{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/79882"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/79882","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Image Reconstruction Using Generative Adversarial Networks with a Hybrid Loss","abstract":"M.Eng.","abstract_html":"M.Eng.","abstract_has_math":false,"creators":["Liu, Ruiying"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Ying, Leslie","Electrical Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2019,"date_issued":"2019-07-30T15:10:44Z","date_published":"2019-07-30T15:10:44Z","updated_at":"2026-07-27T19:05:19Z","subjects":["electrical engineering"],"languages":["eng"],"rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"rights_urls":[],"identifier_entries":[]},"links":{"outbound_url":"http://hdl.handle.net/10477/79882","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ying, Leslie","Electrical Engineering"]},{"key":"dc:creator","label":"Author","values":["Liu, Ruiying"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2019-07-30T15:10:44Z","2019","2019-05-01 12:06:37"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Thesis"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["electrical engineering"]}]},{"id":"language_rights","label":"Language and Rights","entries":[{"key":"dc:language","label":"Dc Language","values":["eng"]},{"key":"dc:rights","label":"Dc Rights","values":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."]}]},{"id":"identifiers","label":"Identifiers","entries":[{"key":"dc:identifier","label":"Identifier","values":["http://hdl.handle.net/10477/79882"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["M.Eng.","High-quality magnetic resonance images (MRI) provides detailed anatomical information important for clinical application and quantitative image analysis. In many clinical applications, MRI k-space data is undersampled in order to reduce the scanning time and improve the patient experience. In this case, image reconstruction algorithm becomes important to maintain the image quality using only undersampled data. Deep learning methods have demonstrated great potential in MR image reconstruction due to its ability to learn the non-linearity relationship between the under-sampled k-space data and the corresponding desired image. However, most studies have found that high-frequency details are lost in deep learning based reconstruction and the images are unsatisfactory perceptually with overly smooth textures. Among these methods, Generative Adversarial Networks (GANs) are known to reconstruct images that are sharper and more realistic-looking. In this study, we propose a novel hybrid loss function for GAN to reconstruct high perceptual quality MR images. The experiment demonstrates that the hybrid loss function is superior to the individual ones alone for GAN-based reconstruction."]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Image Reconstruction Using Generative Adversarial Networks with a Hybrid Loss"]}]}],"canonical_facts":{"dc:contributor":["Ying, Leslie","Electrical Engineering"],"dc:creator":["Liu, Ruiying"],"dc:date":["2019-07-30T15:10:44Z","2019","2019-05-01 12:06:37"],"dc:description":["M.Eng.","High-quality magnetic resonance images (MRI) provides detailed anatomical information important for clinical application and quantitative image analysis. In many clinical applications, MRI k-space data is undersampled in order to reduce the scanning time and improve the patient experience. In this case, image reconstruction algorithm becomes important to maintain the image quality using only undersampled data. Deep learning methods have demonstrated great potential in MR image reconstruction due to its ability to learn the non-linearity relationship between the under-sampled k-space data and the corresponding desired image. However, most studies have found that high-frequency details are lost in deep learning based reconstruction and the images are unsatisfactory perceptually with overly smooth textures. Among these methods, Generative Adversarial Networks (GANs) are known to reconstruct images that are sharper and more realistic-looking. In this study, we propose a novel hybrid loss function for GAN to reconstruct high perceptual quality MR images. The experiment demonstrates that the hybrid loss function is superior to the individual ones alone for GAN-based reconstruction."],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/79882"],"dc:language":["eng"],"dc:publisher":["State University of New York at Buffalo"],"dc:rights":["Users of works found in University at Buffalo Institutional Repository (UBIR) are responsible for identifying and contacting the copyright owner for permission to reuse. University at Buffalo Libraries do not manage rights for copyright-protected works and cannot assist with permissions.","Copyright retained by author."],"dc:subject":["electrical engineering"],"dc:title":["Image Reconstruction Using Generative Adversarial Networks with a Hybrid Loss"],"dc:type":["Text","Thesis"]},"updated_at":"2026-07-27T19:05:19Z"}