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Conference Paper: Gibbs-Ringing Artifact reduction in MR images with varying sampling levels Via a Single Convolutional Neural Network
| Title | Gibbs-Ringing Artifact reduction in MR images with varying sampling levels Via a Single Convolutional Neural Network |
|---|---|
| Authors | |
| Issue Date | 2019 |
| Publisher | International Society for Magnetic Resonance in Medicine. |
| Citation | The 27th Annual Meeting & Exhibition of International Society for Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 11-16 May 2019 , paper 4856 How to Cite? |
| Abstract | Gibbs-ringing artifact is caused by the insufficient sampling of the high frequency data. And in clinical practice, the appearance of ringing artifact, i.e. the real sampling level, is not accurately obtained. To address this problem, a single convolutional neural network (CNN) has been trained for reducing Gibbs-ringing artifact in MR images under varying sampling levels. The experimental results demonstrate that Gibbs-ringing artifact can be effectively reduced by the proposed method without introducing noticeable blurring. |
| Description | Digital Poster Session: Machine Learning for Image Enhancement, Quality Assessment & Synthetic Image Generation: Acquisition, Reconstruction & Analysis - no. 4856 |
| Persistent Identifier | http://hdl.handle.net/10722/278726 |
| DC Field | Value | Language |
|---|---|---|
| dc.contributor.author | Ruan, G | - |
| dc.contributor.author | Zhang, Q | - |
| dc.contributor.author | Liu, B | - |
| dc.contributor.author | Yang, W | - |
| dc.contributor.author | Mei, Y | - |
| dc.contributor.author | Wu, EX | - |
| dc.contributor.author | Feng, Y | - |
| dc.date.accessioned | 2019-10-21T02:12:53Z | - |
| dc.date.available | 2019-10-21T02:12:53Z | - |
| dc.date.issued | 2019 | - |
| dc.identifier.citation | The 27th Annual Meeting & Exhibition of International Society for Magnetic Resonance in Medicine (ISMRM), Montreal, Canada, 11-16 May 2019 , paper 4856 | - |
| dc.identifier.uri | http://hdl.handle.net/10722/278726 | - |
| dc.description | Digital Poster Session: Machine Learning for Image Enhancement, Quality Assessment & Synthetic Image Generation: Acquisition, Reconstruction & Analysis - no. 4856 | - |
| dc.description.abstract | Gibbs-ringing artifact is caused by the insufficient sampling of the high frequency data. And in clinical practice, the appearance of ringing artifact, i.e. the real sampling level, is not accurately obtained. To address this problem, a single convolutional neural network (CNN) has been trained for reducing Gibbs-ringing artifact in MR images under varying sampling levels. The experimental results demonstrate that Gibbs-ringing artifact can be effectively reduced by the proposed method without introducing noticeable blurring. | - |
| dc.language | eng | - |
| dc.publisher | International Society for Magnetic Resonance in Medicine. | - |
| dc.relation.ispartof | 2019 Proceedings of International Society for Magnetic Resonance in Medicine (ISMRM) Annual Meeting | - |
| dc.title | Gibbs-Ringing Artifact reduction in MR images with varying sampling levels Via a Single 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 | 307717 | - |
| dc.identifier.spage | p4856 | - |
| dc.identifier.epage | p4856 | - |