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Serial assessment of CT coronary calcifications for regression/progression analysis (Conference Presentation)
Author(s): David L. Wilson; Di Wen; Brendan Eck; Jerry Lipinski; Jacob Levi; Robert C. Gilkeson; Christopher Longenecker
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Paper Abstract

We are creating specialized software to enable serial assessment of individual coronary calcifications. Calcifications give reliable, cost-effective, non-invasive, contrast-agent-free evidence of coronary artery disease. From pathobiology and clinical observations, it is likely that progression of small, spotty, low-density calcifications will provide better evidence of disease progression than typically applied whole-heart Agatston, which is numerically dominated by large, potentially stable, calcifications. In our new analysis paradigm, we analyze individual calcification progression/regression/formation measures. Processing consisted of segmentation; initial rigid body registration using gray scale normalized cross correlation with the last scan as reference; and a modified iterative closest point (ICP) algorithm. Rather than using two, 3D point clouds, we assigned morphological characteristics (e.g. center, divergence, and maximum HU) to each calcification and performed ICP in a high dimensional space so that calcifications of similar shape and size matched. New measurements were enabled. For one example patient over 96-weeks, three new calcifications were formed and one was lost. Eleven of twelve calcifications increased in volume. We applied a new partial volume correction method to normal 3-mm thick scans and compared results to separate high resolution (0.67-mm) scans in a cadaveric heart. Mass score differences were improved from ≈12% to <2% with partial volume correction. Reproducibly of measurements from repeated scans was improved. Preliminary results suggest that we can use advanced computational methods to make detailed serial assessments of coronary artery calcifications, thereby enabling new studies with other biomarkers, response to drug therapy, effects of environmental factors, genes, and improvements to risk estimates.

Paper Details

Date Published: 14 March 2018
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Proc. SPIE 10573, Medical Imaging 2018: Physics of Medical Imaging, 105731Y (14 March 2018); doi: 10.1117/12.2293779
Show Author Affiliations
David L. Wilson, Case Western Reserve Univ. (United States)
Di Wen, Case Western Reserve Univ. (United States)
Brendan Eck, Case Western Reserve Univ. (United States)
Jerry Lipinski, Case Western Reserve Univ. (United States)
Jacob Levi, Case Western Reserve Univ. (United States)
Robert C. Gilkeson, Case Western Reserve Univ. (United States)
Christopher Longenecker, Case Western Reserve Univ (United States)


Published in SPIE Proceedings Vol. 10573:
Medical Imaging 2018: Physics of Medical Imaging
Joseph Y. Lo; Taly Gilat Schmidt; Guang-Hong Chen, Editor(s)

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