9,358 research outputs found
Needle Tip Force Estimation using an OCT Fiber and a Fused convGRU-CNN Architecture
Needle insertion is common during minimally invasive interventions such as
biopsy or brachytherapy. During soft tissue needle insertion, forces acting at
the needle tip cause tissue deformation and needle deflection. Accurate needle
tip force measurement provides information on needle-tissue interaction and
helps detecting and compensating potential misplacement. For this purpose we
introduce an image-based needle tip force estimation method using an optical
fiber imaging the deformation of an epoxy layer below the needle tip over time.
For calibration and force estimation, we introduce a novel deep learning-based
fused convolutional GRU-CNN model which effectively exploits the
spatio-temporal data structure. The needle is easy to manufacture and our model
achieves a mean absolute error of 1.76 +- 1.5 mN with a cross-correlation
coefficient of 0.9996, clearly outperforming other methods. We test needles
with different materials to demonstrate that the approach can be adapted for
different sensitivities and force ranges. Furthermore, we validate our approach
in an ex-vivo prostate needle insertion scenario.Comment: Accepted for Publication at MICCAI 201
Autonomous Tissue Scanning under Free-Form Motion for Intraoperative Tissue Characterisation
In Minimally Invasive Surgery (MIS), tissue scanning with imaging probes is
required for subsurface visualisation to characterise the state of the tissue.
However, scanning of large tissue surfaces in the presence of deformation is a
challenging task for the surgeon. Recently, robot-assisted local tissue
scanning has been investigated for motion stabilisation of imaging probes to
facilitate the capturing of good quality images and reduce the surgeon's
cognitive load. Nonetheless, these approaches require the tissue surface to be
static or deform with periodic motion. To eliminate these assumptions, we
propose a visual servoing framework for autonomous tissue scanning, able to
deal with free-form tissue deformation. The 3D structure of the surgical scene
is recovered and a feature-based method is proposed to estimate the motion of
the tissue in real-time. A desired scanning trajectory is manually defined on a
reference frame and continuously updated using projective geometry to follow
the tissue motion and control the movement of the robotic arm. The advantage of
the proposed method is that it does not require the learning of the tissue
motion prior to scanning and can deal with free-form deformation. We deployed
this framework on the da Vinci surgical robot using the da Vinci Research Kit
(dVRK) for Ultrasound tissue scanning. Since the framework does not rely on
information from the Ultrasound data, it can be easily extended to other
probe-based imaging modalities.Comment: 7 pages, 5 figures, ICRA 202
Real-time Error Control for Surgical Simulation
Objective: To present the first real-time a posteriori error-driven adaptive
finite element approach for real-time simulation and to demonstrate the method
on a needle insertion problem. Methods: We use corotational elasticity and a
frictional needle/tissue interaction model. The problem is solved using finite
elements within SOFA. The refinement strategy relies upon a hexahedron-based
finite element method, combined with a posteriori error estimation driven local
-refinement, for simulating soft tissue deformation. Results: We control the
local and global error level in the mechanical fields (e.g. displacement or
stresses) during the simulation. We show the convergence of the algorithm on
academic examples, and demonstrate its practical usability on a percutaneous
procedure involving needle insertion in a liver. For the latter case, we
compare the force displacement curves obtained from the proposed adaptive
algorithm with that obtained from a uniform refinement approach. Conclusions:
Error control guarantees that a tolerable error level is not exceeded during
the simulations. Local mesh refinement accelerates simulations. Significance:
Our work provides a first step to discriminate between discretization error and
modeling error by providing a robust quantification of discretization error
during simulations.Comment: 12 pages, 16 figures, change of the title, submitted to IEEE TBM
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