Park/F-CRIN, Clermont-Ferrand, France
| | - Mylène Meyer
- Neurology department, NS-Park/F-CRIN, Central Hospital, CHRU-Nancy, Nancy, France
| | - Olivier Rascol
- Department of Clinical Pharmacology and Neuroscience, NS-Park/F-CRIN, Toulouse University Hospital, Toulouse, France
| | - Tiphaine Rouaud
- Department of Neurology, Centre Expert Parkinson, NS-Park/F-CRIN, CHU Nantes, Nantes, France
| | - Melissa Tir
- Department of Neurology, NS-PARK/FCRIN, Amiens University Hospital, Amiens, France
| | - Thomas Wirth
- Service de Neurologie, NS-Park/F-CRIN, Hôpitaux Universitaires de Strasbourg et Fédération de Médecine Translationnelle de Médecine de Strasbourg, Strasbourg, France
- Institut de Génétique et de Biologie Moléculaire et Cellulaire, INSERM-U964/CNRS-UMR7104/Université de Strasbourg, Illkirch, France
| | - Jean-Christophe Corvol
- Sorbonne Université, Paris Brain Institute - ICM, NS-Park/F-CRIN, Assistance publique Hôpitaux de Paris, Inserm, CRNS, Hôpital Pitié-Salpêtrière, Department of Neurology, Paris, France
| | - David Devos
- Univ. Lille, Inserm, Lille Neurosciences and Cognition, CHU-Lille, Neurology and Movement Disorders department, NS-Park/F-CRIN, Lille, France
- Univ. Lille, Inserm, Lille Neurosciences and Cognition, CHU-Lille, Department of Medical Pharmacology, NS-Park/F-CRIN, Lille, France
| | - Kathy Dujardin
- Univ. Lille, Inserm, Lille Neurosciences and Cognition, CHU-Lille, Neurology and Movement Disorders department, NS-Park/F-CRIN, Lille, France
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Miranda F, Garib D, Pugliese F, da Cunha Bastos JC, Janson G, Palomo JM. Upper airway changes in Class III patients using miniscrew-anchored maxillary protraction with hybrid and hyrax expanders: a randomized controlled trial. Clin Oral Investig 2022; 26:183-195. [PMID: 34041608 DOI: 10.1007/s00784-021-03989-3] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/04/2020] [Accepted: 05/12/2021] [Indexed: 10/21/2022]
Abstract
OBJECTIVES The aim of this study was to compare the upper airway space changes after miniscrew-anchored maxillary protraction with hybrid (HH) and conventional hyrax (CH) expanders. MATERIAL AND METHODS The sample comprised Class III malocclusion growing patients that were randomized into two groups of miniscrew-anchored maxillary protraction. The group HH was treated with a hybrid hyrax appliance in the maxilla and two miniscrews distally to the canines in the mandible. Class III elastics were used from the maxillary first molar to the mandibular miniscrews until anterior crossbite correction. The group CH was treated with a similar protocol except for the conventional hyrax expander in the maxilla. Cone-beam computed tomography was obtained before (T1) and after 12 months of therapy (T2). The shape and size of upper airway were assessed. Intergroup comparisons were performed using Mann-Whitney U test (p < 0.05). RESULTS The group HH was composed of 20 patients (8 female, 12 male) with a mean age of 10.76 years. The group CH was composed of 15 patients (6 female, 9 male) with a mean age of 11.52 years. Anteroposterior and transverse increases of the upper airway were found for both groups. The oropharynx and the most constricted area increased similarly in both groups. CONCLUSIONS No differences in upper airway changes were observed using protraction anchored on hybrid or conventional hyrax expanders. CLINICAL RELEVANCE Maxillary protraction anchored on hybrid or conventional hyrax expanders may benefit patients with breathing disorders due to the increase of the upper airway volume and most constricted area. Registration: ClinicalTrials.gov (NCT03712007).
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Affiliation(s)
- Felicia Miranda
- Department of Orthodontics, Bauru Dental School, University of São Paulo, Alameda Octávio Pinheiro Brisolla 9-75, SP, 17012-901, Bauru, Brazil.
| | - Daniela Garib
- Department of Orthodontics, Bauru Dental School, University of São Paulo, Alameda Octávio Pinheiro Brisolla 9-75, SP, 17012-901, Bauru, Brazil
- Hospital for Rehabilitation of Craniofacial Anomalies, University of São Paulo, Silvio Marchione 3-20, SP 17012-900, Bauru, Brazil
| | - Fernando Pugliese
- Department of Orthodontics, School of Dental Medicine, Case Western Reserve University, 9601 Chester Avenue, OH, 44106, Cleveland, USA
| | - José Carlos da Cunha Bastos
- Hospital for Rehabilitation of Craniofacial Anomalies, University of São Paulo, Silvio Marchione 3-20, SP 17012-900, Bauru, Brazil
| | - Guilherme Janson
- Department of Orthodontics, Bauru Dental School, University of São Paulo, Alameda Octávio Pinheiro Brisolla 9-75, SP, 17012-901, Bauru, Brazil
| | - Juan Martin Palomo
- Department of Orthodontics, School of Dental Medicine, Case Western Reserve University, 9601 Chester Avenue, OH, 44106, Cleveland, USA
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14
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Dot G, Licha R, Goussard F, Sansalone V. A new protocol to accurately track long-term orthodontic tooth movement and support patient-specific numerical modeling. J Biomech 2021; 129:110760. [PMID: 34628204 DOI: 10.1016/j.jbiomech.2021.110760] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/12/2021] [Revised: 09/15/2021] [Accepted: 09/18/2021] [Indexed: 10/20/2022]
Abstract
Numerical simulation of long-term orthodontic tooth movement based on Finite Element Analysis (FEA) could help clinicians to plan more efficient and mechanically sound treatments. However, most of FEA studies assume idealized loading conditions and lack experimental calibration or validation. The goal of this paper is to propose a novel clinical protocol to accurately track orthodontic tooth displacement in three-dimensions (3D) and provide 3D models that may support FEA. Our protocol uses an initial cone beam computed tomography (CBCT) scan and several intra-oral scans (IOS) to generate 3D models of the maxillary bone and teeth ready for use in FEA. The protocol was applied to monitor the canine retraction of a patient during seven months. A second CBCT scan was performed at the end of the study for validation purposes. In order to ease FEA, a frictionless and statically determinate lingual device for maxillary canine retraction was designed. Numerical simulations were set up using the 3D models provided by our protocol to show the relevance of our proposal. Comparison of numerical and clinical results highlights the suitability of this protocol to support patient-specific FEA.
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Affiliation(s)
- Gauthier Dot
- Univ Paris Est Creteil, CNRS, MSME, F-94010, Creteil, France; Univ Gustave Eiffel, MSME, F-77474, Marne-la-Vallée, France; Service d'Odontologie, Hopital Pitie-Salpetriere, AP-HP, Universite de Paris, Paris, France
| | - Raphael Licha
- Univ Paris Est Creteil, CNRS, MSME, F-94010, Creteil, France; Univ Gustave Eiffel, MSME, F-77474, Marne-la-Vallée, France
| | - Florent Goussard
- CR2P, UMR 7207, Muséum national d'Histoire naturelle, CNRS, Sorbonne Université, 8 rue Buffon, CP38 75005, Paris, France
| | - Vittorio Sansalone
- Univ Paris Est Creteil, CNRS, MSME, F-94010, Creteil, France; Univ Gustave Eiffel, MSME, F-77474, Marne-la-Vallée, France.
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15
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Nuninga JO, Mandl RCW, Siero J, Nieuwdorp W, Heringa SM, Boks MP, Somers M, Sommer IEC. Shape and volume changes of the superior lateral ventricle after electroconvulsive therapy measured with ultra-high field MRI. Psychiatry Res Neuroimaging 2021; 317:111384. [PMID: 34537602 DOI: 10.1016/j.pscychresns.2021.111384] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 04/21/2021] [Revised: 08/11/2021] [Accepted: 08/31/2021] [Indexed: 11/18/2022]
Abstract
The subventricular zone (SVZ) of the lateral ventricles harbors neuronal stem cells in adult mammals. Rodent studies report neurogenic effects in the SVZ of electroconvulsive stimulation. We hypothesize that if this finding translates to depressed patients undergoing electroconvulsive therapy (ECT), this would be reflected in shape changes at the SVZ. Using T1-weighted MR images acquired at ultra-high field strength (7T), the shape and volume of the ventricles were compared from pre to post ECT after 10 ECT sessions (in patients twice weekly) or 5 weeks apart (controls) using linear mixed models with age and gender as covariates. Ventricle shape significantly changed and volume significantly decreased over time in patients for the left ventricle, but not in controls. The decrease in volume of the ventricles was associated to a decrease in depression scores, and an increase in the left dentate gyrus, However, the shape changes of the ventricles were not restricted to the neurogenic niche in the lateral walls of the ventricles, providing no clear evidence for neurogenesis as sole explanation of volume changes in the ventricles after ECT.
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Affiliation(s)
- Jasper O Nuninga
- University Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells and Systems, Groningen, the Netherlands; Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands.
| | - René C W Mandl
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands
| | - Jeroen Siero
- Department of Radiology, University Medical Center Utrecht, Utrecht, the Netherlands; Spinoza Centre for Neuroimaging, Amsterdam, the Netherlands
| | - Wendy Nieuwdorp
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands
| | - Sophie M Heringa
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands
| | - Marco P Boks
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands
| | - Metten Somers
- Department of Psychiatry, UMC Utrecht Brain Center, University Utrecht, Utrecht, the Netherlands
| | - Iris E C Sommer
- University Groningen, University Medical Center Groningen, Department of Biomedical Sciences of Cells and Systems, Groningen, the Netherlands
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16
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Rodríguez-Cárdenas YA, Arriola-Guillén LE, Ruíz-Mora GA, Aliaga-Del Castillo A, Boessio-Vizzotto M, Dias-Da Silveira HL. Root changes in buccal versus palatal maxillary impacted canines of adults: A longitudinal and retrospective 3-dimensional study before and after orthodontic traction. Int Orthod 2020; 18:490-502. [PMID: 32513608 DOI: 10.1016/j.ortho.2020.05.007] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.3] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/10/2020] [Revised: 05/14/2020] [Accepted: 05/17/2020] [Indexed: 11/30/2022]
Abstract
OBJECTIVE Maxillary impacted canines (MIC) could suffer root changes after canine traction. The aim of this study was to evaluate the 3-dimensional root changes in buccal versus palatal MIC after orthodontic traction. MATERIALS AND METHODS This longitudinal and retrospective study included pre-treatment and after traction cone beam computed tomography scans (CBCTs) of 30 subjects with unilateral/bilateral MIC. A total of 43 MIC were divided into 2 groups: buccal (n=17) or palatal (n=26). Root changes in length and area after orthodontic traction were measured at sagittal, coronal and axial sections. Intergroup comparison was carried out by t or U Mann-Whitney tests, depending on normality. Multiple linear regression analysis was used to evaluate the influence of all predictor variables on root changes (P<0.05). RESULTS Significant difference between groups was found for root area changes in the upper limit of the cervical third at axial section that showed greater appositional values for the palatal impacted canine group (-1.18mm2) and resorptive values for the buccal impacted canine group (0.62mm2) (P=0.024). Position of impaction palatal influenced the increase of root area in the coronal section and in the upper limit of the cervical third at axial section. Age directly influenced the decrease of total length and root area in sagittal and coronal sections, respectively. CONCLUSION Orthodontic traction of MIC produced an important appositional root change in the palatal impaction group in the axial root area of the upper limit of the cervical third. Impaction position and age influenced the increase and decrease of root area and length of some specific radicular regions.
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Affiliation(s)
| | - Luis Ernesto Arriola-Guillén
- Division of Orthodontics and Division of Oral and Maxillofacial Radiology, School of Dentistry, Universidad Científica del Sur, Lima, Peru.
| | - Gustavo Armando Ruíz-Mora
- Division of Oral and Maxillofacial Radiology, School of Dentistry, Universidad Científica del Sur, Lima, Peru
| | - Aron Aliaga-Del Castillo
- Department of Orthodontics. Bauru Dental School, University of São Paulo, Bauru, São Paulo, Brazil
| | - Mariana Boessio-Vizzotto
- Division of Oral Radiology, Faculty of Dentistry, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
| | - Heraldo Luis Dias-Da Silveira
- Division of Oral Radiology, Faculty of Dentistry, Federal University of Rio Grande do Sul, Porto Alegre, Rio Grande do Sul, Brazil
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