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Journal of Medical Imaging • new

Quantification of uncertainty in the assessment of coronary plaque in CCTA through a dynamic cardiac phantom and 3D-printed plaque model
Author(s): Taylor Richards; Gregory M. Sturgeon; Juan Carlos Ramirez-Giraldo; Geoffrey D. Rubin; Lynne Hurwitz Koweek; William Paul Segars; Ehsan Samei
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Paper Abstract

The purpose of this study was to develop a dynamic physical cardiac phantom with a realistic coronary plaque to investigate stenosis measurement accuracy under clinically relevant heart-rates. The coronary plaque model (5 mm diameter, 50% stenosis, and 32 mm long) was designed and 3D-printed with tissue equivalent materials (calcified plaque with iodine-enhanced lumen). Realistic cardiac motion was modeled by converting computational cardiac motion vectors into compression and rotation profiles executed by a commercial base cardiac phantom. The phantom was imaged on a dual-source CT system applying a retrospective gated coronary CT angiography (CCTA) protocol using synthesized motion-synchronized electrocardiogram (ECG) waveforms. Multiplanar reformatted images were reconstructed along vessel centerlines. Enhanced lumens were segmented by five independent operators. On average, stenosis measurement accuracy was 0.9% positively biased for the motion-free condition. Average measurement accuracy monotonically decreased from 0.9% positive bias for the motion-free condition to 18.5% negative bias at 90 beats per minute. Contrast-to-noise ratio, lumen circularity, and segmentation conformity also decreased monotonically with increasing heart-rate. These results demonstrate successful implementation of a base cardiac phantom with a 3D-printed coronary plaque model, relevant motion profile, and coordinated ECG waveform. They further show the utility of the model to ascertain metrics of CCTA accuracy and image quality under realistic plaque, motion, and acquisition conditions.

Paper Details

Date Published: 17 January 2018
PDF: 9 pages
J. Med. Imag. 5(1) 013501 doi: 10.1117/1.JMI.5.1.013501
Published in: Journal of Medical Imaging Volume 5, Issue 1
Show Author Affiliations
Taylor Richards, Carl E. Ravin Advanced Imaging Labs. (United States)
Gregory M. Sturgeon, Carl E. Ravin Advanced Imaging Labs. (United States)
Juan Carlos Ramirez-Giraldo, Siemens Healthineers (United States)
Geoffrey D. Rubin, Duke Univ. School of Medicine (United States)
Lynne Hurwitz Koweek, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
William Paul Segars, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)
Ehsan Samei, Carl E. Ravin Advanced Imaging Labs., Duke Univ. (United States)

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