High performance Savu software for fast 3D model-based iterative reconstruction of large data at Diamond Light Source

The challenge of processing big data effectively and efficiently is crucial for many synchrotron facilities which can collect up to several petabytes of data annually. At Diamond Light Source, the tomographic data is reconstructed with Python-based software Savu which utilises Message Passing Interface protocols to efficiently reconstruct parallel beam geometry data. When projection data is undersampled and/or noisy, regularised iterative reconstruction methods can provide a better reconstruction quality than direct methods. The iterative methods, however, require significantly more computational resources than direct methods and their usability is impeded by the choice of additional hyper-parameters. Notably, the use of 2D regularised iterative methods for reconstruction of 3D objects results in inconsistent (saw-shaped) features in a perpendicular to slicing orientation. Due to large data sizes, fully 3D regularised model-based iterative reconstruction is problematic or impossible in practice due to high memory requirements and long processing times. In this work, we demonstrate a practical solution which enables an approximated full 3D regularised iterative reconstruction running in parallel on a computing cluster. This modification delivers an equivalent to exact 3D reconstruction quality of data volumes with a high computational efficiency. (C) 2022 Diamond Light Source Ltd. Published by Elsevier B.V.