Accuracy of virtual non-contrast images with different algorithms in dual-energy computed tomography.

We investigated the accuracy of the computed tomography (CT) numbers of virtual non-contrast (VNC) images for two different material decomposition algorithms using the same image data for different diluted contrast agent concentrations. A container filled with contrast agents was inserted into a cylindrical phantom and scanned with dual-energy protocols (80/Sn140 kV, 100/Sn140 kV) using a dual-source CT. VNC images were generated by the 2-material decomposition (MD) algorithm using the energy of each tube voltage and the linear attenuation coefficient, calculated from the theoretical spectral curve of the agent and the CT number of the image, respectively. Furthermore, VNC images using 3-material decomposition (3-MD) algorithm were produced by applying LiverVNC, an analysis parameter implemented in the scanner. The robustness of both the algorithms was verified by investigating the CT numbers of the agents in the VNC. The closer the CT number is to 0 HU, the more robust the algorithm. Without beam-hardening correction, the CT numbers increased with an increase in concentration in both the algorithms, maximal at 50 mg/ml concentration, with CT numbers of 38 HU for 2-MD, 86 HU for 3-MD. With correction, CT numbers were ± 10 HU or less for both the algorithms up to 30 mg/ml concentration, whereas, for concentrations above 40 mg/ml, the maximal averaged CT number was 12 HU for 2-MD, 22 HU for 3-MD. For both the algorithms, the accuracy of the CT numbers was maintained in the low-concentration range; parameter adjustment was necessary to maintain the accuracy at concentrations higher than clinically expected.

Accuracy of spectral curves at different phantom sizes and iodine concentrations using dual-source dual-energy computed tomography.

To validate the accuracy of spectral curves obtained by an image-data-based algorithm and clarify the error factors that reduce accuracy. Iodine rods of known composition and different concentrations were inserted into a cylinder or elliptic-cylinder phantom and scanned according to the dual-energy protocol. Spectral curves were obtained by (i) theoretical calculation, (ii) image-data-based 2-material decomposition, and (iii) using a dedicated workstation. Accuracy was verified by comparing the spectral curve obtained by theoretical calculations with those obtained by the image-data-based algorithms or the dedicated workstations. For a quantitative evaluation, the error and relative error (RE) were calculated. In the image-data-based calculation, the errors with respect to the theoretical CT number ranged from - 8.3 to 71.1 HU. For all 192 combinations, 80.7% of the errors were under ± 15 HU, and 97.9% of the REs were under 10%. In the dedicated workstation, the errors ranged from - 94.7 to 26.8 HU. For all combinations, 68.8% of the errors were under ± 15 HU, and 68.2% of the REs were under 10%. By appropriately setting the effective energy corresponding to the CT number of the basis materials, an accurate spectral curve can be obtained. The beam-hardening effect is canceled by the 2-material decomposition process even without beam-hardening correction. Accuracy is primarily reduced by scattered radiation rather than the beam-hardening effect.

Method to calculate frequency characteristics of reconstruction filter kernel in X-ray computed tomography.

A computed tomography (CT) image is generally reconstructed by a filtered back projection (FBP) algorithm. In an FBP algorithm, the image quality primarily depends on a reconstruction filter kernel. Although the details of the filter kernel are not disclosed to users, the frequency response of the filter kernel can theoretically be calculated using the relational formula of the filter kernel and the modulation transfer function (MTF) of the reconstruction algorithm (MTFA). In this study, we proposed a method to determine the frequency response of a filter kernel and verify its validity. Two clinical CT scanners were used to derive the filter kernel. The MTF was obtained and subsequently separated to the MTF of the scanner system and MTFA. Using the relational formula of the filter kernel and MTFA, we calculated the frequency response of the filter kernel. To verify the calculated result, we measured the noise power spectrum (NPS). Additionally, the filter kernel was calculated using the relational formula of the filter kernel and NPS. In both CT scanners, the filter kernels calculated by the two methods showed good agreement, and we confirmed the validity of the results and the effectiveness of the proposed method. Furthermore, the inherent image quality performance of the CT scanner could be clarified by the reconstruction filter kernel.

Estimation and validation of the frequency responses of a scanner system and an image reconstruction system in X-ray computed tomography.

In computed tomography, factors that theoretically affect the modulation transfer function (MTF) in the region near the isocenter are the frequency responses of the scanner system (MTFS) and reconstruction processing (MTFA). Although MTFS and MTFA are performance indices that are not disclosed to the users, both can be estimated by the measured MTF with the use of theoretical formulas. In this study, we proposed two methods to obtain the MTFS and MTFA, and confirm their validity. The first method to obtain the MTFS and MTFA uses a theoretical formula and the measured MTF. Another method uses the measured MTF and the noise power spectrum. In both the methods, the MTFS and MTFA were obtained separately. By our proposed methods, performance indices that are not usually disclosed to the users can be known.