OBJECTIVE The aim of the study was to evaluate the effect of slice thickness, iterative reconstruction (IR) algorithm, and kernel selection on measurement accuracy and interobserver variability for semiautomated renal cortex volumetry (RCV) with multislice computed tomography (CT). METHODS Ten patients (62.4 ± 17.2 years) undergoing abdominal biphasic multislice computed tomography were enrolled in this retrospective study. Computed tomography data sets were reconstructed at 1-, 2-, and 5-mm slice thickness with 2 different IR algorithms (iDose, IMRST) and 2 different kernels (IMRS and IMRR) (Philips, the Netherlands). Two readers independently performed semiautomated RCV for each reconstructed data set to calculate left kidney volume (LKV) and split renal function (SRF). Statistics were calculated using analysis of variance with Geisser-Greenhouse correction, followed by Tukey multiple comparisons post hoc test. Statistical significance was defined as P ≤ 0.05. RESULTS Semiautomated RCV of 120 data sets (240 kidneys) was successfully performed by both readers. Semiautomated RCV provides comparable results for LKV and SRF with 3 different slice thicknesses, 2 different IR algorithms, and 2 different kernels. Only the 1-mm slice thickness showed significant differences for LKV between IMRR and IMRS (P = 0.02, mean difference = 4.28 bb) and IMRST versus IMRS (P = 0.02, mean difference = 4.68 cm) for reader 2. Interobserver variability was low between both readers irrespective of slice thickness and reconstruction algorithm (0.82 ≥ P ≥ 0.99). CONCLUSIONS Semiautomated RCV measurements of LKV and SRF are independent of slice thickness, IR algorithm, and kernel selection. These findings suggest that comparisons between studies using different slice thicknesses and reconstruction algorithms for RCV are valid.OBJECTIVE We developed a patient-specific contrast enhancement optimizer (p-COP) that can exploratorily calculate the contrast injection protocol required to obtain optimal enhancement at target organs using a computer simulator. Appropriate contrast media dose calculated by the p-COP may minimize interpatient enhancement variability. Our study sought to investigate the clinical utility of p-COP in hepatic dynamic computed tomography (CT). METHODS One hundred thirty patients (74 men, 56 women; median age, 65 years) undergoing hepatic dynamic CT were randomly assigned to 1 of 2 contrast media injection protocols using a random number table. Group A (n = 65) was injected with a p-COP-determined iodine dose (developed by Higaki and Awai, Hiroshima University, Japan). In group B (n = 65), a standard protocol was used. The variability of measured CT number (SD) between the 2 groups of aortic and hepatic enhancement was compared using the F test. In the equivalence test, the equivalence margins for aortic and hepatandard injection protocol for hepatic dynamic CT.OBJECTIVES This study aimed to assess if dual-energy computed tomography (DECT) quantitative analysis and radiomics can differentiate normal liver, hepatic steatosis, and cirrhosis. Selleck TPCA-1 MATERIALS AND METHODS Our retrospective study included 75 adult patients (mean age, 54 ± 16 years) who underwent contrast-enhanced, dual-source DECT of the abdomen. We used Dual-Energy Tumor Analysis prototype for semiautomatic liver segmentation and DECT and radiomic features. The data were analyzed with multiple logistic regression and random forest classifier to determine area under the curve (AUC). RESULTS Iodine quantification (AUC, 0.95) and radiomic features (AUC, 0.97) differentiate between healthy and abnormal liver. Combined fat ratio percent and mean mixed CT values (AUC, 0.99) were the strongest differentiators of healthy and steatotic liver. The most accurate differentiating parameters of normal liver and cirrhosis were a combination of first-order statistics (90th percentile), gray-level run length matrix (short-run low gray-level emphasis), and gray-level size zone matrix (gray-level nonuniformity normalized; AUC, 0.99). CONCLUSION Dual-energy computed tomography iodine quantification and radiomics accurately differentiate normal liver from steatosis and cirrhosis from single-section analyses.PURPOSE The aim of this study was to compare hepatic vascular and parenchymal image quality between direct and peristaltic contrast injectors during hepatic computed tomography (HCT). METHODS Patients (n = 171) who underwent enhanced HCT and had both contrast media protocols and injector systems were included; group A direct-drive injector with fixed 100 mL contrast volume (CV), and group B peristaltic injector with weight-based CV. Opacification, contrast-to-noise ratio, signal-to-noise ratio, radiation dose, and CV for liver parenchyma and vessels in both groups were compared by paired t test and Pearson correlation. Receiver operating characteristic curve, visual grading characteristics, and Cohen κ were used. RESULTS Contrast-to-noise ratio compared with hepatic vein for functional liver, contrast-to-noise ratio was higher in group B (2.17 ± 0.83) than group A (1.82 ± 0.63); portal vein higher in group B (2.281 ± 0.96) than group A (2.00 ± 0.66). Signal-to-noise ratio for functional liver was higher in group B (5.79 ± 1.58 Hounsfield units) than group A (4.81 ± 1.53 Hounsfield units). Radiation dose and contrast media were lower in group B (1.98 ± 0.92 mSv) (89.51 ± 15.49 mL) compared with group A (2.77 ± 1.03 mSv) (100 ± 1.00 mL). Receiver operating characteristic curve demonstrated increased reader in group B (95% confidence interval, 0.524-1.0) than group A (95% confidence interval, 0.545-1.0). Group B had increased revenue up to 58% compared with group A. CONCLUSIONS Image quality improvement is achieved with lower CV and radiation dose when using peristaltic injector with weight-based CV in HCT.INTRODUCTION Liver segmentation and volumetry have traditionally been performed using computed tomography (CT) attenuation to discriminate liver from other tissues. In this project, we evaluated if spectral detector CT (SDCT) can improve liver segmentation over conventional CT on 2 segmentation methods. MATERIALS AND METHODS In this Health Insurance Portability and Accountability Act-compliant institutional review board-approved retrospective study, 30 contrast-enhanced SDCT scans with healthy livers were selected. The first segmentation method is based on Gaussian mixture models of the SDCT data. The second method is a convolutional neural network-based technique called U-Net. Both methods were compared against equivalent algorithms, which used conventional CT attenuation, with hand segmentation as the reference standard. Agreement to the reference standard was assessed using Dice similarity coefficient. RESULTS Dice similarity coefficients to the reference standard are 0.93 ± 0.02 for the Gaussian mixture model method and 0.