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Novel reconfigurable hardware systems for tumor growth prediction

Malavazos Konstantinos, Papadogiorgaki Maria, Malakonakis Pavlos, Papaefstathiou Ioannis

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Year 2021
Type of Item Peer-Reviewed Journal Publication
Bibliographic Citation K. Malavazos, M. Papadogiorgaki, P. Malakonakis and I. Papaefstathiou, “Novel reconfigurable hardware systems for tumor growth prediction,” ACM Trans. Comput. Healthcare, vol. 2, no. 4, July 2021, doi: 10.1145/3454126.
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An emerging trend in biomedical systems research is the development of models that take full advantage of the increasing available computational power to manage and analyze new biological data as well as to model complex biological processes. Such biomedical models require significant computational resources, since they process and analyze large amounts of data, such as medical image sequences. We present a family of advanced computational models for the prediction of the spatio-temporal evolution of glioma and their novel implementation in state-of-the-art FPGA devices. 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 MRI slices. The presented models have been proved highly accurate in predicting the growth of the tumor, whereas the developed innovative hardware system, when implemented on a low-end, low-cost FPGA, is up to 85% faster than a high-end server consisting of 20 physical cores (and 40 virtual ones) and more than 28× more energy-efficient than it; the energy efficiency grows up to 50× and the speedup up to 14× if the presented designs are implemented in a high-end FPGA. Moreover, the proposed reconfigurable system, when implemented in a large FPGA, is significantly faster than a high-end GPU (i.e., from 80% and up to 250% faster), for the majority of the models, while it is also significantly better (i.e., from 80% to over 1,600%) in terms of power efficiency, for all the implemented models.