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Moore SM, Quirk JD, Lassiter AW, Laforest R, Ayers GD, Badea CT, Fedorov AY, Kinahan PE, Holbrook M, Larson PEZ, Sriram R, Chenevert TL, Malyarenko D, Kurhanewicz J, Houghton AM, Ross BD, Pickup S, Gee JC, Zhou R, Gammon ST, Manning HC, Roudi R, Daldrup-Link HE, Lewis MT, Rubin DL, Yankeelov TE, Shoghi KI. Co-Clinical Imaging Metadata Information (CIMI) for Cancer Research to Promote Open Science, Standardization, and Reproducibility in Preclinical Imaging. Tomography 2023; 9:995-1009. [PMID: 37218941 PMCID: PMC10204428 DOI: 10.3390/tomography9030081] [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: 01/25/2023] [Revised: 04/30/2023] [Accepted: 05/04/2023] [Indexed: 05/24/2023] Open
Abstract
Preclinical imaging is a critical component in translational research with significant complexities in workflow and site differences in deployment. Importantly, the National Cancer Institute's (NCI) precision medicine initiative emphasizes the use of translational co-clinical oncology models to address the biological and molecular bases of cancer prevention and treatment. The use of oncology models, such as patient-derived tumor xenografts (PDX) and genetically engineered mouse models (GEMMs), has ushered in an era of co-clinical trials by which preclinical studies can inform clinical trials and protocols, thus bridging the translational divide in cancer research. Similarly, preclinical imaging fills a translational gap as an enabling technology for translational imaging research. Unlike clinical imaging, where equipment manufacturers strive to meet standards in practice at clinical sites, standards are neither fully developed nor implemented in preclinical imaging. This fundamentally limits the collection and reporting of metadata to qualify preclinical imaging studies, thereby hindering open science and impacting the reproducibility of co-clinical imaging research. To begin to address these issues, the NCI co-clinical imaging research program (CIRP) conducted a survey to identify metadata requirements for reproducible quantitative co-clinical imaging. The enclosed consensus-based report summarizes co-clinical imaging metadata information (CIMI) to support quantitative co-clinical imaging research with broad implications for capturing co-clinical data, enabling interoperability and data sharing, as well as potentially leading to updates to the preclinical Digital Imaging and Communications in Medicine (DICOM) standard.
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Affiliation(s)
- Stephen M. Moore
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - James D. Quirk
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Andrew W. Lassiter
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Richard Laforest
- Mallinckrodt Institute of Radiology, Washington University School of Medicine, St. Louis, MO 63110, USA
| | - Gregory D. Ayers
- Department of Biostatistics, Vanderbilt University, Nashville, TN 37235, USA
| | - Cristian T. Badea
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University, Durham, NC 27708, USA
| | - Andriy Y. Fedorov
- Brigham and Women’s Hospital and Harvard Medical School, Boston, MA 02115, USA
| | - Paul E. Kinahan
- Department of Radiology, University of Washington, Seattle, WA 98195, USA
| | - Matthew Holbrook
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University, Durham, NC 27708, USA
| | - Peder E. Z. Larson
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143, USA
| | - Renuka Sriram
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143, USA
| | - Thomas L. Chenevert
- Department of Radiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Dariya Malyarenko
- Department of Radiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - John Kurhanewicz
- Department of Radiology and Biomedical Imaging, University of California, San Francisco, CA 94143, USA
| | | | - Brian D. Ross
- Department of Radiology, University of Michigan Medical School, Ann Arbor, MI 48109, USA
| | - Stephen Pickup
- Department of Radiology, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - James C. Gee
- Department of Radiology, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Rong Zhou
- Department of Radiology, Abramson Cancer Center, University of Pennsylvania, Philadelphia, PA 19104, USA
| | - Seth T. Gammon
- Department of Cancer Systems Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Henry Charles Manning
- Department of Cancer Systems Imaging, Division of Diagnostic Imaging, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Raheleh Roudi
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Heike E. Daldrup-Link
- Department of Radiology, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Michael T. Lewis
- Dan L Duncan Comprehensive Cancer Center, Departments of Molecular and Cellular Biology and Radiology, Baylor College of Medicine, Houston, TX 77030, USA
| | - Daniel L. Rubin
- Departments of Biomedical Data Science, Radiology and Medicine, Stanford University School of Medicine, Stanford, CA 94305, USA
| | - Thomas E. Yankeelov
- Departments of Biomedical Engineering, Diagnostic Medicine and Oncology, Oden Institute for Computational and Engineering Sciences, Livestrong Cancer Institutes, The University of Texas at Austin, Austin, TX 78712, USA
- Department of Imaging Physics, The University of Texas MD Anderson Cancer Center, Houston, TX 77030, USA
| | - Kooresh I. Shoghi
- Mallinckrodt Institute of Radiology, Department of Biomedical Engineering, Siteman Cancer Center, Washington University School of Medicine, St. Louis, MO 63110, USA
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2
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Verhaegen F, Butterworth KT, Chalmers AJ, Coppes RP, de Ruysscher D, Dobiasch S, Fenwick JD, Granton PV, Heijmans SHJ, Hill MA, Koumenis C, Lauber K, Marples B, Parodi K, Persoon LCGG, Staut N, Subiel A, Vaes RDW, van Hoof S, Verginadis IL, Wilkens JJ, Williams KJ, Wilson GD, Dubois LJ. Roadmap for precision preclinical x-ray radiation studies. Phys Med Biol 2023; 68:06RM01. [PMID: 36584393 DOI: 10.1088/1361-6560/acaf45] [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: 05/12/2022] [Accepted: 12/30/2022] [Indexed: 12/31/2022]
Abstract
This Roadmap paper covers the field of precision preclinical x-ray radiation studies in animal models. It is mostly focused on models for cancer and normal tissue response to radiation, but also discusses other disease models. The recent technological evolution in imaging, irradiation, dosimetry and monitoring that have empowered these kinds of studies is discussed, and many developments in the near future are outlined. Finally, clinical translation and reverse translation are discussed.
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Affiliation(s)
- Frank Verhaegen
- MAASTRO Clinic, Radiotherapy Division, GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
- SmART Scientific Solutions BV, Maastricht, The Netherlands
| | - Karl T Butterworth
- Patrick G. Johnston, Centre for Cancer Research, Queen's University Belfast, Belfast, Northern Ireland, United Kingdom
| | - Anthony J Chalmers
- School of Cancer Sciences, University of Glasgow, Glasgow G61 1QH, United Kingdom
| | - Rob P Coppes
- Departments of Biomedical Sciences of Cells & Systems, Section Molecular Cell Biology and Radiation Oncology, University Medical Center Groningen, University of Groningen, 9700 AD Groningen, The Netherlands
| | - Dirk de Ruysscher
- MAASTRO Clinic, Radiotherapy Division, GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | - Sophie Dobiasch
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine and Klinikum rechts der Isar, Germany
- Department of Medical Physics, Institute of Radiation Medicine (IRM), Department of Radiation Sciences (DRS), Helmholtz Zentrum München, Germany
| | - John D Fenwick
- Department of Medical Physics & Biomedical Engineering University College LondonMalet Place Engineering Building, London WC1E 6BT, United Kingdom
| | | | | | - Mark A Hill
- MRC Oxford Institute for Radiation Oncology, University of Oxford, ORCRB Roosevelt Drive, Oxford OX3 7DQ, United Kingdom
| | - Constantinos Koumenis
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Kirsten Lauber
- Department of Radiation Oncology, University Hospital, LMU München, Munich, Germany
- German Cancer Consortium (DKTK), Partner site Munich, Germany
| | - Brian Marples
- Department of Radiation Oncology, University of Rochester, NY, United States of America
| | - Katia Parodi
- German Cancer Consortium (DKTK), Partner site Munich, Germany
- Department of Medical Physics, Faculty of Physics, Ludwig-Maximilians-Universität München, Garching b. Munich, Germany
| | | | - Nick Staut
- SmART Scientific Solutions BV, Maastricht, The Netherlands
| | - Anna Subiel
- National Physical Laboratory, Medical Radiation Science Hampton Road, Teddington, Middlesex, TW11 0LW, United Kingdom
| | - Rianne D W Vaes
- MAASTRO Clinic, Radiotherapy Division, GROW-School for Oncology and Reproduction, Maastricht University Medical Centre+, Maastricht, The Netherlands
| | | | - Ioannis L Verginadis
- Department of Radiation Oncology, Perelman School of Medicine, University of Pennsylvania, Philadelphia, Pennsylvania, United States of America
| | - Jan J Wilkens
- Department of Radiation Oncology, Technical University of Munich (TUM), School of Medicine and Klinikum rechts der Isar, Germany
- Physics Department, Technical University of Munich (TUM), Germany
| | - Kaye J Williams
- Division of Pharmacy and Optometry, University of Manchester, Manchester, United Kingdom
| | - George D Wilson
- Department of Radiation Oncology, Beaumont Health, MI, United States of America
- Henry Ford Health, Detroit, MI, United States of America
| | - Ludwig J Dubois
- The M-Lab, Department of Precision Medicine, GROW-School for Oncology and Reproduction, Maastricht University, Maastricht, The Netherlands
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3
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Commowick O, Combès B, Cervenansky F, Dojat M. Editorial: Automatic methods for multiple sclerosis new lesions detection and segmentation. Front Neurosci 2023; 17:1176625. [PMID: 36998735 PMCID: PMC10043498 DOI: 10.3389/fnins.2023.1176625] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Key Words] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/28/2023] [Accepted: 03/01/2023] [Indexed: 03/15/2023] Open
Affiliation(s)
- Olivier Commowick
- Empenn INSERM U1228, CNRS UMR6074, Inria, University of Rennes I, Rennes, France
| | - Benoît Combès
- Empenn INSERM U1228, CNRS UMR6074, Inria, University of Rennes I, Rennes, France
| | - Frédéric Cervenansky
- Univ Lyon, INSA-Lyon, Université Claude Bernard Lyon 1, UJM-Saint Etienne, CNRS, Inserm, CREATIS UMR 5220, U1206, Lyon, France
| | - Michel Dojat
- Univ Grenoble Alpes, Inserm, U1216, CHU Grenoble Alpes, Grenoble Institut Neurosciences, GIN, Grenoble, France
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Pemmaraju R, Minahan R, Wang E, Schadl K, Daldrup-Link H, Habte F. Web-Based Application for Biomedical Image Registry, Analysis, and Translation (BiRAT). Tomography 2022; 8:1453-1462. [PMID: 35736865 PMCID: PMC9228304 DOI: 10.3390/tomography8030117] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [Abstract] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 04/21/2022] [Revised: 05/23/2022] [Accepted: 05/27/2022] [Indexed: 11/18/2022] Open
Abstract
Imaging has become an invaluable tool in preclinical research for its capability to non-invasively detect and monitor disease and assess treatment response. With the increased use of preclinical imaging, large volumes of image data are being generated requiring critical data management tools. Due to proprietary issues and continuous technology development, preclinical images, unlike DICOM-based images, are often stored in an unstructured data file in company-specific proprietary formats. This limits the available DICOM-based image management database to be effectively used for preclinical applications. A centralized image registry and management tool is essential for advances in preclinical imaging research. Specifically, such tools may have a high impact in generating large image datasets for the evolving artificial intelligence applications and performing retrospective analyses of previously acquired images. In this study, a web-based server application is developed to address some of these issues. The application is designed to reflect the actual experimentation workflow maintaining detailed records of both individual images and experimental data relevant to specific studies and/or projects. The application also includes a web-based 3D/4D image viewer to easily and quickly view and evaluate images. This paper briefly describes the initial implementation of the web-based application.
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Affiliation(s)
- Rahul Pemmaraju
- School of Bioengineering and Biomedical Engineering, Johns Hopkins University, Baltimore, MD 21218, USA;
| | - Robert Minahan
- Computational and Systems Biology, University of California-Los Angeles, Los Angeles, CA 90095, USA;
| | - Elise Wang
- School of Medicine, University of Rochester, Rochester, NY 14642, USA;
| | - Kornel Schadl
- Department of Orthopedic Surgery, Stanford School of Medicine, Stanford, CA 94305, USA;
| | - Heike Daldrup-Link
- Department of Radiology, Stanford School of Medicine, Stanford, CA 94305, USA;
| | - Frezghi Habte
- Department of Radiology, Stanford School of Medicine, Stanford, CA 94305, USA;
- Correspondence:
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Zullino S, Paglialonga A, Dastrù W, Longo DL, Aime S. XNAT-PIC: Extending XNAT to Preclinical Imaging Centers. J Digit Imaging 2022; 35:860-875. [PMID: 35304674 PMCID: PMC9485318 DOI: 10.1007/s10278-022-00612-z] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 02/26/2021] [Revised: 02/07/2022] [Accepted: 02/15/2022] [Indexed: 11/22/2022] Open
Abstract
Molecular imaging generates large volumes of heterogeneous biomedical imagery with an impelling need of guidelines for handling image data. Although several successful solutions have been implemented for human epidemiologic studies, few and limited approaches have been proposed for animal population studies. Preclinical imaging research deals with a variety of machinery yielding tons of raw data but the current practices to store and distribute image data are inadequate. Therefore, standard tools for the analysis of large image datasets need to be established. In this paper, we present an extension of XNAT for Preclinical Imaging Centers (XNAT-PIC). XNAT is a worldwide used, open-source platform for securely hosting, sharing, and processing of clinical imaging studies. Despite its success, neither tools for importing large, multimodal preclinical image datasets nor pipelines for processing whole imaging studies are yet available in XNAT. In order to overcome these limitations, we have developed several tools to expand the XNAT core functionalities for supporting preclinical imaging facilities. Our aim is to streamline the management and exchange of image data within the preclinical imaging community, thereby enhancing the reproducibility of the results of image processing and promoting open science practices.
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Affiliation(s)
- Sara Zullino
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| | - Alessandro Paglialonga
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy
| | - Walter Dastrù
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
| | - Dario Livio Longo
- Institute of Biostructures and Bioimaging (IBB), Italian National Research Council (CNR), Via Nizza 52, 10126, Torino, Italy.
| | - Silvio Aime
- Molecular Imaging Center, Department of Molecular Biotechnology and Health Sciences, University of Torino, Torino, Italy.,Euro-BioImaging ERIC, Torino, Italy
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Clark D, Badea C. Advances in micro-CT imaging of small animals. Phys Med 2021; 88:175-192. [PMID: 34284331 PMCID: PMC8447222 DOI: 10.1016/j.ejmp.2021.07.005] [Citation(s) in RCA: 41] [Impact Index Per Article: 13.7] [Reference Citation Analysis] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Submit a Manuscript] [Subscribe] [Scholar Register] [Received: 03/30/2021] [Revised: 06/23/2021] [Accepted: 07/05/2021] [Indexed: 12/22/2022] Open
Abstract
PURPOSE Micron-scale computed tomography (micro-CT) imaging is a ubiquitous, cost-effective, and non-invasive three-dimensional imaging modality. We review recent developments and applications of micro-CT for preclinical research. METHODS Based on a comprehensive review of recent micro-CT literature, we summarize features of state-of-the-art hardware and ongoing challenges and promising research directions in the field. RESULTS Representative features of commercially available micro-CT scanners and some new applications for both in vivo and ex vivo imaging are described. New advancements include spectral scanning using dual-energy micro-CT based on energy-integrating detectors or a new generation of photon-counting x-ray detectors (PCDs). Beyond two-material discrimination, PCDs enable quantitative differentiation of intrinsic tissues from one or more extrinsic contrast agents. When these extrinsic contrast agents are incorporated into a nanoparticle platform (e.g. liposomes), novel micro-CT imaging applications are possible such as combined therapy and diagnostic imaging in the field of cancer theranostics. Another major area of research in micro-CT is in x-ray phase contrast (XPC) imaging. XPC imaging opens CT to many new imaging applications because phase changes are more sensitive to density variations in soft tissues than standard absorption imaging. We further review the impact of deep learning on micro-CT. We feature several recent works which have successfully applied deep learning to micro-CT data, and we outline several challenges specific to micro-CT. CONCLUSIONS All of these advancements establish micro-CT imaging at the forefront of preclinical research, able to provide anatomical, functional, and even molecular information while serving as a testbench for translational research.
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Affiliation(s)
- D.P. Clark
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC 27710
| | - C.T. Badea
- Quantitative Imaging and Analysis Lab, Department of Radiology, Duke University Medical Center, Durham, NC 27710
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Brossard C, Montigon O, Boux F, Delphin A, Christen T, Barbier EL, Lemasson B. MP3: Medical Software for Processing Multi-Parametric Images Pipelines. Front Neuroinform 2020; 14:594799. [PMID: 33304261 PMCID: PMC7701116 DOI: 10.3389/fninf.2020.594799] [Citation(s) in RCA: 4] [Impact Index Per Article: 1.0] [Reference Citation Analysis] [Abstract] [Key Words] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 08/14/2020] [Accepted: 10/14/2020] [Indexed: 01/28/2023] Open
Abstract
This article presents an open source software able to convert, display, and process medical images. It differentiates itself from the existing software by its ability to design complex processing pipelines and to wisely execute them on a large databases. An MP3 pipeline can contain unlimited homemade or ready-made processes and can be carried out with a parallel execution system. As a viewer, MP3 allows display of up to four images together and to draw Regions Of Interest (ROI). Two applications showing the strengths of the software are presented as examples: a preclinical study involving Magnetic Resonance Imaging (MRI) data and a clinical one involving Computed Tomography (CT) images. MP3 is downloadable at https://github.com/nifm-gin/MP3.
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Affiliation(s)
- Clément Brossard
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France.,MoGlimaging Network, HTE Program of the French Cancer Plan, Toulouse, France
| | - Olivier Montigon
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France
| | - Fabien Boux
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France.,University of Grenoble Alpes, Inria, CNRS, G-INP, Grenoble, France
| | - Aurélien Delphin
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France
| | - Thomas Christen
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France
| | - Emmanuel L Barbier
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France
| | - Benjamin Lemasson
- University of Grenoble Alpes, INSERM, U1216, Grenoble Institut des Neurosciences (GIN), Grenoble, France.,MoGlimaging Network, HTE Program of the French Cancer Plan, Toulouse, France
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