Enhancing needle puncture detection using high-pass filtering and diffuse reflectance

Rachael L'Orsa ^1,2,3,4* Anupam Bisht ⁵ Linhui Yu ² Kartikeya Murari ^3,5,6 Garnette Sutherland ^3,4,7 David Westwick ⁶ Katherine J. Kuchenbecker ¹

• ¹ Department of Haptic Intelligence, Max Planck Institute for Intelligent Systems, Stuttgart, Germany
• ² Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, Canada
• ³ Hotchkiss Brain Institute, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
• ⁴ Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada
• ⁵ Department of Biomedical Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada
• ⁶ Department of Electrical and Software Engineering, Schulich School of Engineering, University of Calgary, Calgary, Alberta, Canada
• ⁷ Department of Clinical Neurosciences, Cumming School of Medicine, University of Calgary, Calgary, Alberta, Canada

Chest trauma or disease progression can lead to tension pneumothorax, a condition where mounting pressurization of the pleural cavity (the space between the chest wall and the lungs) leads rapidly to cardiac arrest. In pre-hospital settings, tension pneumothorax is treated by venting the pleural cavity via a needle introduced through the chest wall. Very high failure rates (up to 94.1%) have been reported for pre-hospital needle decompression, however, and the procedure can result in the accidental puncture of critical thoracic tissues because it is performed blind.Instrumented needles could help operators more reliably identify when the tool has entered the target space. This paper investigates technical approaches to provide such support; we created an experimental system that acquires needle force and position signals, as well as the diffuse backscattered reflectance from white light carried to and collected from the needle's tip via two in-bore optical fibers. Data collection occurred while two experimenters inserted a bevel-tipped percutaneous needle into an ex vivo porcine rib section simulating human chest anatomy. Four data-driven puncture-detection (DDPD) algorithms from the literature, which are appropriate for use with the variable tool velocities produced by manual insertions, were applied to the resulting data set offline. Grid search was performed across key signal-processing parameters, high-pass filters (HPFs) were applied to examine their impact on puncture detection, and a first exploration of multimodal (ensemble) methods was performed. Combining high-pass filters with DDPD methods resulted in a 2.7-fold improvement (from 8.2% to 21.9%) in the maximum overall precision (MOP) produced by force signals. Applying this HPF + DDPD scheme to reflectance data streams yielded a peak MOP of 36.4%, and combining reflectance with force generated the best MOP