Dr. Gaelle Fiard Profile

Dr. Gaelle Fiard

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Proceedings Article | 16 March 2020 Presentation + Paper

Efficient target tracking for 3D ultrasound-guided needle steering

Guillaume Lapouge, Gaelle Fiard, Philippe Poignet, Jocelyne Troccaz

Proceedings Volume 11315, 113150I (2020) https://doi.org/10.1117/12.2548321

KEYWORDS: 3D acquisition, Detection and tracking algorithms, Tissues, 3D image processing, Motion estimation, Ultrasonography, Diamond, Model-based design, Filtering (signal processing), Automatic tracking

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3D ultrasound imaging can be used in the context of robotic needle steering to reach a physical target with a flexible, steerable needle. During the insertion, the tissue may be deformed by the inserted needle, patient breathing or external force application. It may therefore be necessary to track intra-operatively the displacement of the target. Most ultrasound based needle steering works concentrate on 2D ultrasound probes, which do not allow to simultaneously track both the target and the needle during 3D needle steering. Physical target tracking in 3D ultrasound-guided needle steering is seldom carried out, and may require computational power that is precious for intra-operative needle steering. This paper proposes a new approach for computationally inexpensive and precise tracking of a moving target in 3D Bmode ultrasound volumes. It is based on the interconnection of intensity-based tracking and motion estimation algorithms. The intensity-based tracking consists in a 3D extension of the Diamond Shape block matching algorithm, used here for the first time in 3D ultrasound volumes for tissue tracking. The motion estimation is done by linear Kalman filtering. It predicts the next target position and ensures faster and more robust convergence of the Diamond Shape block matching algorithm. An experimental validation on ex-vivo tissue is proposed with promising tracking precision (estimated average error of 0.3mm) while significantly lowering the computational cost when compared to classical block matching based tracking.

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