8 December 2017 Measuring arterial wall perfusion using photon-counting computed tomography (CT): improving CT number accuracy of artery wall using image deconvolution
Kishore Rajendran, Shuai Leng, Steven M. Jorgensen, Jill L. Anderson, Ahmed F. Halaweish, Dilbar Abdurakhimova, Erik L. Ritman, Cynthia H. McCollough
Author Affiliations +
Abstract
Changes in arterial wall perfusion mark the onset of atherosclerosis. A characteristic change is the increased spatial density of vasa vasorum (VV), the microvessels in the arterial walls. Measuring this increased VV (IVV) density using contrast-enhanced computed tomography (CT) has had limited success due to blooming effects from contrast media. If the system point-spread function (PSF) is known, then the blooming effect can be modeled as a convolution between the true signal and the PSF. We report the application of image deconvolution to improve the CT number accuracy in the arterial wall of a phantom and in a porcine model of IVV density, both scanned using a whole-body research photon-counting CT scanner. A 3D-printed carotid phantom filled with three concentrations of iodinated contrast material was scanned to assess blooming and its effect on wall CT number accuracy. The results showed a reduction in blooming effects following image deconvolution, and, consequently, a better delineation between lumen and wall was achieved. Results from the animal experiment showed improved CT number difference between the carotid with IVV density and the normal carotid artery after deconvolution, enabling the detection of VV proliferation, which may serve as an early indicator of atherosclerosis.
© 2017 Society of Photo-Optical Instrumentation Engineers (SPIE) 2329-4302/2017/$25.00 © 2017 SPIE
Kishore Rajendran, Shuai Leng, Steven M. Jorgensen, Jill L. Anderson, Ahmed F. Halaweish, Dilbar Abdurakhimova, Erik L. Ritman, and Cynthia H. McCollough "Measuring arterial wall perfusion using photon-counting computed tomography (CT): improving CT number accuracy of artery wall using image deconvolution," Journal of Medical Imaging 4(4), 044006 (8 December 2017). https://doi.org/10.1117/1.JMI.4.4.044006
Received: 27 April 2017; Accepted: 20 October 2017; Published: 8 December 2017
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Cited by 7 scholarly publications.
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KEYWORDS
Computed tomography

Arteries

Image deconvolution

Deconvolution

Optical computing

Point spread functions

3D modeling

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