Learning-based compressive MRI

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buir.contributor.authorÇukur, Tolga
dc.citation.epage1406en_US
dc.citation.issueNumber6en_US
dc.citation.spage1394en_US
dc.citation.volumeNumber37en_US
dc.contributor.authorGözcü, B.en_US
dc.contributor.authorMahabadi, R. K.en_US
dc.contributor.authorLi, Y. H.en_US
dc.contributor.authorIlıcak, E.en_US
dc.contributor.authorÇukur, Tolgaen_US
dc.contributor.authorScarlett, J.en_US
dc.contributor.authorCevher, V.en_US
dc.date.accessioned2019-02-21T16:05:41Zen_US
dc.date.available2019-02-21T16:05:41Zen_US
dc.date.issued2018en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractIn the area of magnetic resonance imaging (MRI), an extensive range of non-linear reconstruction algorithms has been proposed which can be used with general Fourier subsampling patterns. However, the design of these subsampling patterns has typically been considered in isolation from the reconstruction rule and the anatomy under consideration. In this paper, we propose a learning-based framework for optimizing MRI subsampling patterns for a specific reconstruction rule and anatomy, considering both the noiseless and noisy settings. Our learning algorithm has access to a representative set of training signals, and searches for a sampling pattern that performs well on average for the signals in this set. We present a novel parameter-free greedy mask selection method and show it to be effective for a variety of reconstruction rules and performance metrics. Moreover, we also support our numerical findings by providing a rigorous justification of our framework via statistical learning theory.en_US
dc.description.provenanceMade available in DSpace on 2019-02-21T16:05:41Z (GMT). No. of bitstreams: 1 Bilkent-research-paper.pdf: 222869 bytes, checksum: 842af2b9bd649e7f548593affdbafbb3 (MD5) Previous issue date: 2018en
dc.description.sponsorshipManuscript received February 1, 2018; revised April 18, 2018; accepted April 20, 2018. Date of publication May 2, 2018; date of current version May 31, 2018. This work was supported by the European Research Council through the European Union’s Horizon 2020 Research and Innovation Program (time-data) under Grant 725594, in part by the Hasler Foundation Program: Cyber Human Systems under Project 16066, and in part by the Department of the Navy, Office of Naval Research, under Grant N62909-17-1-2111. (Corresponding author: Baran Gözcü.) B. Gözcü, R. K. Mahabadi, Y.-H. Li, and V. Cevher are with the Laboratory for Information and Inference Systems, École Polytech-nique Fédérale de Lausanne, 1015 Lausanne, Switzerland (e-mail: baran.goezcue@epfl.ch).en_US
dc.identifier.doi10.1109/TMI.2018.2832540en_US
dc.identifier.eissn1558-254Xen_US
dc.identifier.issn0278-0062en_US
dc.identifier.urihttp://hdl.handle.net/11693/50267en_US
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttps://doi.org/10.1109/TMI.2018.2832540en_US
dc.relation.project16066 - European Research Council, ERC - Horizon 2020: 725594 - Office of Naval Research, ONR: N62909-17-1-2111en_US
dc.source.titleIEEE Transactions on Medical Imagingen_US
dc.subjectCompressive sensingen_US
dc.subjectGreedy algorithmsen_US
dc.subjectLearning-based subsamplingen_US
dc.subjectMagnetic resonance imagingen_US
dc.titleLearning-based compressive MRIen_US
dc.typeArticleen_US

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