Name
Abstract | ||
|---|---|---|
This paper is the first report on the monitoring of tissue ablation using ultrasound RF echo time series.We calcuate frequency and time domain features of time series of RF echoes from stationary tissue and transducer, and correlate them with ablated and non-ablated tissue properties.We combine these features in a nonlinear classification framework and demonstrate up to 99% classification accuracy in distinguishing ablated and non-ablated regions of tissue, in areas as small as 12mm2 in size. We also demonstrate significant improvement of ablated tissue classification using RF time series compared to the conventional approach of using single RF scan lines.The results of this study suggest RF echo time series as a promising approach for monitoring ablation, and capturing the changes in the tissue microstructure as a result of heat-induced necrosis. |
Year | DOI | Venue |
|---|---|---|
2011 | 10.1007/978-3-642-23623-5_48 | MICCAI |
Keywords | Field | DocType |
rf echo,tissue ablation,single rf,stationary tissue,rf echo time series,ultrasound rf data,tissue microstructure,ablated tissue classification,time series,rf time series,non-ablated tissue property | Time domain,Biomedical engineering,Transducer,Computer vision,Computer science,Ablation,Nonlinear classification,Artificial intelligence,Pathology,Scan line,Ultrasound | Conference |
Volume | Issue | ISSN |
14 | Pt 1 | 0302-9743 |
Citations | PageRank | References |
1 | 0.46 | 3 |
Authors | ||
8 | ||
Name | Order | Citations | PageRank |
|---|---|---|---|
| Farhad Imani | 1 | 53 | 7.40 |
| Mark Z Wu | 2 | 5 | 1.02 |
| Andras Lasso | 3 | 106 | 31.99 |
| Everett C Burdette | 4 | 60 | 7.78 |
| Mohammad I. Daoud | 5 | 118 | 13.89 |
| Gabor Fitchinger | 6 | 1 | 0.46 |
| Purang Abolmaesumi | 7 | 951 | 111.52 |
| Parvin Mousavi | 8 | 366 | 56.95 |