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Attention-based Convolutional Neural Network for MRI Gibbs-ringing Artifact Suppression
- Source :
- Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2. :34-1
- Publication Year :
- 2020
- Publisher :
- MONOMAX Limited Liability Company, 2020.
-
Abstract
- Gibbs-ringing artifact is a common artifact in MRI image processing. As MRI raw data is taken in a frequency domain, 2D in- verse discrete Fourier transform is applied to visualize data. Inability to take inverse Fourier transform of full spectrum (full k-space) leads to the insufficient sampling of the high frequency data and results in a well-known Gibbs phenomenon. It is worth to notice that truncation of high frequency information generates a significant blur, thus some techniques from other image restoration problems (for example, image deblur task) can be successfully used. We propose attention-based convolutional neural network for Gibbs-ringing reduction which is the extension of recently proposed GAS-CNN (Gibbs-ringing Artifact Suppression Convolutional Neural Network). Proposed method includes simplified non-linear mapping, amended by LRNN (Layer Recurrent Neural Network) refinement block with feature attention module, controlling the correlation between input and output tensors of the refinement unit. The research shows that the proposed post-processing refinement construction considerably simplifies the non-linear mapping.
- Subjects :
- business.industry
Computer science
0202 electrical engineering, electronic engineering, information engineering
020207 software engineering
020201 artificial intelligence & image processing
Pattern recognition
02 engineering and technology
Artificial intelligence
Ringing artifacts
business
Convolutional neural network
Subjects
Details
- Database :
- OpenAIRE
- Journal :
- Proceedings of the 30th International Conference on Computer Graphics and Machine Vision (GraphiCon 2020). Part 2
- Accession number :
- edsair.doi...........247e3649eb8d90efcc0ae09024dfd7a7