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. 2022 Feb 11;8(1):497-512.
doi: 10.3390/tomography8010040.

Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies

Simon J Doran  1   2 , Mohammad Al Sa'd  2   3 , James A Petts  4 , James Darcy  1   2 , Kate Alpert  5 , Woonchan Cho  6 , Lorena Escudero Sanchez  2   7   8 , Sachidanand Alle  9 , Ahmed El Harouni  9 , Brad Genereaux  9 , Erik Ziegler  10   11 , Gordon J Harris  10   12   13 , Eric O Aboagye  2   3 , Evis Sala  2   7   8 , Dow-Mu Koh  2   14 , Dan Marcus  5   6
Affiliations

Integrating the OHIF Viewer into XNAT: Achievements, Challenges and Prospects for Quantitative Imaging Studies

Simon J Doran et al. Tomography. .

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.

Keywords: OHIF; XNAT; image visualisation; rapid reader; regions-of-interest; web viewer.

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Conflict of interest statement

The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript, or in the decision to publish the results. E.O.A. is a member of the Editorial Board of Tomography. G.J.H. is a member of Novometrics LLC and IQ Medical Imaging LLC and an advisor for Fovia Inc. E.S. is co-founder and shareholder of Lucida Medical Ltd. L.E.S. has received consulting fees from Lucida Medical Ltd.

Figures

Figure 1
Figure 1
ICR-XNAT-OHIF viewer development project timeline.
Figure 2
Figure 2
Visualisation of an RT Structure Set within the ICR-XNAT-OHIF viewer, also demonstrating the contour sidebar component developed as part of this project.
Figure 3
Figure 3
Our integration of the NVIDIA AIAA tool for automatic and semiautomatic segmentation based on machine learning models. A key advantage of the new tool is that the AI-assisted segmentations are processed and stored in exactly the same way as manual segmentations and so any shortcomings in the AI-based results, such as those seen in the left lung here can easily be refined manually and used to retrain the model.
Figure 4
Figure 4
Display of DICOM fractional segmentation objects both in standard 2D mode and multiplanar reformatting (MPR) mode, also demonstrating the integration of a new XNAT project navigation sidebar and that 3D mask ROIs are rendered correctly in all three planes.
Figure 5
Figure 5
Custom “four-up” view created by Radiologics Inc., demonstrating the visualisation of surface mesh files alongside contour-based ROIs. Currently available only commercially via Flywheel.
Figure 6
Figure 6
The new image composition tool, allowing overlay of DICOM series within a session to display, for example, PET-CT images.
Figure 7
Figure 7
Rapid Reader workflow view and modified viewer window illustrating new electronic case report form (eCRF) panel used to render a RadReport template. Note the additional navigation and report controls on the right-hand side of the toolbar. Rapid Reader is currently in development: source code is available on request, but not yet supported by the XNAT team.
Figure 7
Figure 7
Rapid Reader workflow view and modified viewer window illustrating new electronic case report form (eCRF) panel used to render a RadReport template. Note the additional navigation and report controls on the right-hand side of the toolbar. Rapid Reader is currently in development: source code is available on request, but not yet supported by the XNAT team.
See this image and copyright information in PMC

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