Semi-Automated Needle Steering in Biological Tissue Using an Ultrasound-Based Deflection Predictor

Abstract

The performance of needle-based interventions depends on the accuracy of needle tip positioning. Here, a novel needle steering strategy is proposed that enhances accuracy of needle steering. In our approach the surgeon is in charge of needle insertion to ensure the safety of operation, while the needle tip bevel location is robotically controlled to minimize the targeting error. The system has two main components: (1) a real-time predictor for estimating future needle deflection as it is steered inside soft tissue, and (2) an online motion planner that calculates control decisions and steers the needle toward the target by iterative optimization of the needle deflection predictions. The predictor uses the ultrasound-based curvature information to estimate the needle deflection. Given the specification of anatomical obstacles and a target from preoperative images, the motion planner uses the deflection predictions to estimate control actions, i.e., the depth(s) at which the needle should be rotated to reach the target. Ex-vivo needle insertions are performed with and without obstacle to validate our approach. The results demonstrate the needle steering strategy guides the needle to the targets with a maximum error of 1.22 mm.

Geometric control of 3D needle steering in soft-tissue

“…Large uncertainties in the value of κ can increase number of needle retractions, which is undesirable. In practice, needle curvature can be measured on the fly by means of an online estimator [7,13] or model-based nonlinear observer [5]. One can also relax the constant curvature path assumption by employing a high-level controller that controls the needle curvature through duty-cycling [12,15].…”

“…As we will show later, the selected closed path simulates a retraction/insertion motion and is expected to keep the insertion depth (i.e., x) fixed. In the remainder of this paper, the geometric phase given by (7) and the normal form of the system in (4) will be used to design a needle steering controller…”

“…As f 2 → 0, needle insertion depth error x − x e goes to ± k 0,2 − θ e . From (7) we can obtain θ e = asin(x e κ) at the desired insertion depth x e . Selecting k 0,2 = θ e , guarantees the needle insertion depth error goes to zero.…”

“…Different paths can be selected for other initial conditions. The proposed paths and the corresponding base coordinates calculated by solving (7) in the closed form are shown in Fig. 3 and in Table. 1, respectively.…”

“…The needle steering system is a nonlinear constrained system and most of the system states, namely, needle tip position and orientation, cannot be directly measured from 2D imaging systems. Thus, most of the presented needle steering strategies can only steer the needle in 2D [6,7,12]. Several 3D needle steering algorithms are developed by incorporating image-based algorithms for calculating the needle pose in 2D ultrasound images and consequently estimating unicycle model parameters [15].…”