ToMoBAR’s documentation

ToMoBAR (cite [CT2020], [SX2022]) is a Python library 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.

Although ToMoBAR does offer a variety of reconstruction methods, the FISTA algorithm [BT2009] specifically provides various useful modifications, e.g.: convergence acceleration with ordered-subsets, different data fidelities: PWLS, Kullback-Leibler, Huber, Group-Huber [PM2015], Students’t [KAZ1_2017], and SWLS [HOA2017] to deal with noise and reconstruction artefacts (rings, streaks). Together with the regularisers from the CCPi-Regularisation toolkit [KAZ2019] one can construct up to a hundred of complex combinations for the objective function.

Introduction

• About ToMoBAR
  • The general concept
  • What ToMoBAR can do:
• Dependencies
• References

How To's

• Installation Guide
  • Python
  • Matlab
• Using ToMoBAR

Tutorials

• Direct reconstruction
• Basic iterative reconstruction
• Regularised iterative reconstruction
• Synthethic data reconstruction
• Real data reconstruction

Reference guides

• API reference
  • ToMoBAR Modules