Objective: In this study, we introduce a multi-modal sensing and feedback framework aimed at assisting clinicians during endovascular surgeries and catheterization procedures. This framework utilizes state-of-the-art imaging and sensing sub-systems to produce a 3D visualization of an endovascular catheter and surrounding vasculature without the need for intra-operative X-rays. Methods: The catheterization experiments within this study are conducted inside a porcine limb undergoing motions. A hybrid position-force controller of a robotically-actuated ultrasound (US) transducer for uneven porcine tissue surfaces is introduced. The tissue, vasculature, and catheter are visualized by integrated real-time US images, 3D surface imaging, and Fiber Bragg Grating (FBG) sensors. Results: During externally-induced limb motions, the vasculature and catheter can be reliably reconstructed at mean accuracies of 1.9 +/- 0.3 mm and 0.82 +/- 0.21 mm, respectively. Conclusions: The conventional use of intra-operative X-ray imaging to visualize instruments and vasculature in the human body can be reduced by employing improved diagnostic technologies that do not operate via ionizing radiation or nephrotoxic contrast agents. Significance: The presented multi-modal framework enables the radiation-free and accurate reconstruction of significant tissues and instruments involved in catheterization procedures.


    Zugriff

    Download


    Exportieren, teilen und zitieren



    Titel :

    Real-Time Multi-Modal Sensing and Feedback for Catheterization in Porcine Tissue


    Beteiligte:

    Erscheinungsdatum :

    2021-01-03


    Anmerkungen:

    Heunis , C M , Suligoj , F , Fambuena Santos , C & Misra , S 2021 , ' Real-Time Multi-Modal Sensing and Feedback for Catheterization in Porcine Tissue ' , Sensors , vol. 21 , no. 1 , 273 . https://doi.org/10.3390/s21010273



    Medientyp :

    Aufsatz (Zeitschrift)


    Format :

    Elektronische Ressource


    Sprache :

    Englisch



    Klassifikation :

    DDC:    629



    MULTI-MODAL SENSING TRANSDUCERS

    LEE CHENG / LU CHEE | Europäisches Patentamt | 2018

    Freier Zugriff

    MULTI-MODAL SENSING TRANSDUCERS

    LEE CHENG SEONG / LU CHEE WAI | Europäisches Patentamt | 2021

    Freier Zugriff



    Automatic Geographic Enrichment by Multi-modal Bike Sensing

    Verstockt, Steven / Slavkovikj, Viktor / Potter, Pieterjan De et al. | Tema Archiv | 2014