PANCREATIC TUMOR GROWTH PREDICTION WITH ELASTIC-GROWTH DECOMPOSITION, IMAGE-DERIVED MOTION, AND FDM-FEM COUPLING

Abstract

Pancreatic neuroendocrine tumors are abnormal growths of hormone-producing cells in the pancreas. Unlike the brain which is protected by the skull, the pancreas can be significantly deformed by its surrounding organs. Consequently, the tumor shape difference observable from images at different time points arises from both tumor growth and pancreatic motion, and tumor growth model personalization may be compromised if such motion is ignored. Therefore, we incorporate pancreatic motion information derived from deformable image registration in model personalization. For more accurate mechanical interactions between tumor growth and pancreatic motion, elastic-growth decomposition is used with a hyperelastic constitutive law to model the mass effect, which allows growth modelling while conserving the mechanical properties. Furthermore, a way of coupling the finite difference method and the finite element method is proposed to greatly reduce the computation time. With both 2-[18F]-fluoro-2-deoxy- D-glucose positron emission tomographic and contrast-enhanced computed tomographic images, functional, structural, and motion data are combined for a patient-specific model. Experiments on synthetic and clinical data show the importance of image-derived motion on estimating pathophysiologically plausible mechanical properties and the promising performance of our framework. From seven patient data sets, the recall, precision, Dice coefficient, relative volume difference, and average surface distance between the personalized tumor growth simulations and the measurements were 83.2_8.8%, 86.9_8.3%, 84.4_4.0%, 13.9_9.8%, and 0.6_0.1 mm, respectively.

Existing System

Finite difference method (FDM)

Proposed System

We have proposed a pancreatic tumor growth prediction framework with elastic-growth decomposition, image-derived motion, and FDM-FEM coupling. With the elastic-growth decomposition, the growth and elastic parts can be modelled separately. This avoids compromising the realism of the models. Using the FDM-FEM coupling, the simulation and thus personalization time can be greatly reduced. Experiments on synthetic and clinical data showed that, with the use of image-derived motion, more pathophysiologically plausible biomechanical parameters can be estimated. Experiments on synthetic data showed that the optimization framework can properly identify the model parameters, and the experiments on clinical data showed that the proposed framework can achieve promising prediction performance.

Architecture

BLOCK DIAGRAM