Kalman Filter Based Data Fusion for Needle Deflection Estimation Using Optical-EM Sensor

In many clinical procedures involving needle insertion, such as cryoablation, accurate navigation of the needle to the desired target is of paramount importance to optimize the treatment and minimize the damage to the neighboring anatomy. However, the force interaction between the needle and tissue may lead to needle deflection, resulting in considerable error in the intraoperative tracking of the needle tip. In this paper, we have proposed a Kalman filter-based formulation to fuse two sensor data — optical sensor at the base and magnetic resonance (MR) gradient-field driven electromagnetic (EM) sensor placed 10 cm from the needle tip — to estimate the needle deflection online. Angular springs model based tip estimations and EM based estimation without model are used to form the measurement vector in the Kalman filter. Static tip bending experiments show that the fusion method can reduce the error of the tip estimation by from 29.23 mm to 3.15 mm and from 39.96 mm to 6.90 mm at the MRI isocenter and 650 mm from the isocenter respectively.