Purpose: This study aimed to evaluate the effects of various computed tomography (CT) acquisition parameters and metal artifacts on CT number measurement for CT thermometry during CT-guided thermal ablation. Methods: The effects of tube voltage (100-140 kVp), tube current (20-250 mAs), pitch (0.6-1.5) and gantry rotation time (0.5, 1.0 s) as well as metal artifacts from a radiofrequency ablation (RFA) needle on CT number were evaluated using liver tissue equivalent polyacrylamide (PAA) phantom. The correlation between CT number and temperature from 37 to 80 °C was studied on PAA phantom using optimum CT acquisition parameters. Results: No statistical significant difference (p > 0.05) was found on CT numbers under the variation of different acquisition parameters for the same temperature setting. On the other hand, the RFA needle has induced metal artifacts on the CT images of up to 8 mm. The CT numbers decreased linearly when the phantom temperature increased from 37 to 80 °C. A linear regression analysis on the CT numbers and temperature suggested that the CT thermal sensitivity was -0.521 ± 0.061 HU/°C (R2 = 0.998). Conclusion: CT thermometry is feasible for temperature assessment during RFA with the current CT technology, which produced a high CT number reproducibility and stable measurement at different CT acquisition parameters. Despite being affected by metal artifacts, the CT-based thermometry could be further developed as a tissue temperature monitoring tool during CT-guided thermal ablation.
Liver is the heaviest internal organ of the human body and performs many vital functions. Prolonged cirrhosis and fatty liver disease may lead to the formation of benign or malignant lesions in this organ, and an early and reliable evaluation of these conditions can improve treatment outcomes. Ultrasound imaging is a safe, non-invasive, and cost-effective way of diagnosing liver lesions. However, this technique has limited performance in determining the nature of the lesions. This study initiates a computer-aided diagnosis (CAD) system to aid radiologists in an objective and more reliable interpretation of ultrasound images of liver lesions. In this work, we have employed radon transform and bi-directional empirical mode decomposition (BEMD) to extract features from the focal liver lesions. After which, the extracted features were subjected to particle swarm optimization (PSO) technique for the selection of a set of optimized features for classification. Our automated CAD system can differentiate normal, malignant, and benign liver lesions using machine learning algorithms. It was trained using 78 normal, 26 benign and 36 malignant focal lesions of the liver. The accuracy, sensitivity, and specificity of lesion classification were 92.95%, 90.80%, and 97.44%, respectively. The proposed CAD system is fully automatic as no segmentation of region-of-interest (ROI) is required.