A Monte Carlo simulation study of the effect of energy windows in computed tomography images based on an energy-resolved photon counting detector

Abstract

The energy-resolved photon counting detector provides the spectral information that can be used to generate images. The novel imaging methods, including the K-edge imaging, projection-based energy weighting imaging and image-based energy weighting imaging, are based on the energy-resolved photon counting detector and can be realized by using various energy windows or energy bins. The location and width of the energy windows or energy bins are important because these techniques generate an image using the spectral information defined by the energy windows or energy bins. In this study, the reconstructed images acquired with K-edge imaging, projection-based energy weighting imaging and image-based energy weighting imaging were simulated using the Monte Carlo simulation. The effect of energy windows or energy bins was investigated with respect to the contrast, coefficient-of-variation (COV) and contrast-to-noise ratio (CNR). The three images were compared with respect to the CNR. We modeled the x-ray computed tomography system based on the CdTe energy-resolved photon counting detector and polymethylmethacrylate phantom, which have iodine, gadolinium and blood. To acquire K-edge images, the lower energy thresholds were fixed at K-edge absorption energy of iodine and gadolinium and the energy window widths were increased from 1 to 25 bins. The energy weighting factors optimized for iodine, gadolinium and blood were calculated from 5, 10, 15, 19 and 33 energy bins. We assigned the calculated energy weighting factors to the images acquired at each energy bin. In K-edge images, the contrast and COV decreased, when the energy window width was increased. The CNR increased as a function of the energy window width and decreased above the specific energy window width. When the number of energy bins was increased from 5 to 15, the contrast increased in the projection-based energy weighting images. There is a little difference in the contrast, when the number of energy bin is increased from 15 to 33. The COV of the background in the projection-based energy weighting images is only slightly changed as a function of the number of energy bins. In the image-based energy weighting images, when the number of energy bins were increased, the contrast and COV increased and decreased, respectively. The CNR increased as a function of the number of energy bins. It was concluded that the image quality is dependent on the energy window, and an appropriate choice of the energy window is important to improve the image quality.

Quantitative material decomposition using spectral computed tomography with an energy-resolved photon-counting detector

“…The decomposition images were obtained from the optimal energy bins for each basis material. should be provided to obtain optimal energy weighting factors (Lee et al 2012). Dual-energy CT imaging with energy-resolved photon-counting detectors, on the other hand, is able to be realized using two energy-selective bins and enhance soft-tissue contrast.…”

“…Different from conventional x-ray detectors, which are operated in the charge integrating mode, photon-counting detectors with energy-discrimination capabilities are able to measure the photon energy deposited by each event and provide energy-resolved information from a polychromatic spectrum by using multiple energy thresholds (Shikhaliev 2008. Photon-counting detectors with energy-discrimination capabilities allow K-edge imaging and optimal energy weighting imaging, which significantly improve the contrastto-noise ratio (CNR) of CT images using energy-resolved information (Feuerlein et al 2008, Schlomka et al 2008, Schmidt 2009, Lee et al 2012. More importantly, multi-energy x-ray imaging, such as dual-and triple-energy imaging, is possible without additional exposures using spectral information produced by the photon-counting detectors.…”

K-edge ratio method for identification of multiple nanoparticulate contrast agents by spectral CT imaging

“…Selective imaging of contrast agents using the K-edge energy using energy-resolved CT was first investigated by Riederer and Mistretta [20]. Subsequently, the decomposition of the attenuation coefficient has been evaluated for identification of contrast agents in projection space [21,22] and in image space [23]. The common idea among all these attempts is to solve a system of equations to obtain the coefficient of each material contribution to the total attenuation coefficient at two or multiple energies.…”

“…The effect of energy bin width on image noise and optimisation of bin size in multi-energy CT have been investigated elsewhere [22,33,34]. We evaluated the effect of multiplication of mAs and slice thickness value (mAsT) on the K-edge ratio parametric map quality.…”

A Monte Carlo software bench for simulation of spectral k-edge CT imaging: Initial results

“…These discriminated photons carry with them the spectral information, which provides additional absorption information on the composition and density of the materials in the scanned object [3]. This information can be used in various clinical applications such as quantitative Kedge imaging and material decomposition [4], the usage of high-Z contrast agents [5,6], plaque detection and characterization [5,7], energy weighted image reconstruction [8,9], and metal artifact reduction [10]. Apart from the clinical applications already investigated, the introduction of PCD systems will allow for an additional and significant reduction of radiation exposure [11].…”