Multi-body statistical shape representation of anatomy for navigation in robot-assisted laparoscopic partial nephrectomy

Abstract

Image guidance for abdominal procedures requires an anatomical model capable of representing significant displacement and deformation of relevant tissues in a computationally efficient manner. This work evaluates the suitability of statistical shape modeling to represent key structures in Robot Assisted Laparoscopic Partial Nephrectomy (RALPN) both individually, and also as a multi-body composite model. Tomography obtained from subjects in an ongoing RALPN study was used to produce surface model representations of the kidneys, abdominal aorta, and inferior vena cava. Each structure was resliced and remeshed in a standardized fashion to allow for extraction of the principal modes of variation. Reduced parameter representations of the example structures based on the strongest eigenmodes indicate that <5mm average RMSE modeling accuracy can be achieved with four parameters for the individual models and eight parameters for the four-body composite model. The magnitude of centroid displacements observed under the principal modes of variation is consistent with literature-reported values, suggesting that this approach may be suitable for image guidance in RALPN.