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EIT image reconstruction may be performed by a Simulated Annealing algorithm that minimizes the differences between the superficial impedance behavior of a virtual body simulated using the Finite Element Method and real data acquired on a physical body. The evaluation of objective functions - that involve solving FEM linear systems - is responsible for the majority of the process computational cost. This work presents a strategy for implementing the Preconditioned Conjugate Gradient algorithm on a GPU in order to benefit from its massive parallel computing capacities. This strategy takes in account the specificities of the EIT reconstruction through SA. It involves heavy preprocessing to identify the computations that may be performed in parallel. Initial results show that this strategy greatly improves not only on sequential approaches, but also on other generic GPU approaches.

GPU accelerated reconstruction of Electrical Impedance Tomography Images through Simulated Annealing