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Multi-Scale Learned Iterative Reconstruction

Hauptmann, A; Adler, J; Arridge, SR; Öktem, O; (2020) Multi-Scale Learned Iterative Reconstruction. IEEE Transactions on Computational Imaging , 6 pp. 843-856. 10.1109/TCI.2020.2990299. Green open access

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Abstract

Model-based learned iterative reconstruction methods have recently been shown to outperform classical reconstruction algorithms. Applicability of these methods to large scale inverse problems is however limited by the available memory for training and extensive training times, the latter due to computationally expensive forward models. As a possible solution to these restrictions we propose a multi-scale learned iterative reconstruction scheme that computes iterates on discretisations of increasing resolution. This procedure does not only reduce memory requirements, it also considerably speeds up reconstruction and training times, but most importantly is scalable to large scale inverse problems with non-trivial forward operators, such as those that arise in many 3D tomographic applications. In particular, we propose a hybrid network that combines the multi-scale iterative approach with a particularly expressive network architecture which in combination exhibits excellent scalability in 3D. Applicability of the algorithm is demonstrated for 3D cone beam computed tomography from real measurement data of an organic phantom. Additionally, we examine scalability and reconstruction quality in comparison to established learned reconstruction methods in two dimensions for low dose computed tomography on human phantoms.

Type: Article
Title: Multi-Scale Learned Iterative Reconstruction
Open access status: An open access version is available from UCL Discovery
DOI: 10.1109/TCI.2020.2990299
Publisher version: https://doi.org/10.1109/TCI.2020.2990299
Language: English
Additional information: This version is the author accepted manuscript. For information on re-use, please refer to the publisher’s terms and conditions.
Keywords: Model-based learning, iterative reconstruction, cone beam computed tomography, deep learning, inverse problems
UCL classification: UCL
UCL > Provost and Vice Provost Offices
UCL > Provost and Vice Provost Offices > UCL BEAMS
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science
UCL > Provost and Vice Provost Offices > UCL BEAMS > Faculty of Engineering Science > Dept of Computer Science
URI: https://discovery.ucl.ac.uk/id/eprint/10096511
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