Projection onto epigraph sets for rapid self-tuning compressed sensing MRI

Simple item page
buir.contributor.authorÇetin, A. Enis
buir.contributor.authorShahdloo, Mohammad
buir.contributor.authorIlıcak, Efe
buir.contributor.authorTofighi, Mohammad
buir.contributor.authorSarıtaş, Emine Ülkü
buir.contributor.authorÇukur, Tolga
buir.contributor.orcidÇetin, A. Enis|0000-0002-3449-1958
dc.citation.epage1689en_US
dc.citation.spage1677en_US
dc.citation.volumeNumber38en_US
dc.contributor.authorShahdloo, Mohammaden_US
dc.contributor.authorIlıcak, Efeen_US
dc.contributor.authorTofighi, Mohammaden_US
dc.contributor.authorSarıtaş, Emine Ülküen_US
dc.contributor.authorÇetin, A. Enisen_US
dc.contributor.authorÇukur, Tolgaen_US
dc.date.accessioned2018-12-24T13:28:13Z
dc.date.available2018-12-24T13:28:13Z
dc.date.issued2019en_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.description.abstractThe compressed sensing (CS) framework leverages the sparsity of MR images to reconstruct from undersampled acquisitions. CS reconstructions involve one or more regularization parameters that weigh sparsity in transform domains against fidelity to acquired data. While parameter selection is critical for reconstruction quality, the optimal parameters are subject and dataset specific. Thus, commonly practiced heuristic parameter selection generalizes poorly to independent datasets. Recent studies have proposed to tune parameters by estimating the risk of removing significant image coefficients. Line searches are performed across the parameter space to identify the parameter value that minimizes this risk. Although effective, these line searches yield prolonged reconstruction times. Here, we propose a new self-tuning CS method that uses computationally efficient projections onto epigraph sets of the ℓ1 and total-variation norms to simultaneously achieve parameter selection and regularization. In vivo demonstrations are provided for balanced steady-state free precession, time-of-flight, and T1-weighted imaging. The proposed method achieves an order of magnitude improvement in computational efficiency over line-search methods while maintaining near-optimal parameter selection.en_US
dc.description.provenanceSubmitted by Taner Korkmaz (tanerkorkmaz@bilkent.edu.tr) on 2018-12-24T13:28:13Z No. of bitstreams: 1 08567963.pdf: 1711922 bytes, checksum: 4ff761fc554c7ba159dfd0ee96837d1e (MD5)en
dc.description.provenanceMade available in DSpace on 2018-12-24T13:28:13Z (GMT). No. of bitstreams: 1 08567963.pdf: 1711922 bytes, checksum: 4ff761fc554c7ba159dfd0ee96837d1e (MD5) Previous issue date: 2018-12-07en
dc.description.sponsorshipIEEE Engineering in Medicine and Biology Society IEEE Signal Processing Society IEEE Nuclear and Plasma Sciences Society IEEE Ultrasonics, Ferroelectrics, and Frequency Control Societyen_US
dc.identifier.doi10.1109/TMI.2018.2885599en_US
dc.identifier.eissn1558-254X
dc.identifier.issn0278-0062
dc.identifier.urihttp://hdl.handle.net/11693/48210
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://www.doi.org/10.1109/TMI.2018.2885599en_US
dc.source.titleIEEE Transactions on Medical Imagingen_US
dc.subjectImage reconstructionen_US
dc.subjectCoilsen_US
dc.subjectTVen_US
dc.subjectCalibrationen_US
dc.subjectMagnetic resonance imagingen_US
dc.titleProjection onto epigraph sets for rapid self-tuning compressed sensing MRIen_US
dc.typeArticleen_US

Files

Original bundle
Now showing 1 - 1 of 1
Thumbnail Image
Name:
Projection_onto_epigraph_sets_for_rapid_self_tuning_compressed_sensing_MRI.pdf
Size:
3.79 MB
Format:
Adobe Portable Document Format
Description:
View / Download
Download

Collections

Scholarly Publications - Electrical and Electronics Engineering