We have presented a novel approach for unifying planning and control of steerable needles in 3D environments with obstacles and real-world perturbations. Our approach relies on a fast RRT motion planner for steerable needles that uses variable curvature kinematics and a novel distance measure for planning that speeds up motion planning to the point that it can be done in real time with typical needle insertion velocities. We use the fast performance to enable us to consider two clinically motivated optimization criteria: minimizing insertion length and maximizing clearance from critical anatomical structures. Our approach accounts for perturbations as they occur, thus eliminating
0 2 4 6 8 10 12 14 16 18 20
Scene #2 Scene #1
Targeting Error (mm)
Targeting Error (Porcine Loin)
Rapid Replanning Shortest Path Open-loop Shortest Path Rapid Replanning Maximum Clearance Open-loop Maximum Clearance
Figure 2.11: We compare the targeting error using closed-loop, rapid replanning steering and open- loop execution for the two proposed optimization criteria in ex vivo porcine loin tissue. Our approach significantly outperforms open-loop execution. Error bars indicate one standard deviation of the targeting error over repeated trials. Detailed experimental results are available in Tab. 2.1.
(a) Hepatic veins model (b) Hepatic veins model (c) Experimental setup
(d) Plans computed at time step 1 (e) CT Reconstructed needle path
Figure 2.12: We applied our needle steering system with rapid replanning to a scenario motivated by the clinical task of ablating a tumor in the liver while avoiding the hepatic veins. (a) We constructed an anthropomorphic liver phantom that includes the major hepatic veins in the liver (right) based on an anatomical model provided by Desser et al. (Fig. 1 in (Desser et al., 2003)). The model was built to scale to match human liver dimensions and is shown next to a geometrically correct human liver model manufactured based on segmented CT images of a human patient. (b) We placed the model in a container that was filled with Sim-Test material to create the liver phantom for experiments. The hepatic vein model placed in a box along with fiducial markers for registration before it was cast in Sim-Test phantom tissue material. (c) We used a portable flat-panel CT scanner to obtain preoperative images of the environment while the electromagnetic tracking system provided measurements of the position and orientation of the needle tip during the procedure. (d) We specified the insertion location and target region and annotated segmented structures such as veins that needed to be avoided. We illustrate feasible motion plans (shown in green) computed at time step1. (e) Via rapid replanning, our planner successfully guided the needle (reconstructed from CT scans after the procedure) between the middle and left hepatic veins to reach the target on the surface of the tumor.
the need for modeling complex phenomena such as needle and tissue deformation, needle-tissue interaction, and torsional build-up along the needle shaft. Our approach also eliminates the need for designing complex feedback controllers that try to guide the needle tip along a pre-planned trajectory
We also presented the first fully integrated, automated needle steering system that is capable of avoiding obstacles in 3D environments with real-world perturbations. We experimentally evaluated our system by performing procedures in tissue phantoms and ex vivo porcine loin tissue. In our anatomic liver scenario, we took steps toward demonstrating how this system could be used for clinical steerable needle procedures. Our experimental results demonstrate that our rapid re-planning strategy successfully guides the needle to desired targets while avoiding obstacles with an average error of less than 3 mm, which is within clinically acceptable thresholds and better than the accuracy achieved by trained clinicians. In addition to accuracy, our system offers the added advantage of automatically avoiding sensitive structures.
This research is another step towards realizing needle steering in actual clinical practice. There are several avenues for improving this work. First, tissue damage due to duty cycled spinning of the needle is a clinically-relevant concern. Previous studies in phantom tissues (Reed et al., 2011) and in vivo tissues (Engh et al., 2010) showed that duty cycled spinning can leave corkscrew trails within the cut tissue. Recently, Swaney et al. (Swaney et al., 2012) showed that flexible joints can be used to connect the bevel tip to minimize tissue damage during duty cycled spinning of the needle. Second, it is possible to use ideas from Patil et al. (Patil et al., 2011) to plan in deformable environments instead of quasi-static environments to increase the probability of successful plan execution in scenarios with very large deformations. Third, we assume that the measurements of the state of the needle tip obtained from the magnetic tracking system are accurate. Since the measurement noise (Northern Digital Inc., 2012) is very low when compared to the errors in the needle tip motion, this is not a a major concern. However, there is always a possibility of improving the quality of localization of the needle tip and improve targeting accuracy by using a Kalman filter for state estimation (Kallem et al., 2010; van den Berg et al., 2010).
Further, we plan to investigate methods to avoid detailed characterization of the needle in tissue by estimating the curvature of the needle and estimate points on the calibration curve as the procedure is being performed. Finally, we would like to evaluate our approach in in vivo tissues where accurate needle characterization cannot always be performed and there are other sources of uncertainty such as involuntary patient motions and unforeseen needle tip deflections due to tissue membranes.
CHAPTER 3
Data-Driven Stochastic Models For Simulating
Steerable Needle Procedures
In this chapter, we present a data-driven stochastic model of steerable needle insertion for simulating steerable needle procedures. We describe the need for a stochastic model of steerable needle insertion and the challenges associated with constructing such a model. In this work, we consider a model that incorporates a stochastic motion model of the needle tip pose and a stochastic measurement model of the partial and possibly noisy measurements of the needle tip pose. We describe an expectation maximization (EM) algorithm for estimating the parameters of the stochastic model from data gathered from experiments and prior procedures. We validate the stochastic model by comparing the targeting error achieved in simulated steerable needle procedures using our stochastic model vis-a-vis targeting errors achieved in needle procedures performed in tissue phantoms and ex vivo porcine loin tissue.