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Learned Primal-Dual Reconstruction
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.). Elekta Instrument AB, Stockholm, Sweden.ORCID iD: 0000-0001-9928-3407
KTH, School of Engineering Sciences (SCI), Mathematics (Dept.), Mathematics (Div.).ORCID iD: 0000-0002-1118-6483
2018 (English)In: IEEE Transactions on Medical Imaging, ISSN 0278-0062, E-ISSN 1558-254X, Vol. 37, no 6, p. 1322-1332Article in journal (Refereed) 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.

Place, publisher, year, edition, pages
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC , 2018. Vol. 37, no 6, p. 1322-1332
Keywords [en]
Inverse problems, tomography, deep learning, primal-dual, optimization
National Category
Computer Vision and Robotics (Autonomous Systems)
Identifiers
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
Note

QC 20180629

Available from: 2018-06-29 Created: 2018-06-29 Last updated: 2018-06-29Bibliographically approved

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Adler, JonasÖktem, Ozan

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