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Proceedings Paper

MR efficiency using automated MRI-desktop eProtocol
Author(s): Fei Gao; Yanzhe Xu; Anshuman Panda; Min Zhang; James Hanson; Congzhe Su; Teresa Wu; William Pavlicek; Judy R. James
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Paper Abstract

MRI protocols are instruction sheets that radiology technologists use in routine clinical practice for guidance (e.g., slice position, acquisition parameters etc.). In Mayo Clinic Arizona (MCA), there are over 900 MR protocols (ranging across neuro, body, cardiac, breast etc.) which makes maintaining and updating the protocol instructions a labor intensive effort. The task is even more challenging given different vendors (Siemens, GE etc.). This is a universal problem faced by all the hospitals and/or medical research institutions. To increase the efficiency of the MR practice, we designed and implemented a web-based platform (eProtocol) to automate the management of MRI protocols. It is built upon a database that automatically extracts protocol information from DICOM compliant images and provides a user-friendly interface to the technologists to create, edit and update the protocols. Advanced operations such as protocol migrations from scanner to scanner and capability to upload Multimedia content were also implemented. To the best of our knowledge, eProtocol is the first MR protocol automated management tool used clinically. It is expected that this platform will significantly improve the radiology operations efficiency including better image quality and exam consistency, fewer repeat examinations and less acquisition errors. These protocols instructions will be readily available to the technologists during scans. In addition, this web-based platform can be extended to other imaging modalities such as CT, Mammography, and Interventional Radiology and different vendors for imaging protocol management.

Paper Details

Date Published: 13 March 2017
PDF: 8 pages
Proc. SPIE 10138, Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications, 101380Z (13 March 2017); doi: 10.1117/12.2249712
Show Author Affiliations
Fei Gao, Arizona State Univ. (United States)
Yanzhe Xu, Arizona State Univ. (United States)
Anshuman Panda, Mayo Clinic Arizona (United States)
Min Zhang, Mayo Clinic Arizona (United States)
James Hanson, Mayo Clinic Arizona (United States)
Congzhe Su, Arizona State Univ. (United States)
Teresa Wu, Mayo Clinic Arizona (United States)
Arizona State Univ. (United States)
William Pavlicek, Mayo Clinic Arizona (United States)
Judy R. James, Mayo Clinic Arizona (United States)

Published in SPIE Proceedings Vol. 10138:
Medical Imaging 2017: Imaging Informatics for Healthcare, Research, and Applications
Tessa S. Cook; Jianguo Zhang, Editor(s)

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