A framework for predictive modeling of anatomical deformations

A framework for modeling and predicting anatomical deformations is presented, and tested on simulated images. Although a variety of deformations can be modeled in this framework, emphasis is placed on surgical planning, and particularly on modeling and predicting changes of anatomy between preoperative and intraoperative positions, as well as on deformations induced by tumor growth. Two methods are examined. The first is purely shape-based and utilizes the principal modes of co-variation between anatomy and deformation in order to statistically represent deformability. When a patient's anatomy is available, it is used in conjunction with the statistical model to predict the way in which the anatomy will/can deform. The second method is related, and it uses the statistical model in conjunction with a biomechanical model of anatomical deformation. It examines the principal modes of co-variation between shape and forces, with the latter driving the biomechanical model, and thus predicting deformation. Results are shown on simulated images, demonstrating that systematic deformations, such as those resulting from change in position or from tumor growth, can be estimated very well using these models. Estimation accuracy will depend on the application, and particularly on how systematic a deformation of interest is.

I. Introduction

Modeling anatomical deformations is becoming increasingly important in image-guided surgery. Anatomical deformations might be due to diverse factors such as changes in patient positioning, needle puncturing, skull opening, tumor growth, or natural bone growth after reconstructive surgery. Many of these deformations can be predicted to a large extent by statistical or biomechanical deformable models. The former require that the deformation of interest can be observed in a number of cases, which are treated as training samples from which a statistical predictive model is built. The latter are based on knowledge of the biomechanical behavior of biological tissues.