Modelling Prostate Gland Motion for Image-Guided Interventions

A direct approach to using finite element analysis (FEA) to predict organ motion typically requires accurate estimates of soft-tissue properties, which are very difficult to measure and are known to vary significantly between patients. In this paper, we describe a method that combines FEA with a statistical approach to overcome these problems. We show how a patientspecific, statistical motion model (SMM) of the prostate gland can be generated from FE simulations and used to predict the displacement field over the whole gland and constrain a deformable surface registration algorithm. Using 3D transrectal ultrasound images of the prostates of five patients, acquired before and after expanding the balloon covering the ultrasound probe, the mean target registration error calculated for anatomical landmarks within the gland was 1.9mm.