[Comparison of Lower Extremity Computed Tomography Angiography by Using Different Subtraction Techniques].

PURPOSE: To compare the diagnostic capabilities of orbital synchronized helical scanning at lower extremity computed tomography angiography between the Add/Sub software and the deformable image registration. METHODS: From March 2015 to December 2016, 100 dialysis patients underwent orbital synchronized lower limb CT subtraction angiography and lower limb endovascular treatment within 4 months. For the visual evaluation of blood vessels in the lower extremities, a stenosis rate of 50% or more was considered to be stenosis. It was classified into two areas: above-knee (AK) region (superficial femoral artery and popliteal artery) and below-knee (BK) region (anterior tibial artery, posterior tibial artery, and fibula artery). Considering angiography for the lower limb endovascular treatment as the golden standard, we calculated the sensitivity, specificity, positive-predictive value, negative-predictive value, and diagnostic capabilities. Receiver operating characteristic curve (ROC) analysis was performed to calculate the area under curve (AUC). RESULTS: Calcification subtraction failure was observed to be 11% in the AK region and 2% in the BK region using the Add/Sub software. The specificity, positive-predictive value, diagnostic capabilities, and AUC of the deformable image registration were lower than those of the Add/Sub software. CONCLUSIONS: Add/Sub software and deformable image registration have high diagnostic capability to remove calcification. On the other hand, the specificity and AUC of the deformable image registration were lower than those of the Add/Sub software. Also, even if the same deformable image registration is used, caution is required because the diagnostic performance varies depending on the site.

Does the Tube Voltage Affect the Characterization of Coronary Plaques on 100- and 120-kVp Computed Tomography Scans.

OBJECTIVE: The aim of this study was to compare the diagnostic performance of 100- and 120-kVp coronary computed tomography (CT) angiography (CCTA) scans for the identification of coronary plaque components. METHODS: We included 116 patients with coronary plaques who underwent CCTA and integrated backscatter intravascular ultrasound studies. On 100-kVp scans, we observed 24 fibrous and 24 fatty/fibrofatty plaques; on 120-kVp scans, we noted 27 fibrous and 41 fatty/fibrofatty plaques. We compared the fibrous and the fatty/fibrofatty plaques, the CT number of the coronary lumen, and the radiation dose on scans obtained at 100 and 120 kVp. We also compared the area under the receiver operating characteristic (ROC) curve of the coronary plaques on 100- and 120-kVp scans with their ROC curves on integrated backscatter intravascular ultrasound images. RESULTS: The mean CT numbers of fatty and fatty/fibrofatty plaques were 5.71 ± 36.5 and 76.6 ± 33.7 Hounsfield units (HU), respectively, on 100-kVp scans; on 120-kVp scans, they were 13.9 ± 29.4 and 54.5 ± 22.3 HU, respectively. The CT number of the coronary lumen was 323.1 ± 81.2 HU, and the radiation dose was 563.7 ± 81.2 mGy-cm on 100-kVp scans; these values were 279.3 ± 61.8 HU and 819.1 ± 115.1 mGy-cm on 120-kVp scans. The results of ROC curve analysis identified 30.5 HU as the optimal diagnostic cutoff value for 100-kVp scans (area under the curve = 0.93, 95% confidence interval = 0.87-0.99, sensitivity = 95.8%, specificity = 78.9%); for 120-kVp plaque images, the optimal cutoff was 37.4 HU (area under the curve = 0.87, 95% confidence interval = 0.79-0.96, sensitivity = 82.1%, specificity = 85.7%). CONCLUSIONS: For the discrimination of coronary plaque components, the diagnostic performance of 100- and 120-kVp CCTA scans is comparable.

Machine-learning integration of CT histogram analysis to evaluate the composition of atherosclerotic plaques: Validation with IB-IVUS.

BACKGROUND: To determine whether machine learning with histogram analysis of coronary CT angiography (CCTA) yields higher diagnostic performance for coronary plaque characterization than the conventional cut-off method using the median CT number. METHODS: We included 78 patients with 78 coronary plaques who had undergone CCTA and integrated backscatter intravascular ultrasound (IB-IVUS) studies. IB-IVUS diagnosed 32 as fibrous- and 46 as fatty or fibro-fatty plaques. We recorded the coronary CT number and 7 histogram parameters (minimum and mean value, standard deviation (SD), maximum value, skewness, kurtosis, and entropy) of the plaque CT number. We also evaluated the importance of each feature using the Gini index which rates the importance of individual features. For calculations we used XGBoost. Using 5-fold cross validation of the plaque CT number, the area under the receiver operating characteristic curve of the machine learning- (extreme gradient boosting) and the conventional cut-off method was compared. RESULTS: The median CT number was 56.38 Hounsfield units (HU, 8.00-95.90) for fibrous- and 1.15 HU (-35.8-113.30) for fatty- or fibro-fatty plaques. The calculated optimal threshold for the plaque CT number was 36.1 ±â€¯2.8 HU. The highest Gini index was the coronary CT number (0.19) followed by the minimum value (0.17), kurtosis (0.17), entropy (0.14), skewness (0.11), the mean value (0.11), the standard deviation (0.06), and the maximum value (0.05), and energy (0.00). By validation analysis, the machine learning-yielded a significantly higher area under the curve than the conventional method (area under the curve 0.92 and 95%, confidence interval 0.86-0.92 vs 0.83 and 0.75-0.92, p = 0.001). CONCLUSION: The machine learning-was superior the conventional cut-off method for coronary plaque characterization using the plaque CT number on CCTA images.