Cone beam computed tomography systems generate 3D volumetric images, which provide further morphological
information compared to radiography and tomosynthesis systems. However, reconstructed images by FDK algorithm
contain cone beam artifacts when a cone angle is large. To reduce the cone beam artifacts, two-pass algorithm has been
proposed. The two-pass algorithm considers the cone beam artifacts are mainly caused by high density materials, and
proposes an effective method to estimate error images (i.e., cone beam artifacts images) by the high density materials.
While this approach is simple and effective with a small cone angle (i.e., 5 - 7 degree), the correction performance is
degraded as the cone angle increases. In this work, we propose a new method to reduce the cone beam artifacts using a
dual energy technique. The basic idea of the proposed method is to estimate the error images generated by the high
density materials more reliably. To do this, projection data of the high density materials are extracted from dual energy
CT projection data using a material decomposition technique, and then reconstructed by iterative reconstruction using
total-variation regularization. The reconstructed high density materials are used to estimate the error images from the
original FDK images. The performance of the proposed method is compared with the two-pass algorithm using root
mean square errors. The results show that the proposed method reduces the cone beam artifacts more effectively,
especially with a large cone angle.
In a cone beam computed tomography (CBCT), the severity of the cone beam artifacts is increased as the cone angle increases. To reduce the cone beam artifacts, several modified FDK algorithms and compressed sensing based iterative algorithms have been proposed. In this paper, we used two pass algorithm and Gradient-Projection-Barzilai-Borwein (GPBB) algorithm to reduce the cone beam artifacts, and compared their performance using structural similarity (SSIM) index. In two pass algorithm, it is assumed that the cone beam artifacts are mainly caused by extreme-density(ED) objects, and therefore the algorithm reproduces the cone beam artifacts(i.e., error image) produced by ED objects, and then subtract it from the original image. GPBB algorithm is a compressed sensing based iterative algorithm which minimizes an energy function for calculating the gradient projection with the step size determined by the Barzilai- Borwein formulation, therefore it can estimate missing data caused by the cone beam artifacts. To evaluate the performance of two algorithms, we used testing objects consisting of 7 ellipsoids separated along the z direction and cone beam artifacts were generated using 30 degree cone angle. Even though the FDK algorithm produced severe cone beam artifacts with a large cone angle, two pass algorithm reduced the cone beam artifacts with small residual errors caused by inaccuracy of ED objects. In contrast, GPBB algorithm completely removed the cone beam artifacts and restored the original shape of the objects.
To describe internal noise levels for different target sizes, contrasts, and noise structures, Gaussian targets with four different sizes (i.e., standard deviation of 2,4,6 and 8) and three different noise structures(i.e., white, low-pass, and highpass) were generated. The generated noise images were scaled to have standard deviation of 0.15. For each noise type, target contrasts were adjusted to have the same detectability based on NPW, and the detectability of CHO was calculated accordingly. For human observer study, 3 trained observers performed 2AFC detection tasks, and correction rate, Pc, was calculated for each task. By adding proper internal noise level to numerical observer (i.e., NPW and CHO), detectability of human observer was matched with that of numerical observers. Even though target contrasts were adjusted to have the same detectability of NPW observer, detectability of human observer decreases as the target size increases. The internal noise level varies for different target sizes, contrasts, and noise structures, demonstrating different internal noise levels should be considered in numerical observer to predict the detection performance of human observer.