{"id":{"repo_id":"buffalo","oai_identifier":"oai:ubir.buffalo.edu:10477/84034"},"canonical_url":"https://search.dev.ndltd.org/etd/buffalo/oai:ubir.buffalo.edu:10477/84034","repository":{"repo_id":"buffalo","name":"Buffalo","base_url":"https://ubir.buffalo.edu/oai/request"},"display":{"title":"Privacy-Preserving Outsourcing of Magnetic Resonance Imaging Reconstruction","abstract":"Ph.D.","abstract_html":"Ph.D.","abstract_has_math":false,"creators":["Shan, Zihao; 0000-0001-9219-4372"],"institution":"State University of New York at Buffalo","degree_name":null,"degree_level":null,"degree_discipline":null,"degree_department":null,"school":null,"contributors":["Ren, Kui","Computer Science and Engineering"],"advisors":[],"committee_chairs":[],"committee_members":[],"year":2022,"date_issued":"2022-06-21T15:47:15Z","date_published":"2022-06-21T15:47:15Z","updated_at":"2026-07-27T19:05:30Z","subjects":["computer science"],"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/10477/84034","outbound_label":"Handle","outbound_source":"dc:identifier"},"metadata_groups":[{"id":"people","label":"People","entries":[{"key":"dc:contributor","label":"Contributor","values":["Ren, Kui","Computer Science and Engineering"]},{"key":"dc:creator","label":"Author","values":["Shan, Zihao; 0000-0001-9219-4372"]}]},{"id":"academic_context","label":"Academic Context","entries":[{"key":"dc:date","label":"Dc Date","values":["2022-06-21T15:47:15Z","2020"]},{"key":"dc:publisher","label":"Institution","values":["State University of New York at Buffalo"]},{"key":"dc:type","label":"Dc Type","values":["Text","Dissertation"]}]},{"id":"subjects_keywords","label":"Subjects and Keywords","entries":[{"key":"dc:subject","label":"Dc Subject","values":["computer science"]}]},{"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/10477/84034"]}]},{"id":"additional","label":"Additional Metadata","entries":[{"key":"dc:description","label":"Description","values":["Ph.D.","Since the broad application of Magnetic Resonance Imaging (MRI) in the clinic, faster data acquisition against the physical and physiological constraints has been the subject of intense experimental research and theoretical work. Among these efforts, parallel imaging uses multiple receiver elements to exploit the explicit redundancy of the MR data, which can achieve an aggressive under-sampling rate and hence reduced acquisition time. Unwillingly, it leads to the time-consuming post-processing procedure of image reconstruction instead. In this thesis, we propose to leverage the benefit of cloud computing to address the speeding issue. Based on a pay-per-use manner, a clinic with low computational power can easily outsource the computational-intensive tasks of image reconstruction to Cloud Service Providers (CSPs). However, the privacy concerns with outsourcing patients' private data to public cloud servers are ignited and hinder those practitioners from enjoying the benefits of cloud computing. Specifically, we explore the problem of privacy-preserving outsourcing the image reconstruction process in a calibration-less parallel imaging reconstruction method, termed simultaneous auto-calibrating and k-space estimation (SAKE). SAKE is one of the state-of-art algorithms in clinical implementation, which structure an under-sampled, multi-channel data-set in the k-space domain into a single data matrix. The reconstruction can then be formulated as a structured low-rank matrix completion problem. This thesis is seeking to enable a clinic to outsource the most computationally-intensive tasks in SAKE of the resource-abundant cloud servers, taking consideration of both the factors of security and efficiency. In particular, two different protocols are put forward in SecSAKE, with extra emphasis on privacy and efficiency, respectively. The first protocol we propose can enforce a low-complexity matrix transformation over the data in the k-space domain on the clinic end. The clinic can then outsource the transformed data to the cloud and then harness the cloud server to perform iterative computation tasks. The corresponding security analysis shows that the outsourced MRI data are computationally indistinguishable under Chosen Plaintext Attack (CPA) under our assumption. The second protocol provides an alternative model that pursues efficiency by leveraging the architectures of multiple non-colluding cloud servers instead. This protocol can primarily reduce the computational complexity on the clinic side since it only needs to perform a one-round data transformation to retrieve the reconstructed MRI data. During the entire functional computation part, the clinic can stay offline with more flexibility. We conduct thorough privacy and efficiency analysis and extensive experiments over real-world image benchmark to evaluate the performance of the proposed designs. Compared with the original SAKE, the experimental results demonstrate that the proposed privacy-preserving mechanism can provide significant reconstruction time savings while achieving comparative performance on the quality of reconstructed images.In conclusion, we close with a brief discussion of future research directions and then speculate about further approaches to preserving the data privacy in related medical applications, along with the potential obstacles on the road on achieving such objectives that we may encounter.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"]},{"key":"dc:format","label":"Dc Format","values":["application/pdf"]},{"key":"dc:title","label":"Title","values":["Privacy-Preserving Outsourcing of Magnetic Resonance Imaging Reconstruction"]}]}],"canonical_facts":{"dc:contributor":["Ren, Kui","Computer Science and Engineering"],"dc:creator":["Shan, Zihao; 0000-0001-9219-4372"],"dc:date":["2022-06-21T15:47:15Z","2020"],"dc:description":["Ph.D.","Since the broad application of Magnetic Resonance Imaging (MRI) in the clinic, faster data acquisition against the physical and physiological constraints has been the subject of intense experimental research and theoretical work. Among these efforts, parallel imaging uses multiple receiver elements to exploit the explicit redundancy of the MR data, which can achieve an aggressive under-sampling rate and hence reduced acquisition time. Unwillingly, it leads to the time-consuming post-processing procedure of image reconstruction instead. In this thesis, we propose to leverage the benefit of cloud computing to address the speeding issue. Based on a pay-per-use manner, a clinic with low computational power can easily outsource the computational-intensive tasks of image reconstruction to Cloud Service Providers (CSPs). However, the privacy concerns with outsourcing patients' private data to public cloud servers are ignited and hinder those practitioners from enjoying the benefits of cloud computing. Specifically, we explore the problem of privacy-preserving outsourcing the image reconstruction process in a calibration-less parallel imaging reconstruction method, termed simultaneous auto-calibrating and k-space estimation (SAKE). SAKE is one of the state-of-art algorithms in clinical implementation, which structure an under-sampled, multi-channel data-set in the k-space domain into a single data matrix. The reconstruction can then be formulated as a structured low-rank matrix completion problem. This thesis is seeking to enable a clinic to outsource the most computationally-intensive tasks in SAKE of the resource-abundant cloud servers, taking consideration of both the factors of security and efficiency. In particular, two different protocols are put forward in SecSAKE, with extra emphasis on privacy and efficiency, respectively. The first protocol we propose can enforce a low-complexity matrix transformation over the data in the k-space domain on the clinic end. The clinic can then outsource the transformed data to the cloud and then harness the cloud server to perform iterative computation tasks. The corresponding security analysis shows that the outsourced MRI data are computationally indistinguishable under Chosen Plaintext Attack (CPA) under our assumption. The second protocol provides an alternative model that pursues efficiency by leveraging the architectures of multiple non-colluding cloud servers instead. This protocol can primarily reduce the computational complexity on the clinic side since it only needs to perform a one-round data transformation to retrieve the reconstructed MRI data. During the entire functional computation part, the clinic can stay offline with more flexibility. We conduct thorough privacy and efficiency analysis and extensive experiments over real-world image benchmark to evaluate the performance of the proposed designs. Compared with the original SAKE, the experimental results demonstrate that the proposed privacy-preserving mechanism can provide significant reconstruction time savings while achieving comparative performance on the quality of reconstructed images.In conclusion, we close with a brief discussion of future research directions and then speculate about further approaches to preserving the data privacy in related medical applications, along with the potential obstacles on the road on achieving such objectives that we may encounter.","**To request an accessible version of the file(s) associated with this item, contact library@buffalo.edu. Please include the item's persistent URL [http://hdl.handle.net/. . .] in your request.**"],"dc:format":["application/pdf"],"dc:identifier":["http://hdl.handle.net/10477/84034"],"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":["computer science"],"dc:title":["Privacy-Preserving Outsourcing of Magnetic Resonance Imaging Reconstruction"],"dc:type":["Text","Dissertation"]},"updated_at":"2026-07-27T19:05:30Z"}