Baichuan Jiang1, Wenpeng Gao2, Daniel Kacher3, Erez Nevo4, Barry Fetics4, Thomas C Lee5, Jagadeesan Jayender3. 1. School of Mechanical Engineering, Tianjin University, Tianjin, 300072, China. baichuan@tju.edu.cn. 2. School of Life Science and Technology, Harbin Institute of Technology, Harbin, 150001, China. 3. Department of Radiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA. 4. Robin Medical Inc., Baltimore, MD, 21203, USA. 5. Department of Neuroradiology, Brigham and Women's Hospital, Harvard Medical School, Boston, MA, 02115, USA.
Abstract
PURPOSE: In many clinical procedures such as cryoablation that involves needle insertion, accurate placement of the needle's tip at the desired target is the major issue for optimizing the treatment and minimizing damage to the neighboring anatomy. However, due to the interaction force between the needle and tissue, considerable error in intraoperative tracking of the needle tip can be observed as needle deflects. METHODS: In this paper, measurements data from an optical sensor at the needle base and a magnetic resonance (MR) gradient field-driven electromagnetic (EM) sensor placed 10 cm from the needle tip are used within a model-integrated Kalman filter-based sensor fusion scheme. Bending model-based estimations and EM-based direct estimation are used as the measurement vectors in the Kalman filter, thus establishing an online estimation approach. RESULTS: Static tip bending experiments show that the fusion method can reduce the mean error of the tip position estimation from 29.23 mm of the optical sensor-based approach to 3.15 mm of the fusion-based approach and from 39.96 to 6.90 mm, at the MRI isocenter and the MRI entrance, respectively. CONCLUSION: This work established a novel sensor fusion scheme that incorporates model information, which enables real-time tracking of needle deflection with MRI compatibility, in a free-hand operating setup.
PURPOSE: In many clinical procedures such as cryoablation that involves needle insertion, accurate placement of the needle's tip at the desired target is the major issue for optimizing the treatment and minimizing damage to the neighboring anatomy. However, due to the interaction force between the needle and tissue, considerable error in intraoperative tracking of the needle tip can be observed as needle deflects. METHODS: In this paper, measurements data from an optical sensor at the needle base and a magnetic resonance (MR) gradient field-driven electromagnetic (EM) sensor placed 10 cm from the needle tip are used within a model-integrated Kalman filter-based sensor fusion scheme. Bending model-based estimations and EM-based direct estimation are used as the measurement vectors in the Kalman filter, thus establishing an online estimation approach. RESULTS: Static tip bending experiments show that the fusion method can reduce the mean error of the tip position estimation from 29.23 mm of the optical sensor-based approach to 3.15 mm of the fusion-based approach and from 39.96 to 6.90 mm, at the MRI isocenter and the MRI entrance, respectively. CONCLUSION: This work established a novel sensor fusion scheme that incorporates model information, which enables real-time tracking of needle deflection with MRI compatibility, in a free-hand operating setup.
Authors: Farshid Alambeigi; Sahba Aghajani Pedram; Jason L Speyer; Jacob Rosen; Iulian Iordachita; Russell H Taylor; Mehran Armand Journal: IEEE Trans Robot Date: 2019-10-29 Impact factor: 5.567