Measuring arterial wall perfusion using photon-counting computed tomography (CT): improving CT number accuracy of artery wall using image deconvolution

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.

Evaluation of a preclinical photon-counting CT prototype for pulmonary imaging

“…The essential advantage of a spectral photon-counting CT (SPCCT) system is that incoming x-ray photons are directly converted in electronic signals and spectrally binned by analyzing the pulse heights generated in a semiconductor detection layer 20 . Recent developments showed promising results in the areas of abdominal 21 – 25 , cardiovascular 25 – 30 , neurological 31 – 33 , and nanoparticle imaging 34 . In addition to those possibilities, SPCCT will offer an improved spatial resolution due to smaller detector pixel sizes compared to the current clinical standard.…”

High‐pitch, high temporal resolution, multi‐energy cardiac imaging on a dual‐source photon‐counting‐detector CT

“…The boundary of the rod was first segmented using a full-width-at-half -maximum thresholding technique. 19 The circularity index was then computed as:…”

“…The circularity and diameter of the rod were computed to evaluate geometric distortions introduced by motion. The boundary of the rod was first segmented using a full‐width‐at‐half‐maximum thresholding technique 19 . The circularity index was then computed as: $$$$\begin{equation} \def\eqcellsep{&}\begin{array}{*{20}{c}} {CI = 4\pi \cdot \frac{{Area}}{{Perimete{r^2}}}} \end{array} \end{equation}$$$$while the effective diameter was computed as: $$$$\begin{equation} \def\eqcellsep{&}\begin{array}{*{20}{c}} {\;{D_{eff}} = 2 \cdot \sqrt {\frac{{Area}}{\pi }} } \end{array} \end{equation}$$$$…”

Computed Tomography Perfusion Imaging Quality Affected by Different Input Arteries in Patients of Internal Carotid Artery Stenosis

“…Non-deconvolution model is based on the assumption that the venous outflow can be neglected in the first-pass state of contrast medium, and enhanced TDC can be obtained without extravasation of contrast medium. The deconvolution model is relatively independent and is not limited by the assumption of organ hemodynamics, which improves the accuracy of measurement [17][18][19].…”