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Proceedings Paper

The implementation of an AR (augmented reality) approach to support mammographic interpretation training: an initial feasibility study
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Paper Abstract

Appropriate feedback plays an important role in optimising mammographic interpretation training whilst also ensuring good interpretation performance. The traditional keyboard, mouse and workstation technical approach has a critical limitation in providing supplementary image-related information and providing complex feedback in real time. Augmented Reality (AR) provides a possible superior approach in this situation, as feedback can be provided directly overlaying the displayed mammographic images so making a generic approach which can also be vendor neutral. In this study, radiological feedback was dynamically remapped virtually into the real world, using perspective transformation, in order to provide a richer user experience in mammographic interpretation training. This is an initial attempt of an AR approach to dynamically superimpose pre-defined feedback information of a DICOM image on top of a radiologist’s view, whilst the radiologist is examining images on a clinical workstation. The study demonstrates the feasibility of the approach, although there are limitations on interactive operations which are due to the hardware used. The results of this fully functional approach provide appropriate feedback/image correspondence in a simulated mammographic interpretation environment. Thus, it is argued that employing AR is a feasible way to provide rich feedback in the delivery of mammographic interpretation training.

Paper Details

Date Published: 10 March 2017
PDF: 8 pages
Proc. SPIE 10136, Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment, 1013604 (10 March 2017); doi: 10.1117/12.2255833
Show Author Affiliations
Qiang Tang, Loughborough Univ. (United Kingdom)
Yan Chen, Loughborough Univ. (United Kingdom)
Alastair G. Gale, Loughborough Univ. (United Kingdom)


Published in SPIE Proceedings Vol. 10136:
Medical Imaging 2017: Image Perception, Observer Performance, and Technology Assessment
Matthew A. Kupinski; Robert M. Nishikawa, Editor(s)

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