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Conference Paper: MRI Reconstruction Using Deep Bayesian Inference

TitleMRI Reconstruction Using Deep Bayesian Inference
Authors
Luo, GCao, P
Issue Date2020
PublisherInternational Society for Magnetic Resonance in Medicine (ISMRM)).
Citation
28th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), Virtual Conference, 8-14 August 2020 How to Cite?
AbstractA deep neural network provides a practical approach to extract features from existing image database. For MRI reconstruction, we presented a novel method to take advantage of such feature extraction by Bayesian inference. The innovation of this work includes 1) the definition of image prior based on an autoregressive network, and 2) the method uniquely permits the flexibility and generality and caters for changing various MRI acquisition settings, such as the number of radio-frequency coils, and matrix size or spatial resolution.
DescriptionOral - Acquisition, Reconstruction & Analysis - Scientific Session O-57: Machine Learning for Image Reconstruction - no. 0996
Persistent Identifierhttp://hdl.handle.net/10722/285355

 

DC FieldValueLanguage
dc.contributor.authorLuo, G-
dc.contributor.authorCao, P-
dc.date.accessioned2020-08-18T03:52:42Z-
dc.date.available2020-08-18T03:52:42Z-
dc.date.issued2020-
dc.identifier.citation28th Annual Meeting & Exhibition of the International Society for Magnetic Resonance in Medicine (ISMRM), Virtual Conference, 8-14 August 2020-
dc.identifier.urihttp://hdl.handle.net/10722/285355-
dc.descriptionOral - Acquisition, Reconstruction & Analysis - Scientific Session O-57: Machine Learning for Image Reconstruction - no. 0996-
dc.description.abstractA deep neural network provides a practical approach to extract features from existing image database. For MRI reconstruction, we presented a novel method to take advantage of such feature extraction by Bayesian inference. The innovation of this work includes 1) the definition of image prior based on an autoregressive network, and 2) the method uniquely permits the flexibility and generality and caters for changing various MRI acquisition settings, such as the number of radio-frequency coils, and matrix size or spatial resolution.-
dc.languageeng-
dc.publisherInternational Society for Magnetic Resonance in Medicine (ISMRM)). -
dc.relation.ispartofInternational Society for Magnetic Resonance in Medicine (ISMRM) Virtual Conference-
dc.titleMRI Reconstruction Using Deep Bayesian Inference-
dc.typeConference_Paper-
dc.identifier.emailCao, P: caopeng1@hku.hk-
dc.identifier.authorityCao, P=rp02474-
dc.identifier.hkuros312967-

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