FD-Net: an unsupervised deep forward-distortion model forsusceptibility artifact correction in EPI

buir.contributor.authorZaid Alkilani, Abdallah
buir.contributor.authorÇukur, Tolga
buir.contributor.authorSarıtaş, Emine Ülkü
buir.contributor.orcidZaid Alkilani, Abdallah|0000-0001-8409-8444
buir.contributor.orcidÇukur, Tolga|0000-0002-2296-851X
buir.contributor.orcidSarıtaş, Emine Ülkü|0000-0001-8551-1077
dc.citation.epage296en_US
dc.citation.issueNumber91
dc.citation.spage280
dc.citation.volumeNumber2024
dc.contributor.authorZaid Alkilani, Abdallah
dc.contributor.authorÇukur, Tolga
dc.contributor.authorSarıtaş, Emine Ülkü
dc.date.accessioned2024-03-17T06:49:21Z
dc.date.available2024-03-17T06:49:21Z
dc.date.issued2023-08-15
dc.departmentDepartment of Electrical and Electronics Engineering
dc.departmentNational Magnetic Resonance Research Center (UMRAM)
dc.departmentAysel Sabuncu Brain Research Center (BAM)
dc.description.abstractPurposeTo introduce an unsupervised deep-learning method for fast and effective correction of susceptibility artifacts in reversed phase-encode (PE) image pairs acquired with echo planar imaging (EPI).MethodsRecent learning-based correction approaches in EPI estimate a displacement field, unwarp the reversed-PE image pair with the estimated field, and average the unwarped pair to yield a corrected image. Unsupervised learning in these unwarping-based methods is commonly attained via a similarity constraint between the unwarped images in reversed-PE directions, neglecting consistency to the acquired EPI images. This work introduces a novel unsupervised deep Forward-Distortion Network (FD-Net) that predicts both the susceptibility-induced displacement field and the underlying anatomically correct image. Unlike previous methods, FD-Net enforces the forward-distortions of the correct image in both PE directions to be consistent with the acquired reversed-PE image pair. FD-Net further leverages a multiresolution architecture to maintain high local and global performance.ResultsFD-Net performs competitively with a gold-standard reference method (TOPUP) in image quality, while enabling a leap in computational efficiency. Furthermore, FD-Net outperforms recent unwarping-based methods for unsupervised correction in terms of both image and field quality.ConclusionThe unsupervised FD-Net method introduces a deep forward-distortion approach to enable fast, high-fidelity correction of susceptibility artifacts in EPI by maintaining consistency to measured data. Therefore, it holds great promise for improving the anatomical accuracy of EPI imaging.
dc.identifier.doi10.1002/mrm.29851
dc.identifier.eissn1522-2594
dc.identifier.issn0740-3194
dc.identifier.urihttps://hdl.handle.net/11693/114830
dc.language.isoen_US
dc.publisherWILEY
dc.relation.isversionofhttps://doi.org/10.1002/mrm.29851
dc.rightsCC BY-NC-ND 4.0 DEED (Attribution-NonCommercial-NoDerivs 4.0 International)
dc.rights.urihttps://creativecommons.org/licenses/by-nc-nd/4.0/
dc.source.titleMagnetic Resonance in Medicine
dc.subjectDeep learning
dc.subjectEcho planar imaging
dc.subjectReversed phase-encoding
dc.subjectSusceptibility artifacts
dc.subjectUnsupervised learning
dc.titleFD-Net: an unsupervised deep forward-distortion model forsusceptibility artifact correction in EPI
dc.typeArticle

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