edaGAN: Encoder-Decoder Attention Generative Adversarial Networks for multi-contrast MR image synthesis

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buir.contributor.authorDalmaz, Onat
buir.contributor.authorSağlam, Baturay
buir.contributor.authorGönç, Kaan
buir.contributor.authorÇukur, Tolga
buir.contributor.orcidDalmaz, Onat|0000-0001-7978-5311
buir.contributor.orcidSağlam, Baturay|0000-0002-8324-5980
buir.contributor.orcidÇukur, Tolga|0000-0002-2296-851X
dc.citation.epage324en_US
dc.citation.spage320en_US
dc.contributor.authorDalmaz, Onat
dc.contributor.authorSağlam, Baturay
dc.contributor.authorGönç, Kaan
dc.contributor.authorÇukur, Tolga
dc.coverage.spatialAlanya, Turkeyen_US
dc.date.accessioned2023-02-28T07:33:42Z
dc.date.available2023-02-28T07:33:42Z
dc.date.issued2022-05-16
dc.departmentAysel Sabuncu Brain Research Center (BAM)en_US
dc.departmentDepartment of Computer Engineeringen_US
dc.departmentDepartment of Electrical and Electronics Engineeringen_US
dc.departmentNational Magnetic Resonance Research Center (UMRAM)en_US
dc.descriptionConference Name: International Conf on Electrical and Electronic Engineering (ICEEE)en_US
dc.descriptionDate of conference: 29-31 March 2022en_US
dc.description.abstractMagnetic resonance imaging (MRI) is the preferred modality among radiologists in the clinic due to its superior depiction of tissue contrast. Its ability to capture different contrasts within an exam session allows it to collect additional diagnostic information. However, such multi-contrast MRI exams take a long time to scan, resulting in acquiring just a portion of the required contrasts. Consequently, synthetic multi-contrast MRI can improve subsequent radiological observations and image analysis tasks like segmentation and detection. Because of this significant potential, multi-contrast MRI synthesis approaches are gaining popularity. Recently, generative adversarial networks (GAN) have become the de facto choice for synthesis tasks in medical imaging due to their sensitivity to realism and high-frequency structures. In this study, we present a novel generative adversarial approach for multi-contrast MRI synthesis that combines the learning of deep residual convolutional networks and spatial modulation introduced by an attention gating mechanism to synthesize high-quality MR images. We show the superiority of the proposed approach against various synthesis models on multi-contrast MRI datasets.en_US
dc.description.provenanceSubmitted by Ayça Nur Sezen (ayca.sezen@bilkent.edu.tr) on 2023-02-28T07:33:41Z No. of bitstreams: 1 edaGAN_Encoder-Decoder_Attention_Generative_Adversarial_Networks_for_multi-contrast_MR_image_synthesis.pdf: 3441449 bytes, checksum: 69913a4dbebf970063fac8ff73725b46 (MD5)en
dc.description.provenanceMade available in DSpace on 2023-02-28T07:33:42Z (GMT). No. of bitstreams: 1 edaGAN_Encoder-Decoder_Attention_Generative_Adversarial_Networks_for_multi-contrast_MR_image_synthesis.pdf: 3441449 bytes, checksum: 69913a4dbebf970063fac8ff73725b46 (MD5) Previous issue date: 2022-05-16en
dc.identifier.doi10.1109/ICEEE55327.2022.9772555en_US
dc.identifier.urihttp://hdl.handle.net/11693/111879
dc.language.isoEnglishen_US
dc.publisherInstitute of Electrical and Electronics Engineersen_US
dc.relation.isversionofhttps://doi.org/10.1109/ICEEE55327.2022.9772555en_US
dc.source.titleInternational Conf on Electrical and Electronic Engineering (ICEEE)en_US
dc.subjectMRIen_US
dc.subjectSynthesisen_US
dc.subjectAttentionen_US
dc.subjectGenerativeen_US
dc.titleedaGAN: Encoder-Decoder Attention Generative Adversarial Networks for multi-contrast MR image synthesisen_US
dc.typeConference Paperen_US

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