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Imani F.,Queen's University | Wu M.Z.,Queen's University | Lasso A.,Queen's University | Burdette E.C.,Acoustic MedSystems Incorporation | And 4 more authors.
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics) | Year: 2011

Purpose: This paper is the first report on the monitoring of tissue ablation using ultrasound RF echo time series. Methods: 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. Results: 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. Conclusions: 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. © 2011 Springer-Verlag.

Imani F.,Queen's University | Abolmaesumi P.,University of British Columbia | Wu M.Z.,Queen's University | Lasso A.,Queen's University | And 7 more authors.
IEEE Transactions on Biomedical Engineering | Year: 2013

This paper presents the results of a feasibility study to demonstrate the application of ultrasound RF time series imaging to accurately differentiate ablated and nonablated tissue. For 12 ex vivo and two in situ tissue samples, RF ultrasound signals are acquired prior to, and following, high-intensity ultrasound ablation. Spatial and temporal features of these signals are used to characterize ablated and nonablated tissue in a supervised-learning framework. In cross-validation evaluation, a subset of four features extracted from RF time series produce a classification accuracy of 84.5%, an area under ROC curve of 0.91 for ex vivo data, and an accuracy of 85% for in situ data. Ultrasound RF time series is a promising approach for characterizing ablated tissue. © 1964-2012 IEEE.

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