An Interpolation Method Based on Generalized Regression Neural Network for Ultrasonic 3D Reconstruction

In robot-assisted surgery projects researchers should be able to make fast 3D reconstruction. Usually 2D images acquired with common diagnostic equipments such as UT, CT and MRI are not enough and complete for an accurate 3D reconstruction. There are some interpolation methods for approximating non value voxels which consume large execution time. We introduce a novel algorithm based on generalized regression neural network (GRNN) which can interpolate unknown volxes fast and reliable. The GRNN interpolation is used to produce new 2D images between each two succeeding ultrasonic images. It is shown that the composition of GRNN with image distance transformation can produce higher quality 3D shapes. The results of this method are compared with other interpolation methods practically. It shows this method can decrease overall time consumption and conserve the quality on online 3D reconstruction