An improved combination of ordered subsets and momentum for fast model-based iterative CT reconstruction

Haase V, Stierstorfer K, Hahn K, Schöndube H, Maier A, Noo F (2020)

Publication Type: Conference contribution

Publication year: 2020

Journal

Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Publisher: SPIE

Book Volume: 11312

Conference Proceedings Title: Progress in Biomedical Optics and Imaging - Proceedings of SPIE

Event location: Houston, TX US

ISBN: 9781510633919

DOI: 10.1117/12.2549733

Abstract

Model-based iterative CT reconstruction suffers from its high computational cost. To increase the reconstruction speed, algorithms using a momentum acceleration can be combined with the ordered subset approach. As shown in previous works, the incorporation of ordered subsets strongly improves the reconstruction time but makes the outcome of the reconstruction difficult to predict due to its empirical nature. Specifically, when the number of subsets is too high, divergence or convergence to an unsatisfactory result can easily appear. In this work, we propose a new combination of ordered subsets with momentum to achieve fast reconstruction in a more robust manner. Our approach, referred to as EOS-MFISTA, is an Efficient Ordered Subset strategy based on the steps of MFISTA, a monotonic version of FISTA. In short, EOS-MFISTA uses objective function values to decide on the next update step and does this with little increase in computational effort. The performance of EOS-MFISTA is evaluated on real CT data of a head phantom and a human chest. Starting from a zero image, 200 iterations of EOS-MFISTA are calculated and compared with other algorithms. Quantitative analyses based on the RMSE show that EOS-MFISTA is much more robust than OS-FISTA, especially when the number of subsets is increased to accelerate reconstruction. Visual inspection of results obtained with 50 iterations further supports these findings.

Authors with CRIS profile

Viktor Haase Lehrstuhl für Informatik 5 (Mustererkennung) Andreas Maier Lehrstuhl für Informatik 5 (Mustererkennung)

Involved external institutions

Siemens AG DE Germany (DE) University of Utah US United States (USA) (US)

How to cite

APA:

Haase, V., Stierstorfer, K., Hahn, K., Schöndube, H., Maier, A., & Noo, F. (2020). An improved combination of ordered subsets and momentum for fast model-based iterative CT reconstruction. In Guang-Hong Chen, Hilde Bosmans (Eds.), Progress in Biomedical Optics and Imaging - Proceedings of SPIE. Houston, TX, US: SPIE.

MLA:

Haase, Viktor, et al. "An improved combination of ordered subsets and momentum for fast model-based iterative CT reconstruction." Proceedings of the Medical Imaging 2020: Physics of Medical Imaging, Houston, TX Ed. Guang-Hong Chen, Hilde Bosmans, SPIE, 2020.

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