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This study aimed to compare the vascular enhancement and radiation dose in preoperative transcatheter aortic valve implantation (TAVI) computed tomography (CT) with a reduced contrast medium (CM) using volume scans in 256-multidetector row CT (MDCT) with a standard CM using 64-MDCT. This study included 78 patients with preoperative TAVI CT with either 64- or 256-MDCT. The CM was injected at 1.5 mL/kg in the 64-MDCT group and 1.0 mL/kg in the 256-MDCT group. We compared vascular enhancement of the aortic root and access routes, image quality (IQ) scores, and radiation dose in both groups. Despite the reduced CM (by 33 %) in the 256-MDCT group, the mean vascular enhancement of the right and left subclavian arteries was significantly higher than that in the 64-MDCT group [284 and 267 Hounsfield units (HU) vs. 376 and 359 HU; p < 0.05]; however, no significant differences in the mean vascular enhancement in the ascending aorta, abdominal aorta at the celiac level, and bilateral common femoral arteries were observed between the two groups (p > 0.05 for all). The median IQ scores at the aortic root were higher in the 256-MDCT group than in the 64-MDCT group (3 vs. 4; p < 0.05), and those at the femoral access routes were comparable (4 vs. 4; p = 0.33). The mean effective dose was significantly reduced by 30 % in the 256-MDCT group (23.6 vs. 16.3 mSv; p < 0.05). In preoperative TAVI CT, volume scans using 256-MDCT provide comparable or better vascular enhancement and IQ with a 30 % reduction in CM and radiation dose than those using 64-MDCT. Volume scan using 256-MDCT for preoperative TAVI CT may reduce CM and radiation dose in TAVI patients with renal dysfunction. [ABSTRACT FROM AUTHOR]

Dual-energy computed tomography (DECT) can generate virtual non-contrast (VNC) images. Herein, we sought to improve the accuracy of VNC images by identifying the optimal slope of contrast media (SCM) for VNC-image generation based on the iodine concentration and subject's body size. We used DECT to scan a multi-energy phantom including four iodine concentration rods (15, 10, 5, and 2 mg/mL), and 240 VNC images (eight SCM ranging from 0.49 to 0.56 × three body sizes × ten scans) that were generated by three-material decomposition. The CT number of each iodine and solid water rod part was measured in each VNC image. The difference in the CT number between the iodine and the solid water rod part was calculated and compared using paired t -test or repeated measures ANOVA. The SCM that achieved an absolute value of the difference in CT number of <5.0 Hounsfield units (HU) for all body sizes simultaneously was greater at lower iodine concentration (SCM of 0.5, 0.51, and 0.53 at 10, 5, and 2 mg/mL iodine, respectively). At an iodine concentration of 15 mg/mL, no SCM achieved an absolute difference of <5.0 HU in CT number for all body sizes simultaneously. At all iodine concentrations, the SCM achieving the minimal difference in the CT number increased with the increase in body size. By adjusting the SCM according to the iodine concentration and body size, it is possible to generate VNC images with an accuracy of <5.0 HU. Improving the accuracy of VNC images minimizing incomplete iodine subtraction would make it possible to replace true non-contrast (TNC) images with VNC images and reduce the radiation dose. [ABSTRACT FROM AUTHOR]

This study aims to predict endoleak after endovascular aneurysm repair (EVAR) using machine learning (ML) integration of patient characteristics, stent-graft configuration, and a selection of vessel lengths, diameters and angles measured using pre-operative computed tomography angiography (CTA). We evaluated 1-year follow-up CT scans (arterial and delayed phases) in patients who underwent EVAR for the presence or absence of an endoleak. We also obtained data on the patient characteristics, stent-graft selection, and preoperative CT vessel morphology (diameter, length, and angle). The extreme gradient boosting (XGBoost) for the ML system was trained on 30 patients with endoleaks and 81 patients without. We evaluated 5217 items in 111 patients with abdominal aortic aneurysms, including the patient characteristics, stent-graft configuration and vascular morphology acquired using pre-EVAR abdominal CTA. We calculated the area under the curve (AUC) of our receiver operating characteristic analysis using the ML method. The AUC, accuracy, 95% confidence interval (CI), sensitivity, and specificity were 0.88, 0.88, 0.79–0.97, 0.85, and 0.91 for ML applying XGBoost, respectively. The diagnostic performance of the ML method was useful when factors such as the patient characteristics, stent-graft configuration and vessel length, diameter and angle of the vessels were considered from pre-EVAR CTA. Based on our findings, we suggest that this is a potential application of ML for the interpretation of abdominal CTA scans in patients with abdominal aortic aneurysms scheduled for EVAR. [ABSTRACT FROM AUTHOR]

The purpose of this study was to demonstrate that dose reduction does not compromise image quality when combining high helical pitch (HP) and the ECG-Edit function during low HP retrospectively gated computed tomography angiography (CTA). This study made use of a pulsating cardiac phantom (ALPHA 1 VTPC). The heart rate (HR) of the cardiac phantom was changed in five intervals, every 5 beats per minute (bpm), from 40 to 60 bpm. Evaluation of a range of HR was important because data loss might occur when combining a low HR and high HP. We performed retrospectively gated CTA scans five times using a low HP (0.16) and high HP (0.24), for each of the five HR intervals, using a 64-detector row CT scanner. The CT volume dose index (CTDI vol) was recorded from the CT console of each scan. For the images with data loss, data were repaired using the ECG-Edit function. We compared the CTDI vol , estimated cardiac phantom volume, and the visualization of the coronary ladder phantom between HP 0.16, with or without repaired HP 0.24, using the ECG-Edit function. Data loss occurred with a HR of 40 bpm and 45 bpm when using HP 0.24. The CTDI vol was reduced by approximately 33% with HP 0.24 when compared with HP 0.16. There were no significant differences in the mean cardiac motion phantom volume and visualization scores between HP 0.16 and with and without repaired HP 0.24 using the ECG-Edit function (p < 0.05). The ECG-Edit function is potential useful for repairing the lost data in patients with a low HR, and when combined with a high HP, it is possible to reduce the radiation dose by approximately 33%. The ECG-Edit function and high HP may be a viable option in pediatric CTA studies. [ABSTRACT FROM AUTHOR]

To investigate how changing the injection duration at cardiac computed tomography angiography (CCTA) affects contrast enhancement in newborns and infants. Included were 142 newborns and infants with confirmed congenital heart disease who underwent CCTA between January 2015 and December 2018. In group 1 (n = 71 patients), the injection duration was 8 s; in group 2 (n = 71) it was 16 s. Our findings were assessed by one-to-one matching analysis to estimate the propensity score of each patient. We compare the CT number for the pulmonary artery (PA), ascending aorta (AAO), left superior vena cava (SVC), AAO and PA enhancement ratio, and the scores for visualization between the two groups. In group 1, median CT number and ranges was 345 (211–591) HU in the AAO, 324 (213–567) HU in the PA, and 62 (1–70) HU in the SVC. These values were 465 (308–669) HU, 467 (295–638) HU, and 234 (67–443) HU, respectively, in group 2 (p < 0.05). The median score for volume-rendering visualization on 3D images of the CCTA was 2 in group 1 and 3 in group 2; the score for visualization of the left SVC of the maximum intensity projection images was 2 in group 1 and 3 in group 2 (p < 0.05). The CT number for the AAO and PA enhancement ratio was 15.2 in group 1 and 9.2 in group 2 (p < 0.05). The 16-sec injection protocol yielded significantly higher CT numbers for the AAO, PA, and the SVC than the 8-sec injection protocol; the visualization scores were also significantly higher in group 2. In newborns and infants, the longer injection time for CCTA yields stable and higher contrast enhancement at identical CM concentrations. [ABSTRACT FROM AUTHOR]

This study aimed to compare the correlation between the computed tomography (CT) enhancement rate of the venous to portal venous phase (VP-ER) and the extracellular volume (ECV) fraction with shear-wave ultrasound elastography (USE) findings in patients with liver fibrosis. We included 450 patients with clinically suspected liver cirrhosis who underwent triphasic dynamic CT studies and USE. We compared the USE results with the unenhanced CT phase, with enhancement in the hepatic artery phase (HAP), portal venous phase (PVP), and venous phase (VP), and with the ECV fraction and the VP-ER. We also compared the area under the curve (AUC) of the receiver operating characteristic (ROC) curve of the ECV fraction and VP-ER with that of the values obtained with USE. The VP-ER was the most highly correlated with the liver stiffness value determined with USE (Pearson's correlation coefficient: r = 0.37), followed by enhancement in the PVP (r = −0.25), CT number on unenhanced CT scans (r = −0.22), the ECV fraction (r = 0.19), enhancement in the VP (r = 0.059), and enhancement in the HAP (r = −0.023) (all p < 0.01). The VP-ER showed a significantly higher AUC than the ECV fraction (0.75 vs 0.62) when the liver stiffness was >15 kPa in USE studies (p = 0.04). Compared to the ECV fraction, the VP-ER is more useful for predicting all degrees of liver fibrosis on routine triphasic dynamic CT images. Although improvement is needed, the VP-ER has a higher diagnostic ability for liver fibrosis than the ECV fraction in clinical practice. [ABSTRACT FROM AUTHOR]

To compare the computed tomography (CT) number for paediatric cardiac computed tomography angiography (CCTA) and visualisation score of the three-dimensional (3D) images using the conventional T-shaped extended tube (T-tube) and spiral flow-generating extended tube (spiral-tube) connected between the contrast injector and cannula. In total, 108 patients suspected to have congenital heart disease (CHD) were considered for inclusion. We utilised the T-tube for intravenous contrast and spiral-tube in 54 patients each. Observers individually inspected randomized volume rendering images of the internal thoracic artery, each acquired from the with or without spiral-tube groups, using a four-point scale. We compared the mean CT number of the ascending aorta (AAO) and pulmonary artery (PA), contrast noise ratio (CNR), CT number for the AAO and PA enhancement ratio, and the visualisation scores between the groups. There were no significant differences in patient characteristics between the with or without spiral-tube groups (p > 0.05). The mean CT number ±standard deviation for the AAO and PA, and the CNR without or with spiral-tube groups were 441.2 ± 89.2 and 489.8 ± 86.1 HU for the AAO, 436.3 ± 100.6 and 475.3 ± 85.2 HU for the PA, and 9.5 ± 2.2 and 10.8 ± 2.4 for the CNR, respectively (p < 0.05). In the spiral-tube group, the CT number, CNR, and visualisations score of the 3D images were significantly higher for the AAO and PA than those in the T-tube group (p < 0.05). The spiral-tube proved to be beneficial in improving the CT number for the AAO and PA, CNR, and visualisation score compared with the conventional T-tube during paediatric CCTA. The spiral-tube may allow the visualisation of smaller blood vessels than those visualised by the conventional T-tube for paediatric patients in CCTA. [ABSTRACT FROM AUTHOR]

The aim of this study was to generate virtual Magnetic resonance (MR) from computed tomography (CT) using conditional generative adversarial networks (cGAN). We selected examinations from 22 adults who obtained their CT and MR lumbar spine examinations. Overall, 4 examinations were used as test data, and 18 examinations were used as training data. A cGAN was trained to generate virtual MR images from the CT images using the corresponding MR images as targets. After training, the generated virtual MR images from test data in epochs 1, 10, 50, 100, 500, and 1000 were compared with the original ones using the mean square error (MSE) and structural similarity index (SSIM). Additionally, two radiologists also performed qualitative assessments. The MSE of the virtual MR images decreased as the epoch of the cGANs increased from the original CT images: 8876.7 ± 1192.9 (original CT), 1567.5 ± 433.9 (Epoch 1), 1242.4 ± 442.0 (Epoch 10), 1065.8 ± 478.1 (Epoch 50), 1276.1 ± 718.9 (Epoch 100), 1046.7 ± 488.2 (Epoch 500), and 1031.7 ± 400.0 (Epoch 1000). No considerable differences were observed in the qualitative evaluation between the virtual MR images and the original ones, except in the structure of the spinal canal. Virtual MR lumbar spine images using cGANs could be a feasible technique to generate near-MR images from CT without MR examinations for evaluation of the vertebral body and intervertebral disc. Virtual MR lumbar spine images using cGANs can offer virtual CT images with sufficient quality for attenuation correction for PET or dose planning in radiotherapy. [ABSTRACT FROM AUTHOR]

Deep learning approaches have shown high diagnostic performance in image classifications, such as differentiation of malignant tumors and calcified coronary plaque. However, it is unknown whether deep learning is useful for characterizing coronary plaques without the presence of calcification using coronary computed tomography angiography (CCTA). The purpose of this study was to compare the diagnostic performance of deep learning with a convolutional neural network (CNN) with that of radiologists in the estimation of coronary plaques. We retrospectively enrolled 178 patients (191 coronary plaques) who had undergone CCTA and integrated backscatter intravascular ultrasonography (IB-IVUS) studies. IB-IVUS diagnosed 81 fibrous and 110 fatty or fibro-fatty plaques. We manually captured vascular short-axis images of the coronary plaques as Portable Network Graphics (PNG) images (150 × 150 pixels). The display window level and width were 100 and 700 Hounsfield units (HU), respectively. The deep-learning system (CNN; GoogleNet Inception v3) was trained on 153 plaques; its performance was tested on 38 plaques. The area under the curve (AUC) obtained by receiver operating characteristic analysis of the deep learning system and by two board-certified radiologists was compared. With the CNN, the AUC and the 95% confidence interval were 0.83 and 0.69–0.96, respectively; for radiologist 1 they were 0.61 and 0.42–0.80; for radiologist 2 they were 0.68 and 0.51–0.86, respectively. The AUC for CNN was significantly higher than for radiologists 1 (p = 0.04); for radiologist 2 it was not significantly different (p = 0.22). DL-CNN performed comparably to radiologists for discrimination between fatty and fibro-fatty plaque on CCTA images. The diagnostic performance of the CNN and of two radiologists in the assessment of 191 ROIs on CT images of coronary plaques whose type corresponded with their IB-IVUS characterization was comparable. [ABSTRACT FROM AUTHOR]

We compared the diagnostic performance of morphological methods such as the major axis, the minor axis, the volume and sphericity and of machine learning with texture analysis in the identification of lymph node metastasis in patients with thyroid cancer who had undergone contrast-enhanced CT studies. We sampled 772 lymph nodes with histology defined tissue types (84 metastatic and 688 benign lymph nodes) that were visualised on CT images of 117 patients. A support vector machine (SVM), free programming software (Python), and the scikit-learn machine learning library were used to discriminate metastatic-from benign lymph nodes. We assessed 96 texture and 4 morphological features (major axis, minor axis, volume, sphericity) that were reported useful for the differentiation between metastatic and benign lymph nodes on CT images. The area under the curve (AUC) obtained by receiver operating characteristic analysis of univariate logistic regression and SVM classifiers were calculated for the training and testing datasets. The AUC for all classifiers in training and testing datasets was 0.96 and 0.86, at the SVM for machine learning. When we applied conventional methods to the training and testing datasets, the AUCs were 0.63 and 0.48 for the major axis, 0.70 and 0.44 for the minor axis, 0.66 and 0.43 for the volume, and 0.69 and 0.54 for sphericity, respectively. The SVM using texture features yielded significantly higher AUCs than univariate logistic regression models using morphological features (p = 0.001). For the identification of metastatic lymph nodes from thyroid cancer on contrast-enhanced CT images, machine learning combined with texture analysis was superior to conventional diagnostic methods with the morphological parameters. Our findings suggest that in patients with thyroid cancer and suspected lymph node metastasis who undergo contrast-enhanced CT studies, machine learning using texture analysis is high diagnostic value for the identification of metastatic lymph nodes. [ABSTRACT FROM AUTHOR]

With intra-arterial digital subtraction angiography (DSA) considered as the gold standard, we compared the diagnostic value of computed tomography angiography (CTA) and computed tomography-digital subtraction angiography (CT-DSA in hemodialysis (HD) patients suspected of having lower limb peripheral artery disease (PAD). In this retrospective study, we enrolled 220 HD patients with suspected PAD. CT-DSA images were obtained by subtracting unenhanced images from enhanced images. The research team calculated the area under the curve (AUC), sensitivity, specificity, positive and negative predictive value (PPV, NPV), and recorded the diagnostic accuracy between the CTA and CT-DSA images using the DSA as gold standard. Visual evaluation of calcifications in the peripheral arteries were also compared between CTA and CT-DSA images. At the above-knee level, the CTA AUC [95% confidence interval (CI)] was 0.68 (CI 0.64–0.72), sensitivity and specificity were 60 and 81%, PPV and NPV were 85 and 53%, and accuracy was 67%. Below the knee, these values were 0.66 (CI 0.62–0.70), 71 and 79%, 79 and 47%, and 66%. For CT-DSA, above-knee, the AUC [95% CI] was 0.88 (CI 0.85–0.91), sensitivity and specificity were 84 and 92%, PPV and NPV were 89 and 97%, and accuracy was 93%. Below the knee, these values were 0.95 (CI 0.93–0.97), 95 and 93%, 96 and 83%, and 93%. The scores for the visualization of calcification in the peripheral arteries was significantly higher for CT-DSA than CTA (p < 0.05). CT-DSA helps to assess stenotic PAD with high calcification in the lower extremities of HD patients. On CT-DSA images, the severity of vascular calcification can be assessed for HD patients suspected of PAD of the lower extremities. [ABSTRACT FROM AUTHOR]

To compare the radiation dose, diagnostic accuracy, and the resultant ablation procedures using 80 and 120-kVp cardiac computed tomography angiography (CCTA) protocols with the same contrast-to-noise ratio in patients scheduled for atrial fibrillation (AF) ablation. This retrospective study was performed following institutional review board approval. We divided 140 consecutive patients who had undergone CCTA using a 64-MDCT scanner into two equal groups. Standard deviation (SD) of the CT number was set at 25 Hounsfield units (HU) for the 120-kVp protocol. To facilitate a reduction in radiation dose it was set at 40 HU for the 80 kVp protocol. We compared the two protocols with respect to the radiation dose, the diagnostic accuracy for detecting left atrial appendage (LAA) thrombi, matching for surface registration, and the resultant ablation procedures. At 120 kVp, the dose length product (DLP) was 2.2 times that at 80 kVp (1269.0 vs 559.0 mGy cm, p < 0.01). The diagnostic accuracy for thrombus detection was 100% using both protocols. There was no difference between the two protocols with respect to matching for surface registration. The protocols did not differ with respect to the subsequent time required for the ablation procedures and the ablation fluoroscopy time, and the radiation dose (p = 0.54, 0.33, and 0.32, respectively). For the same CNR, the DLP at 80 kVp (559.0 mGy cm) was 56% of that delivered at 120 kVp (1269.0 mGy cm). There was no reduction in diagnostic accuracy. Maintaining CNR allows for a reduction in the radiation dose without reducing the image quality. [ABSTRACT FROM AUTHOR]