Spectral CT imaging of vulnerable plaque with two independent biomarkers

Abstract

The purpose of this paper is to investigate the feasibility of a novel four-material decomposition technique for assessing the vulnerability of plaque with two contrast materials spectral computer tomography (CT) using two independent markers: plaque's inflammation and spotty calcification. A simulation study was conducted using an energy-sensitive photon-counting detector for k-edge imaging of the coronary arteries. In addition to detecting the inflammation status, which is known as a biological marker of a plaque's vulnerability, we use spotty calcium concentration as an independent marker to test a plaque's vulnerability. We have introduced a new method for detecting and quantifying calcium concentrations in the presence of two contrast materials (iodine and gold), calcium and soft tissue background. In this method, four-material decomposition was performed on a pixel-by-pixel basis, assuming there was an arbitrary mixture of materials in the voxel. The concentrations of iodine and gold were determined by the k-edge material decomposition based on the maximum likelihood method. The calibration curves of the attenuation coefficients, with respect to the concentrations of different materials, were used to separate the calcium signal from both contrast materials and different soft tissues in the mixtures. Three different materials (muscle, blood and lipid) were independently used as soft tissue. The simulations included both ideal and more realistic energy resolving detectors to measure the polychromatic photon spectrum in single slice parallel beam geometry. The ideal detector was used together with a 3 cm diameter digital phantom to demonstrate the decomposition method while a more realistic detector and a 33 × 24 cm(2) digital chest phantom were simulated to validate the vulnerability assessment technique. A 120 kVp spectrum was generated to produce photon flux sufficient for detecting contrast materials above the k-edges of iodine (33.2 keV) and gold (80.7 keV). By performing simulations on a 3 cm diameter digital phantom, we successfully identified four materials that were simultaneously present in the mixture at different proportions and in multiple locations on the phantom. Quantitative analysis with a chest digital phantom showed that the results for iodine, gold and calcium were highly correlated with the known concentrations. The analysis revealed a potentially powerful technique for assessing a plaque's vulnerability with two independent markers. High correlation and low relative errors between calculated and known materials' concentrations showed that the method is feasible. This technique can potentially have a high clinical impact.

Numerical observer for atherosclerotic plaque classification in spectral computed tomography

“…This is due to the fact that the lower energy photons carry larger contrast between different tissues than higher energy photons, 9 so a larger fraction was given to the low-kVp source rather than the high-kVp source. The MECT-PC system separated detected photons into five different energy bins with upper thresholds at 33, 49, 65, 80, and 120 keV, which were selected to exploit the K-edge of iodine while the rest of the energy thresholds chosen for a nearly equal number of counts contained in each bin, following the work by Baturin et al 4 The mean energies of the kV spectra were 41.80, 52.51, 56.47, 41.80(98.14), 41.80(52.51), and 52.51 keV for CT-80kVp, CT-120kVp, CT-140kVp, DECT-DS, DECT-SW, and MECT-PC, respectively. For all the CT systems, projection data were simulated with energy-weighted integrals over 1 keV spectral steps.…”

“…In this study, the numerical observer framework has been carefully validated on simulated data where the ground truth is known and where the parameters (e.g., plaque feature types, feature size, feature location, equivalent radiation doses of all modalities) can be easily changed to study the impacts of their variation on the classification performance. In the literature, several research studies demonstrated and validated their methods on the assumptions of ideal detectors for investigating the performance of conventional CT and spectral CT. 4,61,62 In order to model more realistic systems, it is suggested that our validation may apply decreased efficiency to the implementations of detectors and energy resolution for further investigation, especially for photon-counting spectral CT, e.g., applying 10% decrease in energy resolution with the quantum detection efficiency varying with photon energy 4 or using 12% full width at half maximum energy resolution. 61 In addition, a material decomposition method is suggested to apply to our data generation to include the possible variation that may obtain from the decomposition method.…”

“…In particular, plaque vulnerability can be assessed by resolving several features, including thin cap fibroatheroma, large lipid core, intimal spotty calcification, positive remodeling, and intraplaque neovascularization. 4 Low-grade stenosis may also be associated with increased risk of the future acute events involving carotid and coronary arteries, so determination of plaque features beyond the degree of stenosis is important. 5 Thus, for each imaging examination, it is not only necessary to identify plaques that create luminal obstructions but also necessary to interpret and characterize plaques that typically form within the arterial vascular system.…”

“…MECT has potential to better characterize and quantify the concentration of the plaque components (e.g., water, lipid, and calcium) as well as various contrast agents (e.g., iodine, gadolinium, and gold) relative to DECT. 4,12 The photon-counting detector approach is one technical strategy to implementing MECT, since it can allow more than the two-energy windows available in DECT. CT based on photon-counting detectors measures each incident x-ray photon as it interacts with the detector and generates an electronic signal, with the measured event then accumulated into different bins based on the measured photon energy.…”

“…Several studies have shown that material decomposition with spectral CT imaging provides better differentiation of materials for atherosclerotic plaque features. 4,12,[28][29][30][31] Nonetheless, there are limited studies of quantitative metrics for evaluating performance in terms of objective image assessment exploiting a numerical observer in spectral CT imaging. 32 The major problems may be because it is difficult to correlate the data from different energy bins, especially given the complexity of the spectral CT data, since spectral CT imaging can be considered a four-dimensional (4-D) technique to form measurements in three spatial dimensions and a unique energy dimension.…”