Modelling Prostate Gland Motion for Image-Guided Interventions
A direct approach to using finite element analysis (FEA) to predict organ motion typically requires accurate boundary conditions, which can be difficult to measure during surgical interventions, and accurate estimates of soft-tissue properties, which 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 patient-specific, statistical motion model (SMM) of the prostate gland, generated from FE simulations, can be used to predict the displacement field over the whole gland given sparse surface displacements. The method was validated 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.
KeywordsBiomechanical modelling finite element analysis image-guided interventions prostate cancer ultrasound statistical shape modelling
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