A novel FPGA-based system for Tumor Growth Prediction

Abstract

An emerging trend in the biomedical community is to create models that take advantage of the increasing available computational power, in order to manage and analyze new biological data as well as to model complex biological processes. Such biomedical software applications require significant computational resources since they process and analyze large amounts of data, such as medical image sequences. This paper presents a novel FPGA-based system that implements a novel model for the prediction of the spatio-temporal evolution of glioma. Glioma is a rapidly evolving type of brain cancer, well known for its aggressive and diffusive behavior. The developed system simulates the glioma tumor growth in the brain tissue, which consists of different anatomic structures, by utilizing individual MRI slices. The presented innovative hardware system is more than 60% faster than a high-end server consisting of 20 physical cores (and 40 virtual ones) and more than 28x more energy efficient.