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Proceedings Paper

Temporal subtraction of 'virtual dual-energy' chest radiographs for improved conspicuity of growing cancers and other pathologic changes
Author(s): Kenji Suzuki; Samuel G. Armato; Roger Engelmann; Philip Caligiuri; Heber MacMahon
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Paper Abstract

A temporal-subtraction (TS) technique provides enhanced visualization of tumor growth and subtle pathologic changes between previous and current chest radiographs (CXRs) from the same patient. Our purpose was to develop a new TS technique incorporating "virtual dual-energy" technology to improve its enhancement quality. Our TS technique consisted of ribcage edge detection, rigid body transformation based on a global alignment criterion, image warping under the maximum cross-correlation criterion, and subtraction between the registered previous and current images. A major problem with TS was obscuring of abnormalities by rib artifacts due to misregistration. To reduce the rib artifacts, we developed a massive-training artificial neural network (MTANN) for separation of ribs from soft tissue. The MTANN was trained with input CXRs and the corresponding "teaching" soft-tissue CXRs obtained with real dualenergy radiography. Once trained, the MTANNs did not require a dual-energy system and provided "soft-tissue" images. Our database consisted of 100 sequential pairs of CXR studies from 53 patients. To assess the registration accuracy and clinical utility, a chest radiologist subjectively rated the original TS and rib-suppressed TS images on a 5-point scale. By use of "virtual dual-energy" technology, rib artifacts in the TS images were reduced substantially. The registration accuracy and clinical utility ratings for TS rib-suppressed images (3.7; 3.9) were significantly better than those for original TS images (3.5; 3.6) (P<0.01; P<0.02, respectively). Our "virtual dual-energy" TS CXRs can provide improved enhancement quality of TS images for the assessment of pathologic change.

Paper Details

Date Published: 4 March 2011
PDF: 6 pages
Proc. SPIE 7963, Medical Imaging 2011: Computer-Aided Diagnosis, 79630F (4 March 2011); doi: 10.1117/12.878662
Show Author Affiliations
Kenji Suzuki, The Univ. of Chicago (United States)
Samuel G. Armato, The Univ. of Chicago (United States)
Roger Engelmann, The Univ. of Chicago (United States)
Philip Caligiuri, Univ. of Utah (United States)
Heber MacMahon, The Univ. of Chicago (United States)

Published in SPIE Proceedings Vol. 7963:
Medical Imaging 2011: Computer-Aided Diagnosis
Ronald M. Summers; Bram van Ginneken, Editor(s)

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