Search Results (2)

Background: This study investigated the image quality and detectability of double low-dose hepatic multiphase CT (DLDCT, which targeted about 30% reductions of both the radiation and iodine concentration) using a vendor-agnostic deep-learning-based contrast-boosting model (DL-CB) compared to those of standard-dose CT (SDCT) using hybrid iterative reconstruction. Methods: The CT images of 73 patients with chronic liver disease who underwent DLDCT between June 2023 and October 2023 and had SDCT were analyzed. Qualitative analysis of the overall image quality, artificial sensation, and liver contour sharpness on the arterial and portal phase, along with the hepatic artery clarity was conducted by two radiologists using a 5-point scale. For quantitative analysis, the image noise, signal-to-noise ratio, and contrast-to-noise ratio were measured. The lesion conspicuity was analyzed using generalized estimating equation analysis. Lesion detection was evaluated using the jackknife free-response receiver operating characteristic figures-of-merit. Results: Compared with SDCT, a significantly lower effective dose (16.4 ± 7.2 mSv vs. 10.4 ± 6.0 mSv, 36.6% reduction) and iodine amount (350 mg iodine/mL vs. 270 mg iodine/mL, 22.9% reduction) were utilized in DLDCT. The mean overall arterial and portal phase image quality scores of DLDCT were significantly higher than SDCT (arterial phase, 4.77 ± 0.45 vs. 4.93 ± 0.24, AUC_VGA 0.572 [95% CI, 0.507–0.638]; portal phase, 4.83 ± 0.38 vs. 4.92 ± 0.26, AUC_VGA 0.535 [95% CI, 0.469–0.601]). Furthermore, DLDCT showed significantly superior quantitative results for the lesion contrast-to-noise ratio (7.55 ± 4.55 vs. 3.70 ± 2.64, p < 0.001) and lesion detectability (0.97 vs. 0.86, p = 0.003). Conclusions: In patients with chronic liver disease, DLDCT using DL-CB can provide acceptable image quality without impairing the detection and evaluation of hepatic focal lesions compared to SDCT. Full article

In this article, the Rasch model is used for assessing a mean difference between two groups for a test of dichotomous items. It is assumed that random differential item functioning (DIF) exists that can bias group differences. The case of balanced DIF is distinguished from the case of unbalanced DIF. In balanced DIF, DIF effects on average cancel out. In contrast, in unbalanced DIF, the expected value of DIF effects can differ from zero and on average favor a particular group. Robust linking methods (e.g., invariance alignment) aim at determining group mean differences that are robust to the presence of DIF. In contrast, group differences obtained from nonrobust linking methods (e.g., Haebara linking) can be affected by the presence of a few DIF effects. Alternative robust and nonrobust linking methods are compared in a simulation study under various simulation conditions. It turned out that robust linking methods are preferred over nonrobust alternatives in the case of unbalanced DIF effects. Moreover, the theory of M-estimation, as an important approach to robust statistical estimation suitable for data with asymmetric errors, is used to study the asymptotic behavior of linking estimators if the number of items tends to infinity. These results give insights into the asymptotic bias and the estimation of linking errors that represent the variability in estimates due to selecting items in a test. Moreover, M-estimation is also used in an analytical treatment to assess standard errors and linking errors simultaneously. Finally, double jackknife and double half sampling methods are introduced and evaluated in a simulation study to assess standard errors and linking errors simultaneously. Half sampling outperformed jackknife estimators for the assessment of variability of estimates from robust linking methods. Full article