A framework for predicting three-dimensional prostate deformation in real time

Background Surgical simulation systems can be used to estimate soft tissue deformation during pre- and intra-operative planning. Such systems require a model that can accurately predict the deformation in real time. In this study, we present a back-propagation neural network for predicting three-dimensional (3D) deformation of a phantom that incorporates the anatomy of the male pelvic region, i.e. the prostate and surrounding structures that support it. Method In the experiments and simulations, a needle guide is used to deform the rectal wall. The neural network predicts the deformation based on the relation between the undeformed and deformed shapes of the phantom. Training data are generated using a validated finite element (FE) model of the prostate and its surrounding structures. The FE model is developed from anatomically accurate magnetic resonance (MR) images. An ultrasound-based acoustic radiation force impulse imaging technique is used to measure in situ the shear wave velocity in soft tissue. The velocity is utilized to calculate the elasticities of the phantom. In the simulation study, the displacement and angle of the needle guide are varied. The neural network then predicts 3D phantom deformation for a given input displacement. Results The results of the simulation study show that the maximum absolute linear and angular errors of the nodal displacement and orientation between neural network and FE predicted deformation are 0.03 mm and 0.01, respectively. Conclusions This study shows that a back-propagation neural network can be used to predict prostate deformation. Further, it is also demonstrated that a combination of ultrasound data, MR images and a neural network can be used as a framework for accurately predicting 3D prostate deformation in real tim