Hardware Optimizations of the X-ray Pre-Processing for Interventional Computed Tomography Using the FPGA - Daniele Passaretti; Mukesh Ghosh; Shiras Abdurahman; Micaela Lambru Egito; Thilo Pionteck (Applied Sciences, 2022)
In computed tomography imaging, the computationally intensive tasks are the pre-processing of 2D detector data to generate total attenuation or line integral projections and the reconstruction of the 3D volume from the projections. This paper proposes the optimization of the X-ray pre-processing to compute total attenuation projections by avoiding the intermediate step to convert detector data to intensity images. In addition, to fulfill the real-time requirements, we design a configurable hardware architecture for data acquisition systems on FPGAs, with the goal to have a “on-the-fly” pre-processing of 2D projections. Finally, this architecture was configured for exploring and analyzing different arithmetic representations, such as floating-point and fixed-point data formats. This design space exploration has allowed us to find the best representation and data format that minimize execution time and hardware costs, while not affecting image quality. Furthermore, the proposed architecture was integrated in an open-interface computed tomography device, used for evaluating the image quality of the pre-processed 2D projections and the reconstructed 3D volume. By comparing the proposed solution with the state-of-the-art pre-processing algorithm that make use of intensity images, the latency was decreased 4.125×, and the resources utilization of ∼6.5×, with a mean square error in the order of <semantics>10−15</semantics> for all the selected phantom experiments. Finally, by using the fixed-point representation in the different data precisions, the latency and the resource utilization were further decreased, and a mean square error in the order of <semantics>10−1</semantics> was reached.