Passive Model-based Error Compensation For Beveled-tip Needle Deflection

The interaction between the asymmetric (bevel) tip needle and the surrounding tissue results in the deflection of the needle and causes a significant targeting error in prostate biopsy. Several works have been proposed to mitigate this issue. While some have shown promising results, they require complex software and hardware which makes them difficult to deploy for clinical use. In this paper, we present a predictive model-based approach for passive compensation of the bevel tip needles in phantom tissues. We predict the needle deflection by approximating the initial deflection angle and simulating the needle path before insertion. The entry point is then modified based on the predicted deflection. To achieve this, we collected a set of needle insertion data into a gelatin phantom in an MRI study and used the data to find the parameters for the predictive model. The model was then tested in another MRI insertion study, which demonstrated promising results with an average of 75.2% targeting accuracy improvement compared with the uncompensated insertions.