Shape Manipulation of Bevel-Tip Needles for Prostate Biopsy Procedures: A Comparison of Two Resolved-Rate Controllers

TL;DR

The paper addresses imprecise needle tip placement in MRI/US-guided prostate biopsies by comparing two resolved-rate controllers for bevel-tip needle steering using template-based shape manipulation. It contrasts a data-driven Jacobian update based on Broyden's method with a mechanics-based Jacobian obtained via finite-difference on a physics-informed simulator, evaluated in a simulated 12-core biopsy with tissue-parameter uncertainty. The mechanics-based controller maintains target reach under $\pm 50\%$ parameter variation, while the data-driven approach requires signal regulation to avoid excessive tissue compression when only final targets are given. The results emphasize that providing a feasible needle path improves safety and performance for both controllers, with the mechanics-based method offering more predictable behavior under uncertainty.

Abstract

Prostate cancer diagnosis continues to encounter challenges, often due to imprecise needle placement in standard biopsies. Several control strategies have been developed to compensate for needle tip prediction inaccuracies, however none were compared against each other, and it is unclear whether any of them can be safely and universally applied in clinical settings. This paper compares the performance of two resolved-rate controllers, derived from a mechanics-based and a data-driven approach, for bevel-tip needle control using needle shape manipulation through a template. We demonstrate for a simulated 12-core biopsy procedure under model parameter uncertainty that the mechanics-based controller can better reach desired targets when only the final goal configuration is presented even with uncertainty on model parameters estimation, and that providing a feasible needle path is crucial in ensuring safe surgical outcomes when either controller is used for needle shape manipulation.

Figures (5)

• Figure 1: Schematic of bevel-tip needle insertion into multi-layered soft tissues. The global frame is placed at needle entry point on skin surface. Needle base translates along the $x$- and $y$-axis with a fixed slope; the needle template moves independently along the $y$-axis with an unconstrained slope. Bevel effect can be achieved by placing contact points (blue crosses) to drive the needle tip in the direction of the bevel ($-y$ in this case). Final needle shape is achieved through a sequence of base and template motions ① through ③.
• Figure 2: (a) Prostate phantom with internal organs. (b) Selected 12-core biopsy target points in prostate phantom. Direction $z$ is flattened for current planar insertion scenario.
• Figure 3: Needle tip and control input trajectories of path following task for Target 9. The $\pm 50\%$ represents parametric variations for the mechanics-based controller.
• Figure 4: Needle tip and control input trajectories of point stabilization task for Target 9. The $\pm 50\%$ represents parametric variations for the mechanics-based controller. Red brackets emphasize the needle's "pivoting" behavior due to excessive tissue compression.
• Figure 5: Target 9 needle shapes comparison against the desired needle shape. Data-driven controller is used for path following (Fig. \ref{['fig:T9_path']} top) and point stabilization (Fig. \ref{['fig:T9_pt']}).