Transmission-Based Monitoring of Dual-Applicator Microwave Ablation Discriminates Discontiguous from Contiguous Ablation Zones in an in Vivo Porcine Liver Model
Journal of Vascular and Interventional Radiology（2025）
Department of Electrical and Computer Engineering
Abstract
Purpose To determine the technical feasibility of discriminating discontiguous from contiguous ablation zones between a pair of microwave ablation (MWA) applicators using broadband microwave transmission signal measurements in an in vivo porcine liver model. Methods Dual applicator 2.45GHz MWA was performed using one directional and one omnidirectional applicator, spaced 3cm apart, under imaging guidance. The study involved 15 hepatic MWAs across four swine, with ablation durations of 200s (n=8) for discontiguous ablation and 600s (n=7), each at 60W; these ablation durations and applied power combinations were selected with the intent of creating discontiguous (200s) and contiguous (600s) ablation zones. A custom software periodically measured transmission signals between the applicators at 46-second intervals. Contrast-enhanced CT, gross pathology, and histopathologic analyses were used to assess the processed transmission signal (PTS). Results Statistical analyses revealed significant differences between contiguous and discontiguous ablation zones on CECT imaging (volume: 16.9±5.2cm3 vs. 3.9±1.5cm3, p=0.0002), gross tissue sections and histology (area: 10±3.3cm2 and 6.5±1.3cm2, p=0.001 and PTS datasets showed values of 85.1±11% and 37.3±12.9% (p=0.02). PTS values functioned well as predictors of complete vs incomplete ablation (area under curve of the receiver operating characteristic curve = 0.90), with a PTS threshold of 53% being optimal for indicating ablation zone contiguity.Ablation zone contiguity was strongly correlated with PTS (Spearman correlation coefficient 0.86, p<0.0001). Conclusion This study demonstrated that PTS between dual MWA applicators can distinguish between contiguous and discontiguous ablation zones.
MoreTranslated text
求助PDF
上传PDF
View via Publisher
AI Read Science
AI Summary
AI Summary is the key point extracted automatically understanding the full text of the paper, including the background, methods, results, conclusions, icons and other key content, so that you can get the outline of the paper at a glance.
Example
Background
Key content
Introduction
Methods
Results
Related work
Fund
Key content
- Pretraining has recently greatly promoted the development of natural language processing (NLP)
- We show that M6 outperforms the baselines in multimodal downstream tasks, and the large M6 with 10 parameters can reach a better performance
- We propose a method called M6 that is able to process information of multiple modalities and perform both single-modal and cross-modal understanding and generation
- The model is scaled to large model with 10 billion parameters with sophisticated deployment, and the 10 -parameter M6-large is the largest pretrained model in Chinese
- Experimental results show that our proposed M6 outperforms the baseline in a number of downstream tasks concerning both single modality and multiple modalities We will continue the pretraining of extremely large models by increasing data to explore the limit of its performance
Upload PDF to Generate Summary
Must-Reading Tree
Example
Generate MRT to find the research sequence of this paper
Data Disclaimer
The page data are from open Internet sources, cooperative publishers and automatic analysis results through AI technology. We do not make any commitments and guarantees for the validity, accuracy, correctness, reliability, completeness and timeliness of the page data. If you have any questions, please contact us by email: report@aminer.cn
Chat Paper
Summary is being generated by the instructions you defined