{"id":2475,"date":"2024-05-03T20:24:10","date_gmt":"2024-05-03T20:24:10","guid":{"rendered":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/mark-wu\/"},"modified":"2024-05-03T20:24:10","modified_gmt":"2024-05-03T20:24:10","slug":"mark-wu","status":"publish","type":"qsc_member","link":"https:\/\/labs.cs.queensu.ca\/perklab\/members\/mark-wu\/","title":{"rendered":"Mark Wu"},"content":{"rendered":"
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Mark Wu<\/h1>\n<\/div>\n
Masters Student<\/div>\n
School of Computing<\/div>\n
Queen’s University<\/div>\n
Member from 2010<\/em> to 2011<\/em><\/div>\n
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Imani, F; Abolmaesumi, Purang; Wu, Mark; Lasso, Andras; Burdette, E. Clif; Ghoshal, G; Heffter, Tamas; Williams, E; Neubauer, P; Fichtinger, Gabor; Mousavi, Parvin<\/p>

Ultrasound-guided Characterization of Interstitial Ablated Tissue Using RF Time Series: Feasibility Study.<\/a> Journal Article<\/span> <\/p>

In: <\/span>IEEE transactions on bio-medical engineering, <\/span>2013<\/span>.<\/p>

Abstract<\/a><\/span> | Links<\/a><\/span> | BibTeX<\/a><\/span><\/p>

@article{Imani2013,
\r\ntitle = {Ultrasound-guided Characterization of Interstitial Ablated Tissue Using RF Time Series: Feasibility Study.},
\r\nauthor = {F Imani and Purang Abolmaesumi and Mark Wu and Andras Lasso and E. Clif Burdette and G Ghoshal and Tamas Heffter and E Williams and P Neubauer and Gabor Fichtinger and Parvin Mousavi},
\r\nurl = {http:\/\/www.ncbi.nlm.nih.gov\/pubmed\/23335657
\r\nhttps:\/\/labs.cs.queensu.ca\/perklab\/wp-content\/uploads\/sites\/3\/2024\/02\/Imani2013.pdf},
\r\nyear  = {2013},
\r\ndate = {2013-01-01},
\r\nurldate = {2013-01-01},
\r\njournal = {IEEE transactions on bio-medical engineering},
\r\nabstract = {<p>This paper presents the results of a feasibility study to demonstrate the application of ultrasound RF time series imaging to accurately differentiate ablated and non-ablated tissue. Methods: 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 non-ablated tissue in a supervised-learning framework. Results: In crossvalidation evaluation, a subset of four features extracted from RF time series produce a classification accuracy of 84.5%, and an area under ROC curve of 0.91 for ex vivo data, and an accuracy of 85% for in situ data. Conclusion: Ultrasound RF time series is a promising approach for characterizing ablated tissue.<\/p>},
\r\nkeywords = {},
\r\npubstate = {published},
\r\ntppubtype = {article}
\r\n}
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<p>This paper presents the results of a feasibility study to demonstrate the application of ultrasound RF time series imaging to accurately differentiate ablated and non-ablated tissue. Methods: 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 non-ablated tissue in a supervised-learning framework. Results: In crossvalidation evaluation, a subset of four features extracted from RF time series produce a classification accuracy of 84.5%, and an area under ROC curve of 0.91 for ex vivo data, and an accuracy of 85% for in situ data. Conclusion: Ultrasound RF time series is a promising approach for characterizing ablated tissue.<\/p><\/div>
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