Data Intensive Computing of X-ray Computed Tomography Reconstruction at the LSDF

In this paper, the method of data intensive computing is studied for large amounts of data in computed tomography (CT). An automatic workflow is built up to connect the tomography beamline of ANKA with the large scale data facility (LSDF), able to enhance the data storage and analysis efficiency. In this workflow, this paper focuses on the parallel computing of 3D computed tomography reconstruction. Different from the existing reconstruction system with filtered back-projection method, an algebraic reconstruction technique based on compressive sampling theory is presented to reconstruct the data from ultrafast computed tomography with fewer projections. Then the connected computing resources at the LSDF are used to implement the 3D CT reconstruction by distributing the whole job into multiple tasks executed in parallel. Promising reconstruction images and high computing performance are reported. For the 3D X-ray CT reconstruction, less than six minutes are actually required. LSDF is not only able to organize data efficiently, but also can provide reconstructed results to users in nearly instantaneous time. After integration into the workflow, this data intensive computing method will largely improve the data processing for ultrafast computed tomography at ANKA.