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Conference Paper: Gibbs-Ringing Artifact Reduction in MRI via Machine Learning Using Convolutional Neural Network
Title | Gibbs-Ringing Artifact Reduction in MRI via Machine Learning Using Convolutional Neural Network |
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Authors | |
Issue Date | 2018 |
Publisher | International Society for Magnetic Resonance in Medicine. |
Citation | Proceedings of International Society for Magnetic Resonance in Medicine & The European Society for Magnetic Resonance in Medicine and Biology (ISMRM-ESMRMB) Joint Annual Meeting, Paris, France, 16-21 June 2018, abstract no. 0429 How to Cite? |
Abstract | The Gibbs-ringing artifact is caused by the insufficient sampling of the high frequency data. Existing methods generally exploit smooth constraints to reduce intensity oscillations near high-contrast boundaries but at the cost of blurring details. This work presents a convolutional neural network (CNN) method that maps ringing images to their ringing-free counterparts for Gibbs-ringing artifact removal in MRI. The experimental results demonstrate that the proposed method can effectively remove Gibbs-ringing without introducing noticeable blurring. |
Description | e-Poster Session: Machine Learning Unleashed - Acquisition, Reconstruction & Analysis - Abstract #0429 |
Persistent Identifier | http://hdl.handle.net/10722/261183 |
DC Field | Value | Language |
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dc.contributor.author | ZHANG, Q | - |
dc.contributor.author | RUAN, G | - |
dc.contributor.author | YANG, W | - |
dc.contributor.author | ZHAO, K | - |
dc.contributor.author | Wu, EX | - |
dc.contributor.author | FENG, Y | - |
dc.date.accessioned | 2018-09-14T08:53:52Z | - |
dc.date.available | 2018-09-14T08:53:52Z | - |
dc.date.issued | 2018 | - |
dc.identifier.citation | Proceedings of International Society for Magnetic Resonance in Medicine & The European Society for Magnetic Resonance in Medicine and Biology (ISMRM-ESMRMB) Joint Annual Meeting, Paris, France, 16-21 June 2018, abstract no. 0429 | - |
dc.identifier.uri | http://hdl.handle.net/10722/261183 | - |
dc.description | e-Poster Session: Machine Learning Unleashed - Acquisition, Reconstruction & Analysis - Abstract #0429 | - |
dc.description.abstract | The Gibbs-ringing artifact is caused by the insufficient sampling of the high frequency data. Existing methods generally exploit smooth constraints to reduce intensity oscillations near high-contrast boundaries but at the cost of blurring details. This work presents a convolutional neural network (CNN) method that maps ringing images to their ringing-free counterparts for Gibbs-ringing artifact removal in MRI. The experimental results demonstrate that the proposed method can effectively remove Gibbs-ringing without introducing noticeable blurring. | - |
dc.language | eng | - |
dc.publisher | International Society for Magnetic Resonance in Medicine. | - |
dc.relation.ispartof | ISMRM-ESMRMB Joint Annual Meeting 2018 | - |
dc.title | Gibbs-Ringing Artifact Reduction in MRI via Machine Learning Using Convolutional Neural Network | - |
dc.type | Conference_Paper | - |
dc.identifier.email | Wu, EX: ewu@eee.hku.hk | - |
dc.identifier.authority | Wu, EX=rp00193 | - |
dc.identifier.hkuros | 291504 | - |
dc.identifier.spage | abstract no. 0429 | - |
dc.identifier.epage | abstract no. 0429 | - |
dc.publisher.place | United States | - |