• DocumentCode
    12604
  • Title

    Ultrasound-Guided Characterization of Interstitial Ablated Tissue Using RF Time Series: Feasibility Study

  • Author

    Imani, Farhad ; Abolmaesumi, P. ; Wu, M.Z. ; Lasso, A. ; Burdette, E.C. ; Ghoshal, Goutam ; Heffter, Tamas ; Williams, Evan ; Neubauer, P. ; Fichtinger, Gabor ; Mousavi, Parvin

  • Author_Institution
    Dept. of Electr. & Comput. Eng., Queen´s Univ., Kingston, ON, Canada
  • Volume
    60
  • Issue
    6
  • fYear
    2013
  • fDate
    Jun-13
  • Firstpage
    1608
  • Lastpage
    1618
  • Abstract
    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.
  • Keywords
    biomedical ultrasonics; learning (artificial intelligence); medical image processing; sensitivity analysis; time series; ultrasonic imaging; ROC curve; high intensity ultrasound ablation; interstitial ablated tissue; spatial features; supervised learning framework; temporal features; ultrasound RF time series; ultrasound guided characterization; Accuracy; Feature extraction; Radio frequency; Time series analysis; Transducers; Tumors; Ultrasonic imaging; Characterization of ablated tissue region; tissue ablation; ultrasound RF time series; Animals; Chickens; Feasibility Studies; High-Intensity Focused Ultrasound Ablation; Image Processing, Computer-Assisted; Liver; Models, Biological; Muscle, Skeletal; Radio Waves; Signal Processing, Computer-Assisted; Swine;
  • fLanguage
    English
  • Journal_Title
    Biomedical Engineering, IEEE Transactions on
  • Publisher
    ieee
  • ISSN
    0018-9294
  • Type

    jour

  • DOI
    10.1109/TBME.2013.2240300
  • Filename
    6412777