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AB1001 A Cloud-Based Enterprise Image Analysis Platform for Musculoskeletal Diseases
  1. J. Vicente1,
  2. D. Roettger1,
  3. M. Boesen2,
  4. M. Hinton1,
  5. O. Kubassova1
  1. 1Image Analysis Ltd, London, United Kingdom
  2. 2Department of Radiology, Bispebjerg and Frederiksberg Hospital, Frederiksberg, Denmark

Abstract

Background MR Imaging is a key to objective analysis of joint diseases. The detection of erosions on radiograms, which only show structural changes, might be delayed for more than a year compared to the use of MRI. Despite its promising results, MRI is not yet widely used in clinical settings due to the lack of a fast, reproducible scoring system. Such a system should support multi-centre collaboration and data sharing for clinical trials.

Objectives We present a novel approach to a cloud-based platform for processing and quantitatively scoring static and dynamic MRI from patients with inflammatory and auto-immune conditions.

Methods A set of proprietary and optimized algorithms for quantitative assessment of disease activity and treatment effect in clinical trials and research studies has been developed. For static and dynamic scans, RAMRIS system for PIP, MCP and wrist [1], 2D and 3D Region of Interest (ROI) for synovitis, oedema and erosion [2] and analysis tools based on area under the curve, variance and histogram analysis, were implemented. Algorithms for quantitative assessment in Dynamic Contrast Enhanced MRI studies have been developed to provide objective measurement of Maximum Enhancement, Initial Rate of Enhancement, Time of Onset of Enhancement, Time of Washout, Initial Rate of Washout and Gadolinium (Gd) update [4]. Functionality to calculate the rate for the diffusion of contrast between blood and tissue (ktrans) has been integrated into the platform [5] as well as an automated patient and inter-study patient motion correction algorithm to improve image quality [6]. The cloud-based architecture has been designed to allow sharing of data, analysis and reports, which is achieved by group functionality where information is securely accessible only by group members [7].

Results A cloud-based platform, Dynamika, has been developed with an interface driven by clinical workflow and compatible with hospital PACs and DICOM standard. The cloud architecture properly supports data sharing and analysis results are accessible from any device with network connection. The quantitative assessment methods included allows for 95% of reproducibility of the results [8]. The algorithms have been successfully applied to clinical and research studies on RA [1,2,4,6,8].

Conclusions Dynamika is specially designed for use in clinical trials and research studies, it supports multi-centre collaboration and seamless data sharing and offers robust algorithms for objective assessment of disease activity and treatment effect.

References

  1. Rheumatology (2012) 51 (1): 134-143 doi:10.1093/rheumatology/ker220

  2. A Rastogi et al Automated Dynamic Contrast Enhanced Wrist 3Tesla MRI (DCE-MRI) in rheumatoid arthritis: evaluating inherent variability over a year, ISS Annual Meeting, 2013

  3. MICCAI 2007. LNCS 4792:261-269 doi:10.1007/978-3-540-75759-7_32

  4. Eur J Radiol (2010) 74(3):e67-72 doi:10.1016/j.ejrad.2009.04.010

  5. J Magn Reson Imging (1999) 10(3):223-232 doi:10.1002/(SICI)1522-2586(199909)10:3<223::AID-JMRI2>3.0.CO;2-S

  6. MICCAI 2008. P:57-64 URI: http://eprints.whiterose.ac.uk/id/eprint/5394

  7. M Hinton et al, An enterprise class computer aided detection platform scalable from laptop to cloud, RSNA 2013

  8. Eur J Radiol (2013) 82(8):1286-91. doi:10.1016/j.ejrad.2013.02.041

Disclosure of Interest None declared

DOI 10.1136/annrheumdis-2014-eular.4923

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