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Proceedings Paper

Development of a web-based DICOM-SR viewer for CAD data of multiple sclerosis lesions in an imaging informatics-based efolder
Author(s): Kevin Ma; Jonathan Wong; Mark Zhong; Jeff Zhang; Brent Liu
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Paper Abstract

In the past, we have presented an imaging-informatics based eFolder system for managing and analyzing imaging and lesion data of multiple sclerosis (MS) patients, which allows for data storage, data analysis, and data mining in clinical and research settings. The system integrates the patient’s clinical data with imaging studies and a computer-aided detection (CAD) algorithm for quantifying MS lesion volume, lesion contour, locations, and sizes in brain MRI studies. For compliance with IHE integration protocols, long-term storage in PACS, and data query and display in a DICOM compliant clinical setting, CAD results need to be converted into DICOM-Structured Report (SR) format. Open-source dcmtk and customized XML templates are used to convert quantitative MS CAD results from MATLAB to DICOM-SR format. A web-based GUI based on our existing web-accessible DICOM object (WADO) image viewer has been designed to display the CAD results from generated SR files. The GUI is able to parse DICOM-SR files and extract SR document data, then display lesion volume, location, and brain matter volume along with the referenced DICOM imaging study. In addition, the GUI supports lesion contour overlay, which matches a detected MS lesion with its corresponding DICOM-SR data when a user selects either the lesion or the data. The methodology of converting CAD data in native MATLAB format to DICOM-SR and displaying the tabulated DICOM-SR along with the patient’s clinical information, and relevant study images in the GUI will be demonstrated. The developed SR conversion model and GUI support aim to further demonstrate how to incorporate CAD post-processing components in a PACS and imaging informatics-based environment.

Paper Details

Date Published: 19 March 2014
PDF: 11 pages
Proc. SPIE 9039, Medical Imaging 2014: PACS and Imaging Informatics: Next Generation and Innovations, 903903 (19 March 2014); doi: 10.1117/12.2044076
Show Author Affiliations
Kevin Ma, The Univ. of Southern California (United States)
Jonathan Wong, The Univ. of Southern California (United States)
Mark Zhong, The Univ. of Southern California (United States)
Jeff Zhang, The Univ. of Southern California (United States)
Brent Liu, The Univ. of Southern California (United States)

Published in SPIE Proceedings Vol. 9039:
Medical Imaging 2014: PACS and Imaging Informatics: Next Generation and Innovations
Maria Y. Law; Tessa S. Cook, Editor(s)

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