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Learned Primal-Dual Reconstruction
KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.). Elekta Instrument AB, Stockholm, Sweden.ORCID-id: 0000-0001-9928-3407
KTH, Skolan för teknikvetenskap (SCI), Matematik (Inst.), Matematik (Avd.).ORCID-id: 0000-0002-1118-6483
2018 (Engelska)Ingår i: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 37, nr 6, s. 1322-1332Artikel i tidskrift (Refereegranskat) Published
Abstract [en]

We propose the Learned Primal-Dual algorithm for tomographic reconstruction. The algorithm accounts for a (possibly non-linear) forward operator in a deep neural network by unrolling a proximal primal-dual optimization method, but where the proximal operators have been replaced with convolutional neural networks. The algorithm is trained end-to-end, working directly from raw measured data and it does not depend on any initial reconstruction such as filtered back-projection (FBP). We compare performance of the proposed method on low dose computed tomography reconstruction against FBP, total variation (TV), and deep learning based post-processing of FBP. For the Shepp-Logan phantom we obtain >6 dB peak signal to noise ratio improvement against all compared methods. For human phantoms the corresponding improvement is 6.6 dB over TV and 2.2 dB over learned post-processing along with a substantial improvement in the structural similarity index. Finally, our algorithm involves only ten forward-back-projection computations, making the method feasible for time critical clinical applications.

Ort, förlag, år, upplaga, sidor
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2018. Vol. 37, nr 6, s. 1322-1332
Nyckelord [en]
Inverse problems, tomography, deep learning, primal-dual, optimization
Nationell ämneskategori
Datorseende och robotik (autonoma system)
Identifikatorer
URN: urn:nbn:se:kth:diva-231206DOI: 10.1109/TMI.2018.2799231ISI: 000434302700004PubMedID: 29870362Scopus ID: 2-s2.0-85041342868OAI: oai:DiVA.org:kth-231206DiVA, id: diva2:1228991
Anmärkning

QC 20180629

Tillgänglig från: 2018-06-29 Skapad: 2018-06-29 Senast uppdaterad: 2019-10-18Bibliografiskt granskad
Ingår i avhandling
1. Data-driven Methods in Inverse Problems
Öppna denna publikation i ny flik eller fönster >>Data-driven Methods in Inverse Problems
2019 (Engelska)Doktorsavhandling, sammanläggning (Övrigt vetenskapligt)
Abstract [en]

In this thesis on data-driven methods in inverse problems we introduce several new methods to solve inverse problems using recent advancements in machine learning and specifically deep learning. The main goal has been to develop practically applicable methods, scalable to medical applications and with the ability to handle all the complexities associated with them.

In total, the thesis contains six papers. Some of them are focused on more theoretical questions such as characterizing the optimal solutions of reconstruction schemes or extending current methods to new domains, while others have focused on practical applicability. A significant portion of the papers also aim to bringing knowledge from the machine learning community into the imaging community, with considerable effort spent on translating many of the concepts. The papers have been published in a range of venues: machine learning, medical imaging and inverse problems.

The first two papers contribute to a class of methods now called learned iterative reconstruction where we introduce two ways of combining classical model driven reconstruction methods with deep neural networks. The next two papers look forward, aiming to address the question of "what do we want?" by proposing two very different but novel loss functions for training neural networks in inverse problems. The final papers dwelve into the statistical side, one gives a generalization of a class of deep generative models to Banach spaces while the next introduces two ways in which such methods can be used to perform Bayesian inversion at scale.

Ort, förlag, år, upplaga, sidor
Stockholm: KTH Royal Institute of Technology, 2019. s. 196
Serie
TRITA-SCI-FOU ; 2019;49
Nyckelord
Inverse Problems, Machine Learning, Tomography
Nationell ämneskategori
Beräkningsmatematik
Identifikatorer
urn:nbn:se:kth:diva-262727 (URN)978-91-7873-334-7 (ISBN)
Disputation
2019-10-31, F3, Lindstedtsvägen26, KTH, Stockholm, 14:00 (Engelska)
Opponent
Handledare
Forskningsfinansiär
Stiftelsen för strategisk forskning (SSF)
Tillgänglig från: 2019-10-21 Skapad: 2019-10-18 Senast uppdaterad: 2019-10-21Bibliografiskt granskad

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