Projection-based Energy Weighting Research Articles

Improved projection-based energy weighting for spectral CT

Conventional X-ray CT scanners have limited ability to distinguish low-contrast substances. However, spectral CTs with photon counting detectors can identify photon energy and utilize spectral information, which is expected to achieve improved contrast. Energy weighting is a kind of reconstruction method for spectral CT. By assigning appropriate weight for each energy channel, the image contrast can be improved. Hence, how to determine the optimal weights is very important. In this paper, we developed an improved projection-based energy weighting model for spectral CT. In this model, the object thickness of low-density materials is assumed as a constant, and the measured spectrum distribution is used to calculate the weight coefficients. Both phantom and tissue experiments were conducted in spectral CT scanner. The results showed that the thickness of low-density materials has little influence on the energy weight, so it can be regarded as a constant. For low-contrast phantom, the contrast-to-noise ratio was improved ~ 32% by the proposed projection-based weighting method. The improved projection-based energy weighting model is effective in practice. It can increase the contrast of low-density materials.

A feasibility study of projection-based energy weighting based on a photon-counting detector in contrast-enhanced digital subtraction mammography: A simulation study

Contrast media, such as iodine and gadolinium, are generally used in digital subtraction mammography to enhance the contrast between target and background materials. In digital subtraction mammography, where one image (with contrast medium) is subtracted from another (anatomical background) to facilitate visualization of the tumor structure, tumors can be more easily distinguished after the injection of a contrast medium. In order to have more an effective method to increase the contrast-to-noise ratio (CNR), we applied a projection-based energy-weighting method. The purpose of this study is to demonstrate the feasibility of using the projection-based energy-weighting method in digital subtraction mammography. Unlike some other previous studies, we applied the projection-based energy-weighting method to more practical mammography conditions by using the Monte Carlo method to simulate four different iodine solutions embedded in a breast phantom comprised of 50% adipose and 50% glandular tissues. We also considered an optimal tube voltage and anode/filter combination in digital iodine contrast media mammography in order to maximize the figure-of-merit (FOM). The simulated source energy was from 20 to 45 keV to prevent electronic noise and include the k-edge energy of iodine (33.2 keV). The results showed that the projection-based energy-weighting improved the CNR by factors of 1.05–1.86 compared to the conventionally integrated images. Consequently, the CNR of digital subtraction mammography images can be improved by using projection-based energy-weighting with photon-counting detectors.

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

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.