A Hybrid FPGA-GPU-based Hardware Accelerator for High Throughput 2D Electrical Impedance Tomography

A high-resolution two-dimensional (2D) electrical impedance tomography (EIT) system requires a larger number of electrodes and a finer mesh than its traditional counterpart. This increases the required number of measurements and, in turn, the amount of computation for the image reconstruction. Given the inverse and ill-posed nature of the EIT systems, they require a high signal-to-noise ratio (SNR) acquisition system as well as a high-precision hardware accelerator platform. In this paper, we present a field programmable gate array (FPGA)-based acquisition system with a tunable single-frequency current source that can reach an acquisition speed of more than 500 and 2400 frames per second (fps) for an excitation signal frequency of 500 kHz using 32 and 16 electrodes, respectively. The data processing and reconstruction are carried out using the most recent embedded Graphical Processing Unit (GPU, Nvidia Jetson Orin) by utilizing multiple Cuda cores to perform parallel high-speed 2D image reconstruction. Five different algorithms, namely linear back projection (LBP), Tikhonov regularization (TK), one-step Gauss Newton (GN), Landweber (LW), and iterative Tikhonov (ITK), were used for investigation. A gain in speed-up of at least 4 times was observed over the traditional implementations on recent general-purpose computers (PCs). Extensive experiments indicate that the proposed system can yield a throughput of more than 2500 fps for a 16-electrode system with around 8192 mesh elements. This paves the way for EIT systems to be potentially used in high-speed imaging applications as well as in 3D EIT applications which involve even larger amount of mesh elements.