Preoperative trajectory planning for percutaneous procedures in deformable environments

Abstract

In image-guided percutaneous interventions, a precise planning of the needle path is a key factor to a successful intervention. In this paper we propose a novel method for computing a patient-specific optimal path for such interventions, accounting for both the deformation of the needle and soft tissues due to the insertion of the needle in the body. To achieve this objective, we propose an optimization method for estimating preoperatively a curved trajectory allowing to reach a target even in the case of tissue motion and needle bending. Needle insertions are simulated and regarded as evaluations of the objective function by the iterative planning process. In order to test the planning algorithm, it is coupled with a fast needle insertion simulation involving a flexible needle model and soft tissue finite element modeling, and experimented on the use-case of thermal ablation of liver tumors. Our algorithm has been successfully tested on twelve datasets of patient-specific geometries. Fast convergence to the actual optimal solution has been shown. This method is designed to be adapted to a wide range of percutaneous interventions.

Real-Time Error Control for Surgical Simulation

“…Needle-based percutaneous procedures are an important part of modern clinical interventions such as biopsy, brachytherapy, cryotherapy or regional anesthesia. The success of these procedures depends on good training and careful planning to optimize the path to the target, while avoiding critical structures [15]. In some instances the procedure can also be assisted by robotic devices.…”

“…In some instances the procedure can also be assisted by robotic devices. Unfortunately, natural tissue motion (due to breathing, for instance), and deformation (due to needle insertion) generally lead to incorrect or inefficient planning [15]. To address these issues, one must rely on an accurate simulation of needle insertion.…”

“…In this strategy, only those elements, where error (see (15)) is higher than certain threshold, are refined. Let η M = max e η e , where η e is defined in (15). We mark an element for refinement if η e ≥ θη M with 0 < θ < 1.…”

“…However, formulating the problem as (24) leads to a non positive definite global matrix, which makes the system challenging to solve. An alternative approach, as proposed in [15], [17], is to solve the interaction problem in three steps: predictive motion (no interaction constraints), constraint solving, and corrective motion. However, this alternative requires the computation of matrix inverse A −1 1 and A −1 2 .…”

Inverse real-time Finite Element simulation for robotic control of flexible needle insertion in deformable tissues

“…Real-time computations are enabled avoiding expensive re-meshing operations when the needle is inserted. [12] extended the method for optimal trajectory planning for liver surgeries. The method takes into account deformations (breathing, needle deformation, friction) to avoid the obstacles.…”