Simplified adaptive path planning for percutaneous needle insertions

Needle placement errors can mitigate the effectiveness of the diagnosis or the therapy, sometimes with catastrophic outcomes. Previous design of a simplified model for needle deflection estimation was motivated by the clinical constraints of ARCS (Abdomino-pelvic Robotic-driven slightly flexible needle insertion performed in CT/MRI-guided Scenario). We present in this work, the validation results for the needle deflection prediction model. Its robustness is evaluated under an unknown context such as a different robotic platform, facing uncertainties conditions not conceived previously in the model´s confection. In addition, the work presents the development and validation experiments of an adaptive path planner that uses the model as predictor´s strategy. It provides pre-operative planning assistance, as well as intra-operative decision-making support. The experiments results showed average error around 1mm for the pre-operative planning and the intra-operative replanning approach showed to be very robust to correct the initial predictions, showing average error smaller than 1 mm.