About ToMoBAR#

The general concept#

ToMoBAR is a Python library (Matlab is not currently maintained) of direct and model-based regularised iterative reconstruction algorithms with a plug-and-play capability. ToMoBAR offers you a selection of various data models and regularisers resulting in complex objectives for tomographic reconstruction. ToMoBAR can operate in GPU device-to-device fashion on CuPy arrays therefore ensuring a better computational efficiency. With GPU device controlling API exposed it can also support multi-GPU parallel computing.

ToMoBAR in action

What ToMoBAR can do:#

  • Reconstruct parallel-beam projection data in 2D and 3D using GPU-accelerated routines from ASTRA-toolbox.

  • Employ the basic direct and iterative schemes to perform reconstruction.

  • Employ advanced model-based regularised iterative schemes such as FISTA and ADMM proximal splitting algorithms.

  • The FISTA algorithm offers various modifications: convergence acceleration with ordered-subsets method, different data fidelities: PWLS, Kullback-Leibler, Huber, Group-Huber [PM2015], Students’t [KAZ1_2017], and SWLS [HOA2017] to deal with noise and imaging artefacts (rings, streaks).

  • Together with regularisers from the CCPi-Regularisation Toolkit [KAZ2019] one can construct up to a hundred of complex combinations for the objective function.

See more on API of ToMoBAR in API reference.