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Doran SJ, Al Sa’d M, Petts JA, Darcy J, Alpert K, Cho W, Sanchez LE, Alle S, El Harouni A, Genereaux B, Ziegler E, Harris GJ, Aboagye EO, Sala E, Koh DM, Marcus D. Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies. Tomography 2022; 8:497-512. [PMID: 35202205 PMCID: PMC8875191 DOI: 10.3390/tomography8010040] [Citation(s) in RCA: 0] [Impact Index Per Article: 0] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Download PDF] [Figures] [Journal Information] [Subscribe] [Scholar Register] [Received: 12/08/2021] [Revised: 02/03/2022] [Accepted: 02/05/2022] [Indexed: 11/16/2022] Open
Abstract
Purpose: XNAT is an informatics software platform to support imaging research, particularly in the context of large, multicentre studies of the type that are essential to validate quantitative imaging biomarkers. XNAT provides import, archiving, processing and secure distribution facilities for image and related study data. Until recently, however, modern data visualisation and annotation tools were lacking on the XNAT platform. We describe the background to, and implementation of, an integration of the Open Health Imaging Foundation (OHIF) Viewer into the XNAT environment. We explain the challenges overcome and discuss future prospects for quantitative imaging studies. Materials and methods: The OHIF Viewer adopts an approach based on the DICOM web protocol. To allow operation in an XNAT environment, a data-routing methodology was developed to overcome the mismatch between the DICOM and XNAT information models and a custom viewer panel created to allow navigation within the viewer between different XNAT projects, subjects and imaging sessions. Modifications to the development environment were made to allow developers to test new code more easily against a live XNAT instance. Major new developments focused on the creation and storage of regions-of-interest (ROIs) and included: ROI creation and editing tools for both contour- and mask-based regions; a "smart CT" paintbrush tool; the integration of NVIDIA's Artificial Intelligence Assisted Annotation (AIAA); the ability to view surface meshes, fractional segmentation maps and image overlays; and a rapid image reader tool aimed at radiologists. We have incorporated the OHIF microscopy extension and, in parallel, introduced support for microscopy session types within XNAT for the first time. Results: Integration of the OHIF Viewer within XNAT has been highly successful and numerous additional and enhanced tools have been created in a programme started in 2017 that is still ongoing. The software has been downloaded more than 3700 times during the course of the development work reported here, demonstrating the impact of the work. Conclusions: The OHIF open-source, zero-footprint web viewer has been incorporated into the XNAT platform and is now used at many institutions worldwide. Further innovations are envisaged in the near future.
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Affiliation(s)
- Simon J. Doran
- Division of Radiotherapy and Imaging, Institute of Cancer Research, 15 Cotswold Rd, London SM2 5NG, UK;
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
| | - Mohammad Al Sa’d
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College, London SW7 2AZ, UK
| | - James A. Petts
- Ovela Solutions Ltd., 20-22 Wenlock Road, London N1 7GU, UK;
| | - James Darcy
- Division of Radiotherapy and Imaging, Institute of Cancer Research, 15 Cotswold Rd, London SM2 5NG, UK;
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
| | - Kate Alpert
- Flywheel LLC, 1015 Glenwood Ave, Suite 300, Minneapolis, MN 55405, USA; (K.A.); (D.M.)
| | - Woonchan Cho
- Neuroimaging Informatics Analysis Center, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA;
| | - Lorena Escudero Sanchez
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
- Department of Radiology, University of Cambridge, Hills Rd, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Sachidanand Alle
- NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA 95051, USA; (S.A.); (A.E.H.); (B.G.)
| | - Ahmed El Harouni
- NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA 95051, USA; (S.A.); (A.E.H.); (B.G.)
| | - Brad Genereaux
- NVIDIA, 2788 San Tomas Expressway, Santa Clara, CA 95051, USA; (S.A.); (A.E.H.); (B.G.)
| | - Erik Ziegler
- Open Health Imaging Foundation, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA; (E.Z.); (G.J.H.)
- Radical Imaging LLC, 188 Annie Moore Rd, Bolton, MA 01740-1140, USA
| | - Gordon J. Harris
- Open Health Imaging Foundation, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA; (E.Z.); (G.J.H.)
- Department of Radiology, Massachusetts General Hospital, 55 Fruit St., Boston, MA 02114, USA
- Harvard Medical School, 25 Shattuck St., Boston, MA 02115, USA
| | - Eric O. Aboagye
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
- Cancer Imaging Centre, Department of Surgery & Cancer, Imperial College, London SW7 2AZ, UK
| | - Evis Sala
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
- Department of Radiology, University of Cambridge, Hills Rd, Cambridge CB2 0QQ, UK
- Cancer Research UK Cambridge Centre, University of Cambridge Li Ka Shing Centre, Robinson Way, Cambridge CB2 0RE, UK
| | - Dow-Mu Koh
- CRUK National Cancer Imaging Translational Accelerator, UK; (M.A.S.); (L.E.S.); (E.O.A.); (E.S.); (D.-M.K.)
- Department of Radiology, Royal Marsden Hospital, Downs Rd, Sutton SM2 5PT, UK
| | - Dan Marcus
- Flywheel LLC, 1015 Glenwood Ave, Suite 300, Minneapolis, MN 55405, USA; (K.A.); (D.M.)
- Neuroimaging Informatics Analysis Center, Washington University School of Medicine, 660 S Euclid Ave, St. Louis, MO 63110, USA;
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Wang G, Zhou W, Kong D, Qu Z, Ba M, Hao J, Yao T, Dong Q, Su Y, Reiman EM, Caselli RJ, Chen K, Wang Y. Studying APOE ɛ4 Allele Dose Effects with a Univariate Morphometry Biomarker. J Alzheimers Dis 2022; 85:1233-1250. [PMID: 34924383 PMCID: PMC10498787 DOI: 10.3233/jad-215149] [Citation(s) in RCA: 1] [Impact Index Per Article: 0.5] [Reference Citation Analysis] [What about the content of this article? (0)] [Affiliation(s)] [Abstract] [Key Words] [MESH Headings] [Grants] [Track Full Text] [Journal Information] [Subscribe] [Scholar Register] [Indexed: 01/06/2023]
Abstract
BACKGROUND A univariate neurodegeneration biomarker (UNB) based on MRI with strong statistical discrimination power would be highly desirable for studying hippocampal surface morphological changes associated with APOE ɛ4 genetic risk for AD in the cognitively unimpaired (CU) population. However, existing UNB work either fails to model large group variances or does not capture AD induced changes. OBJECTIVE We proposed a subspace decomposition method capable of exploiting a UNB to represent the hippocampal morphological changes related to the APOE ɛ4 dose effects among the longitudinal APOE ɛ4 homozygotes (HM, N = 30), heterozygotes (HT, N = 49) and non-carriers (NC, N = 61). METHODS Rank minimization mechanism combined with sparse constraint considering the local continuity of the hippocampal atrophy regions is used to extract group common structures. Based on the group common structures of amyloid-β (Aβ) positive AD patients and Aβ negative CU subjects, we identified the regions-of-interest (ROI), which reflect significant morphometry changes caused by the AD development. Then univariate morphometry index (UMI) is constructed from these ROIs. RESULTS The proposed UMI demonstrates a more substantial statistical discrimination power to distinguish the longitudinal groups with different APOE ɛ4 genotypes than the hippocampal volume measurements. And different APOE ɛ4 allele load affects the shrinkage rate of the hippocampus, i.e., HM genotype will cause the largest atrophy rate, followed by HT, and the smallest is NC. CONCLUSION The UMIs may capture the APOE ɛ4 risk allele-induced brain morphometry abnormalities and reveal the dose effects of APOE ɛ4 on the hippocampal morphology in cognitively normal individuals.
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Affiliation(s)
- Gang Wang
- School of Ulsan Ship and Ocean College, Ludong University, Yantai, China
| | - Wenju Zhou
- School of Mechatronic Engineering and Automation, Shanghai University, Shanghai, China
| | - Deping Kong
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Zongshuai Qu
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Maowen Ba
- Department of Neurology, The Affiliated Yantai Yuhuangding Hospital of Qingdao University, Yantai, China
| | - Jinguang Hao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Tao Yao
- School of Information and Electrical Engineering, Ludong University, Yantai, China
| | - Qunxi Dong
- Institute of Engineering Medicine, Beijing Institute of Technology, Beijing, China
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
| | - Yi Su
- Banner Alzheimer’s Institute, 100 Washtenaw Avenue, Phoenix, AZ, USA
| | - Eric M Reiman
- Banner Alzheimer’s Institute, 100 Washtenaw Avenue, Phoenix, AZ, USA
| | | | - Kewei Chen
- Banner Alzheimer’s Institute, 100 Washtenaw Avenue, Phoenix, AZ, USA
| | - Yalin Wang
- School of Computing and Augmented Intelligence, Arizona State University, Tempe, AZ, USA
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