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Spatiotemporal PET Reconstruction Using ML-EM with Learned Diffeomorphic Deformation
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).ORCID iD: 0000-0002-1118-6483
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).
2019 (English)In: 2nd International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2019 held in Conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019, Springer, 2019, Vol. 11905, p. 151-162Conference paper, Published paper (Refereed)
Abstract [en]

Patient movement in emission tomography deteriorates reconstruction quality because of motion blur. Gating the data improves the situation somewhat: each gate contains a movement phase which is approximately stationary. A standard method is to use only the data from a few gates, with little movement between them. However, the corresponding loss of data entails an increase of noise. Motion correction algorithms have been implemented to take into account all the gated data, but they do not scale well in computation time, especially not in 3D. We propose a novel motion correction algorithm which addresses the scalability issue. Our approach is to combine an enhanced ML-EM algorithm with deep learning based movement registration. The training is unsupervised, and with artificial data. We expect this approach to scale very well to higher resolutions and to 3D, as the overall cost of our algorithm is only marginally greater than that of a standard ML-EM algorithm. We show that we can significantly decrease the noise corresponding to a limited number of gates.

Place, publisher, year, edition, pages
Springer, 2019. Vol. 11905, p. 151-162
Series
Lecture Notes in Computer Science (including subseries Lecture Notes in Artificial Intelligence and Lecture Notes in Bioinformatics), ISSN 03029743 ; 11905
Keywords [en]
Deep learning, Emission tomography, Motion correction
National Category
Electrical Engineering, Electronic Engineering, Information Engineering
Identifiers
URN: urn:nbn:se:kth:diva-266081DOI: 10.1007/978-3-030-33843-5_14Scopus ID: 2-s2.0-85076217927ISBN: 9783030338428 (print)OAI: oai:DiVA.org:kth-266081DiVA, id: diva2:1381177
Conference
2nd International Workshop on Machine Learning for Medical Image Reconstruction, MLMIR 2019 held in Conjunction with 22nd International Conference on Medical Image Computing and Computer-Assisted Intervention, MICCAI 2019; Shenzhen; China; 17 October 2019 through 17 October 2019
Note

QC 20191220

Available from: 2019-12-20 Created: 2019-12-20 Last updated: 2019-12-20Bibliographically approved

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Öktem, OzanPouchol, Camille

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