This study investigated accuracy and consistency of epicardial adipose tissue (EAT) quantification in non-ECG-gated chest computed tomography (CT) scans.
EAT volume was semi-automatically quantified using a standard Hounsfield unit threshold (- 190, - 30) in three independent cohorts: (1) Cohort 1 (N = 49): paired 120 kVp ECG-gated cardiac non-contrast CT (NCCT) and 120 kVp non-ECG-gated chest NCCT; (2) Cohort 2 (N = 34): paired 120 kVp cardiac NCCT and 100 kVp non-ECG-gated chest NCCT; (3) Cohort 3 (N = 32): paired non-ECG-gated chest NCCT and chest contrast-enhanced CT (CECT) datasets (including arterial phase and venous phase). Images were reconstructed with the slice thicknesses of 1.25 mm and 5 mm in the chest CT datasets, and 3 mm in the cardiac NCCT datasets.
In Cohort 1, the chest NCCT-1.25 mm EAT volume was similar to the cardiac NCCT EAT volume, while chest NCCT-5 mm underestimated the EAT volume by 7.5%. In Cohort 2, 100 kVp chest NCCT-1.25 mm were 13.2% larger than 120 kVp cardiac NCCT EAT volumes. In Cohort 3, the chest arterial CECT and venous CECT dataset underestimated EAT volumes by ~ 28% and ~ 18%, relative to chest NCCT datasets. All chest CT-derived EAT volumes were similarly associated with significant coronary atherosclerosis with cardiac CT counterparts.
The 120 kVp non-ECG-gated chest NCCT-1.25 mm images produced EAT volumes comparable to cardiac NCCT. Chest CT EAT volumes derived from consistent imaging settings are excellent alternatives to the cardiac NCCT to investigate their association with coronary artery disease.

To investigate the effects of computed tomography (CT) reconstruction slice thickness and contrast-enhancement phase on the differential diagnosis performance of radiomic signature in lung adenocarcinoma.
A total of 187 patients who had been pathologically confirmed with lung adenocarcinoma and nonadenocarcinoma were divided into a training cohort (n = 149) and validation cohort (n = 38). All the patients underwent contrast-enhanced CT and the images were reconstructed with different slice thickness. The radiomic features were extracted from different slice thickness and scan phase. The logistic regression (LR) algorithm was used to build a machine learning model for each group. The area under the curve (AUC) obtained from the receiver operating characteristic (ROC) curve and DeLong test was used to evaluate its discriminating performance.
Finally, 34 image features and five semantic features were selected to establish a radiomics model. Based on the three contrast-enhanced CT phases and four reconstruction slice thickness, 12 groups of radiomics models showed good discrimination ability with the AUCs range from 0.9287 to 0.9631, sensitivity range from 0.8349 to 0.9083, specificity range from 0.825 to 0.925 in the training group. Similar results were observed in the validation group. However, there was no statistical significance between the different CT scan phase groups and different slice thickness (p > 0.05).
The radiomic analysis of contrast-enhanced CT can be used for the differential diagnosis of lung adenocarcinoma. Moreover, different slice thickness and contrast-enhanced scan phase did not affect the discriminating ability in the radiomics models.

Objectives.To test the effect of traditional up-sampling slice thickness (ST) methods on the reproducibility of CT radiomics features of liver tumors and investigate the improvement using a deep neural network (DNN) scheme.Methods.CT images with ≤ 1 mm ST in the public dataset were converted to low-resolution (3 mm, 5 mm) CT images. A DNN model was trained for the conversion from 3 mm ST and 5 mm ST to 1 mm ST and compared with conventional interpolation-based methods (cubic, linear, nearest) using structural similarity (SSIM) and peak-signal-to-noise-ratio (PSNR). Radiomics features were extracted from the tumor and tumor ring regions. The reproducibility of features from images converted using DNN and interpolation schemes were assessed using the concordance correlation coefficients (CCC) with the cutoff of 0.85. The paired t-test and Mann-Whitney U test were used to compare the evaluation metrics, where appropriate.Results.CT images of 108 patients were used for training (n = 63), validation (n = 11) and testing (n = 34). The DNN method showed significantly higher PSNR and SSIM values (p < 0.05) than interpolation-based methods. The DNN method also showed a significantly higher CCC value than interpolation-based methods. For features in the tumor region, compared with the cubic interpolation approach, the reproducible features increased from 393 (82%) to 422(88%) for the conversion of 3-1 mm, and from 305(64%) to 353(74%) for the conversion of 5-1 mm. For features in the tumor ring region, the improvement was from 395 (82%) to 431 (90%) and from 290 (60%) to 335 (70%), respectively.Conclusions.The DNN based ST up-sampling approach can improve the reproducibility of CT radiomics features in liver tumors, promoting the standardization of CT radiomics studies in liver cancer.

UNASSIGNED: To investigate the influence of slice thickness on radiomic feature (RF) values in solid pulmonary nodules and evaluate the effect of a linear interpolation method in correcting the influence.
UNASSIGNED: Thirty pulmonary nodules from 28 patients were selected prospectively with a thick-slice of 5 mm and a thin-slice of 1.25 mm on CT. A resampling method was used to normalize the voxel size of thick and thin slices CT images to 1×1×1 mm3 by linear interpolation. Lung nodules were segmented manually. A total of 396 radiomic features (RFs) were extracted from thick-slice and thin-slice images, together with the images resampled from thick (thick-r) and thin (thin-r) slices. The differences between the RF values were evaluated using a paired t-test. A comparison between groups was made using the Chi-squared test.
UNASSIGNED: Among the 396 RFs, 305 RFs showed an intraclass correlation coefficient ≥0.75 after test-retest analysis (including 22 histogram features, 20 geometry features, and 263 texture features). In the non-resampled data, 239 RF values (78.4%, 239/305) showed significant differences between thick and thin slice CT images. Resampling of thick images revealed that 202 RF values (66.2%, 202/305) showed significant differences between thick-r and thin slice CT images, showing a significant decrease in the number of different RF values when compared to non-resampled data (P<0.01). For the RF subgroups, only texture features showed a significant reduction in the number of different RF values after resampling (P<0.01). When both thick and thin slice images were resampled, the number of significantly different RF values between thick-r and thin-r images was increased to 247 (81.0%, 247/305), showing no significant difference when compared to non-resampled data (P=0.421).
UNASSIGNED: Slice thickness demonstrated a considerable influence on RF values in solid pulmonary nodules, producing false results when CT images with different slice thicknesses were used. Linear interpolation of the resampling method was limited because of the relatively small correction effect.

UNASSIGNED: Accurate delineation of targets and organs at risk (OAR) is required to ensure treatment efficacy and minimize risk of normal tissue toxicity with radiotherapy. Therefore, we evaluated the impacts of computed tomography (CT) slice thickness and reconstruction methods on the volume and dose evaluations of targets and OAR.
UNASSIGNED: Eleven CT datasets from patients with thoracic cancer were included. 3D images with a slice thickness of 2 mm (2-CT) were created automatically. Images of other slice thickness (4-CT, 6-CT, 8-CT, 10-CT) were reconstructed manually by the selected 2D images using two methods; internal tumor information and external CT Reference markers. Structures and plans on 2-CT images, as a reference data, were copied to the reconstructed images.
UNASSIGNED: The maximum error of volume was 84.6% for the smallest target in 10-CT, and the maximum error (≥20 cm3) was 10.1%, 14.8% for the two reconstruction methods, internal tumor information and external CT Reference, respectively. Changes in conformity index for a target of <20 cm3 were 5.4% and 17.5% in 8-CT. Changes on V30 and V40 of the heart were considerable. In the internal tumor information method, volumes of hearts decreased by 3.2% in 6-CT, while V30 and V40 increased by 18.4% and 46.6%.
UNASSIGNED: The image reconstruction method by internal tumor information was less affected by slice thickness than the image reconstruction method by external CT Reference markers. This study suggested that before positioning scanning, the largest section through the target should be determined and the optimal slice thickness should be estimated.

To determine the optimal slice thickness, playback rate, and scan time for uterine peristalsis with 3.0T magnetic resonance imaging (MRI).
In all, 23 young female volunteers underwent a 3.0T MRI scan with different slice thicknesses of 3 mm (Cine3mm ), 5 mm (Cine5mm ), and 7 mm (Cine7mm ) for 6 minutes. Subjective image quality score, signal-to-noise ratios (SNRs), and contrast-to-noise ratios (CNRs) of those MR images were evaluated by two radiologists independently. The number, intensity, and direction of uterine peristalsis with different thickness were compared at various playback rates. Also, the peristalsis frequency was counted and compared in different acquisition durations (1-6 minutes).
The subjective image quality score, peristalsis number, and intensity were significantly higher in Cine7mm and Cine5mm than Cine3mm (P < 0.05), while the SNRs and CNRs of Cine7mm were significantly higher than Cine3mm (P < 0.05). Peristalsis numbers did not differ significantly at different playback rates with the same slice thickness (P = 0.548-0.962). However, peristalsis intensity at 12×, and 15× was significantly greater than that at 8× the actual speed for Cine7mm and Cine5mm (P < 0.05). The peristalsis frequency at 3, 4, 5, 6 minutes was significantly higher than that at 1 minute and 2 minutes (P < 0.05).
We recommend a slice thickness of 5 mm or 7 mm and a scan time of 3 minutes for uterine peristalsis with 3.0T MRI, and a playback rate of 12× or 15× the actual speed for peristalsis observation. J. Magn. Reson. Imaging 2016;44:1397-1404.

BACKGROUND: Volumetric and whole-tumor attenuation assessment of tumor are of value in assessment of treatment.
OBJECTIVE: To assess the impact of slice thickness on semi-automatic analyses (volume, whole-tumor attenuation) for small colorectal hepatic metastases.
METHODS: Computed tomography (CT) data of patients with colorectal hepatic metastases at 1.5-, 3-, and 5-mm slice thickness were semi-automatically evaluated for volume and whole-tumor attenuation by two radiologists independently. Statistical analysis included paired samples t-test and concordance correlation coefficient (CCC) analysis according to the longest axial tumor diameter (10-20 mm, 20-30 mm, 30-40 mm).
RESULTS: A total of 62 patients (32 men and 30 women) with 62 target tumors were included. The mean volume was significantly higher at 3- and 5-mm slice thicknesses in comparison with the reference (1.5 mm) for the target tumors between 10 mm and 20 mm (P = 0.0295, CCC = 0.9394 for 3 mm; P = 0.0029, CCC = 0.5129 for 5 mm, respectively) and at 5 mm slice thickness for the target tumors between 20 mm and 30 mm (P = 0.0071, CCC = 0.9102). For whole-tumor attenuation measurements, the significant difference was only seen at 5-mm slice thicknesses in comparison with the reference (1.5 mm) for the target tumors between 10 and 20 mm (P = 0.0015, CCC = 0.9389).
CONCLUSIONS: Slice thickness of 1.5 mm might be suggested for semi-automated volumetric measurements, and slice thickness of no more than 3 mm for whole-tumor CT attenuation in hepatic metastasis between 10 mm and 20 mm.