Mechanics of needle-tissue interaction

Roesthuis, Roy J.; Veen, Y. R. J. van; Jahya, Alex; Misra, Sarthak

doi:10.1109/iros.2011.6094969

Cited by 63 publications

(44 citation statements)

References 18 publications

(17 reference statements)

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“…1. The needle insertion device has two degrees of freedom: translation along and rotation about the insertion axis [15]. During the needle insertion, ultrasound-based tracking (Transducer 18L6HD, Siemens ACUSON S2000 ultrasound system, Siemens AG, Erlangen, Germany) is used to estimate the needle tip pose.…”

Section: Methodsmentioning

confidence: 99%

Needle steering in biological tissue using ultrasound-based online curvature estimation

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Patil

Alterovitz

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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Percutaneous needle insertions are commonly performed for diagnostic and therapeutic purposes. Accurate placement of the needle tip is important to the success of many needle procedures. The current needle steering systems depend on needle-tissue-specific data, such as maximum curvature, that is unavailable prior to an interventional procedure. In this paper, we present a novel three-dimensional adaptive steering method for flexible bevel-tipped needles that is capable of performing accurate tip placement without previous knowledge about needle curvature. The method steers the needle by integrating duty-cycled needle steering, online curvature estimation, ultrasound-based needle tracking, and sampling-based motion planning. The needle curvature estimation is performed online and used to adapt the path and duty cycling. We evaluated the method using experiments in a homogenous gelatin phantom, a two-layer gelatin phantom, and a biological tissue phantom composed of a gelatin layer and in vitro chicken tissue. In all experiments, virtual obstacles and targets move in order to represent the disturbances that might occur due to tissue deformation and physiological processes. The average targeting error using our new adaptive method is 40% lower than using the conventional non-adaptive duty-cycled needle steering method.

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Section: Methodsmentioning

confidence: 99%

Needle steering in biological tissue using ultrasound-based online curvature estimation

Moreira

Patil

Alterovitz

et al. 2014

2014 IEEE International Conference on Robotics and Automation (ICRA)

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“…These forces have been investigated by the authors in [11] and [15]. A mechanics-based model for predicting the in-plane deflection for a needle which undergoes multiple bends is presented by the authors in [12].…”

Section: B Mechanics-based Modelmentioning

confidence: 99%

“…A setup was designed for needle insertion into a soft tissue [15]. This setup has 2 DOFs: insertion of the needle along its longitudinal axis, and rotation about the same axis (see Fig.…”

Section: A Experimental Setupmentioning

confidence: 99%

“…Two charge coupled device (CCD) cameras are mounted at orthogonal angles to determine needle deflection in the xy-plane and xz-plane. A corner-detection algorithm is used to track the needle tip in the recorded videos after needle insertion [15].…”

Section: A Experimental Setupmentioning

confidence: 99%

“…Furthermore, the stiffness K 0 of the elastic foundation also needs to be known. The tip force was determined in a previous study for a φ 1 mm needle and in a gelatine phantom of the same composition [15]. The tip force was found to be 0.40 N. The distributed load is evaluated by fitting the model to experimental deflection data.…”

Section: B Needle Insertion Into Gelatine Phantomsmentioning

confidence: 99%

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Three-Dimensional Needle Shape Reconstruction Using an Array of Fiber Bragg Grating Sensors

Roesthuis

Kemp²,

Dobbelsteen

et al. 2014

IEEE/ASME Trans. Mechatron.

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227

151

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We present a prototype of a flexible nitinol needle (φ 1.0 mm and length 172 mm) integrated with an array of 12 Fiber Bragg Grating (FBG) sensors. These sensors measure the axial strain, which enables the computation of the needle curvature. We reconstruct the three-dimensional (3-D) needle shape from the curvature. Experiments are performed where the needle is deflected in free space. The maximum errors between the experiments and beam theory-based model are 0.20 mm (in-plane deflection with single bend), 0.51 mm (in-plane deflection with double bend), and 1.66 mm (out-of-plane). We also describe kinematics-based and mechanics-based models for predicting the 3-D needle shape during insertion into soft tissue. We perform experiments where the needle is inserted into a soft-tissue simulant, and the 3-D needle shape is reconstructed using the FBG sensors. We compare the reconstructed needle shape to deflection obtained from camera images and our models. The maximum error between the experiments and the camera images is 0.74 mm. The maximum errors between the kinematics-based and mechanics-based models and the camera images are 3.77 mm and 2.20 mm, respectively. This study demonstrates that deflection models and needles integrated with FBG sensors have the potential to be used in combination with clinical imaging modalities in order to enable accurate needle steering.

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A Machine Learning Approach to Predict Instrument Bending in Stereotactic Neurosurgery

Granados

Mancini

Vos

et al. 2018

Medical Image Computing and Computer Assisted Intervention – MICCAI 2018

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The accurate implantation of stereo-electroencephalography (SEEG) electrodes is crucial for localising the seizure onset zone in patients with refractory epilepsy. Electrode placement may differ from planning due to instrument deflection during surgical insertion. We present a regression-based model to predict instrument bending using image features extracted from structural and diffusion images. We compare three machine learning approaches: Random Forest, Feed-Forward Neural Network and Long Short-Term Memory on accuracy in predicting global instrument bending in the context of SEEG implantation. We segment electrodes from post-implantation CT scans and interpolate position at 1 mm intervals along the trajectory. Electrodes are modelled as elastic rods to quantify 3 degree-of-freedom (DOF) bending using Darboux vectors. We train our models to predict instrument bending from image features. We then iteratively infer instrument positions from the predicted bending. In 32 SEEG post-implantation cases we were able to predict trajectory position with a MAE of 0.49 mm using RF. Comparatively a FFNN had MAE of 0.71 mm and LSTM had a MAE of 0.93 mm.

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Mechanics of needle-tissue interaction

Cited by 63 publications

References 18 publications

Needle steering in biological tissue using ultrasound-based online curvature estimation

Needle steering in biological tissue using ultrasound-based online curvature estimation

Three-Dimensional Needle Shape Reconstruction Using an Array of Fiber Bragg Grating Sensors

A Machine Learning Approach to Predict Instrument Bending in Stereotactic Neurosurgery

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