Improving image synthesis quality in multi-contrast MRI using transfer learning via autoencoders

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buir.contributor.authorSelçuk, Şahan Yoruç
buir.contributor.authorDalmaz, Onat
buir.contributor.authorUl Hassan Dar, Salman
buir.contributor.authorÇukur, Tolga
buir.contributor.orcidÇukur, Tolga|0000-0002-2296-851X
dc.citation.epage[4]en_US
dc.citation.spage[1]en_US
dc.contributor.authorSelçuk, Şahan Yoruç
dc.contributor.authorDalmaz, Onat
dc.contributor.authorUl Hassan Dar, Salman
dc.contributor.authorÇukur, Tolga
dc.coverage.spatialSafranbolu, Turkeyen_US
dc.date.accessioned2023-02-14T13:13:15Z
dc.date.available2023-02-14T13:13:15Z
dc.date.issued2022-08-29
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentNational Magnetic Resonance Research Center (UMRAM)en_US
dc.departmentAysel Sabuncu Brain Research Center
dc.descriptionConference Name: 2022 30th Signal Processing and Communications Applications Conference (SIU)en_US
dc.descriptionDate of Conference: 15-18 May 2022en_US
dc.description.abstractThe capacity of magnetic resonance imaging (MRI) to capture several contrasts within a session enables it to obtain increased diagnostic information. However, such multi-contrast MRI tests take a long time to scan, resulting in acquiring just a part of the essential contrasts. Synthetic multi-contrast MRI has the potential to improve radiological observations and consequent image analysis activities. Because of its ability to generate realistic results, generative adversarial networks (GAN) have recently been the most popular choice for medical imaging synthesis. This paper proposes a novel generative adversarial framework to improve the image synthesis quality in multi-contrast MRI. Our method uses transfer learning to adapt pre-trained autoencoder networks to the synthesis task and enhances the image synthesis quality by initializing the training process with more optimal network parameters. We demonstrate that the proposed method outperforms competing synthesis models by 0.95 dB on average on a well-known multi-contrast MRI dataset.en_US
dc.identifier.doi10.1109/SIU55565.2022.9864750en_US
dc.identifier.eisbn978-1-6654-5092-8
dc.identifier.issn2165-0608
dc.identifier.urihttp://hdl.handle.net/11693/111273
dc.language.isoEnglishen_US
dc.publisherIEEEen_US
dc.relation.isversionofhttps://www.doi.org/10.1109/SIU55565.2022.9864750en_US
dc.source.titleSignal Processing and Communications Applications Conference (SIU)en_US
dc.subjectMulti-contrast MRIen_US
dc.subjectAutoencoderen_US
dc.subjectTransfer learningen_US
dc.subjectGenerative adversarial networksen_US
dc.titleImproving image synthesis quality in multi-contrast MRI using transfer learning via autoencodersen_US
dc.title.alternativeÇoklu kontrast MRG’de otokodlayıcı ve öğrenme aktarımı kullanarak görüntü sentez kalitesini iyileştirmeen_US
dc.typeConference Paperen_US

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Department of Electrical and Electronics Engineering
Aysel Sabuncu Brain Research Center (BAM)
National Magnetic Resonance Research Center (UMRAM)