Xiejing Li

Accurate and real-time monitoring of thermal ablation areas is an important guarantee for the safety and effectiveness of ablation treatment. Ultrasonic B-mode imaging can realize real-time monitoring, however it can not accurately correspond to the thermal ablation area. Contrast-enhanced ultrasound (CEUS) is clinically used for evaluating thermal ablation treatment with high accuracy, but hard to achieve real-time monitoring and guidance during ablation treatment. In this study, we explored the performance of horizontally normalized Shannon entropy (hNSE) in detecting and monitoring radiofrequency ablation on in vivo rabbit livers with CEUS images as in-situ references. The mean contrast-to-noise ratios (CNRs) of hNSE images was 3.8, being significantly higher than 1.2 of B-mode images (p<0.001). Larger distinction in probability distribution between values of ablated tissue and normal tissue was found in hNSE images than B-mode images. The area under receiver operating characteristic curve of hNSE and B-mode were 0.7760 and 0.6463 (p<0.0001). Additionally, better performance in area and axis length delineation of ablated tissue was found in hNSE, comparable to CEUS references with high correlation coefficients of 0.823, 0.814, and 0.792, respectively. In conclusion, these findings suggest that hNSE imaging could be a promising technique for real-time monitoring and guidance applied to ultrasound-guided thermal ablation treatment in clinical use.

Many quantitative ultrasound imaging techniques based on tissue properties have been established to ensure the safety and efficacy of thermal ablation treatment. However, which quantitative ultrasound imaging technique has superior monitoring properties has not yet been revealed. In this study, we investigated and compared the performance of envelope data distribution-dependent ultrasonic Nakagami, envelope data distribution-independent horizontally normalized Shannon entropy (hNSE), and differential attenuation coefficient intercept (DACI) based on frequency domain imaging technique during liver thermal ablation treatments. Quantitative ultrasound parameter images were constructed simultaneously from radiofrequency (RF) data collected during the process of microwave ablation (MWA) treatments in ex vivo and in vivo porcine livers. Contrast-to-noise ratios (CNR) were calculated to evaluate the contrast resolution of different imaging techniques. The mean CNR value of hNSE images was 6.06 ± 2.34 dB, being significantly higher than that of Nakagami images ( p <0.05, statistical) and DACI images ( p <0.05, statistical). Furthermore, the receiver operating characteristic (ROC) was used to evaluate the thermal lesion detection ability. The area under the curve (AUC) of B-mode, Nakagami, hNSE and DACI was 0.823, 0.839, 0.86 and 0.825, respectively. Additionally, our data analysis exhibited that hNSE has the excellent property of ablation area detections in the present study, whose correlation coefficient is 0.91. In conclusion, our study suggests that hNSE imaging, which does not require the specific data distribution has the best performance in thermal ablation detections and is a promising imaging method for further clinical research and application.

Ultrasonic B-mode imaging offers non-invasive and real-time monitoring of thermal ablation treatment in clinical use, however it faces challenges of moderate lesion-normal contrast and detection accuracy. Quantitative ultrasound imaging techniques have been proposed as promising tools to evaluate the microstructure of ablated tissue. In this study, we introduced Shannon entropy, a non-model based statistical measurement of disorder, to quantitatively detect and monitor microwave-induced ablation in porcine livers. Performance of typical Shannon entropy (TSE), weighted Shannon entropy (WSE), and horizontally normalized Shannon entropy (hNSE) were explored and compared with conventional B-mode imaging. TSE estimated from non-normalized probability distribution histograms was found to have insufficient discernibility of different disorder of data. WSE that improves from TSE by adding signal amplitudes as weights obtained area under receiver operating characteristic (AUROC) curve of 0.895, whereas it underestimated the periphery of lesion region. hNSE provided superior ablated area prediction with the correlation coefficient of 0.90 against ground truth, AUROC of 0.868, and remarkable lesion-normal contrast with contrast-to-noise ratio of 5.86 which was significantly higher than other imaging methods. Data distributions shown in horizontally normalized probability distribution histograms indicated that the disorder of backscattered envelope signal from ablated region increased as treatment went on. These findings suggest that hNSE imaging could be a promising technique to assist ultrasound guided percutaneous thermal ablation.