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Physical limitations found in industrial environments often restrain imaging for process tomography. When information is collected from sparse sensors, the acquired data is limited in terms of radial and angular sampling of the imaged slice. To overcome this problem, we demonstrate an efficient solution based on the parallel implementation of the sinogram recovery algorithm (SRA) for limited views in its variant based on the calculation of the coordinates of the center of mass (CoMs) of the subject under test, rather than performing the complete sinogram restoration. By introducing a modification in the existing SRA, we achieve high parallelization of each stage, making it ideal for implementation in hardware accelerated systems, such as field programmable gate arrays. The potential to parallelize the SRA is first studied in MATLAB, by processing all data projections concurrently and verifying performance by matching the results from the parallel and sequential implementations. Furthermore, the algorithm is coded in very high speed integrated circuits hardware description language, which is implemented and tested on a Xilinx Virtex 6 board. We report speedups of between three and four orders of magnitude, whereas the errors in CoMs' coordinates are reduced.