Mark T. Madsen PhD, Abstract Co-Author: Author, Oakstone Medical Publishing

Anil Tarik Taner MD, Presenter: Nothing to Disclose

Kevin Staley Berbaum PhD, Abstract Co-Author: Nothing to Disclose

Eliot L. Siegel MD, Abstract Co-Author: Research grant, General Electric Company Speakers Bureau, Siemens AG Board of Directors, Carestream Health, Inc Research grant, XYBIX Systems, Inc Research grant, Steelcase, Inc Research grant, Anthro Corp Research grant, RedRick Technologies Inc Research grant, Evolved Technologies Corporation Research grant, Barco nv Research grant, Intel Corporation Research grant, Dell Inc Research grant, Herman Miller, Inc Research grant, Virtual Radiology Research grant, Anatomical Travelogue, Inc Medical Advisory Board, Fovia, Inc Medical Advisory Board, Vital Images Medical Advisory Board, McKesson Corporation Medical Advisory Board, Carestream Health, Inc Medical Advisory Board, Bayer AG Research, TeraRecon, Inc Medical Advisory Board, Bracco Group Researcher, Bracco Group Medical Advisory Board, Merge Healthcare Incorporated Medical Advisory Board, Microsoft Corporation Researcher, Microsoft Corporation

Abnormality manipulation software allows the creation of customized case sets, facilitating image perception, exposure optimization, and other research on liver lesion detection and characterization. The software is available upon request.

Investigations of detection and perception with medical images require acquisition and archival of clinical case sets where presence or absence of target stimuli is carefully controlled. The accumulation of appropriate cases is tedious and is often the time consuming part of the research. Additionally, it is difficult to control for variability in lesion appearance and location for these studies. We have developed software tools for removing and inserting abnormalities in cross-sectional studies and have adapted these for specific use in thin slice CT of the liver.

The abnormality manipulation software allows a researcher to create a synthetic library of images that represent lesions from subtle, rare, or otherwise noteworthy cases. The lesions are represented in three-dimensional space and can then be inserted into another location on the same study or into another study altogether. The software is also able to remove a lesion entirely from a study. When adding and removing lesions from studies, the software is designed to evaluate and adjust for the surrounding tissues so that the fact that a lesion has been added or removed is not perceptible to the study participant. In addition to these primary features, the software also allows adjustment of the appearance of the lesion to make a lesion subtler or more prominent in appearance.

Liver CT is a rich area for perceptual investigations because of the large number of pathologies that can be present and the importance of accurate diagnostic interpretation and difficulty in detecting subtle or early disease. The ability to manipulate thin slice CT images allows the creation of test case sets that can be used in many types of investigations. Our next applications of this software will include investigation of the effect of dose reduction on the perception of small low contrast liver lesions as well as the impact of lesion location and conspicuity on content based retrieval of images.

Madsen, M, Taner, A, Berbaum, K, Siegel, E, Software for the Addition and Removal of Liver Lesions in Cross-Sectional Imaging to Facilitate Research Studies on Detection and Perception.  Radiological Society of North America 2011 Scientific Assembly and Annual Meeting, November 26 - December 2, 2011 ,Chicago IL. http://archive.rsna.org/2011/11006730.html