Atlas-based method for model updating in image-guided liver surgery

Similar to the well documented brain shift experienced during neurosurgical procedures, intra-operative soft tissue deformation in open hepatic resections is the primary source of error in current image-guided liver surgery (IGLS) systems. The use of bio-mechanical models has shown promise in providing the link between the deformed, intra-operative patient anatomy and the pre-operative image data. More specifically, the current protocol for deformation compensation in IGLS involves the determination of displacements via registration of intra-operatively acquired sparse data and subsequent use of the displacements to drive solution of a linear elastic model via the finite element method (FEM). However, direct solution of the model during the surgical procedure has several logistical limitations including computational time and the ability to accurately prescribe boundary conditions and material properties. Recently, approaches utilizing an atlas of pre-operatively computed model solutions based on a priori information concerning the surgical loading conditions have been proposed as a more realistic avenue for intra-operative deformation compensation. Similar to previous work, we propose the use of a simple linear inverse model to match the intra-operatively acquired data to the pre-operatively computed atlas. Additionally, an iterative approach is implemented whereby point correspondence is updated during the matching process, being that the correspondence between intra-operative data and the pre-operatively computed atlas is not explicitly known in liver applications. Preliminary validation experiments of the proposed algorithm were performed using both simulation and phantom data. The proposed method provided comparable results in the phantom experiments with those obtained using the traditional incremental FEM approach.