- Deep Learning in MR Motion Correction:a Brief Review and a New Motion Simulation Tool (view2Dmotion)
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Lee S, Jung S, Jung KJ, Kim DH
- KMID: 2510449
- Investig Magn Reson Imaging.
- 2020 Dec;24(4):196-206.
- doi: 10.13104/imri.2020.24.4.196
With the development of deep-learning techniques, the application of deep learning in MR imaging processing seems to be growing. Accordingly, deep learning has also been introduced in motion correction and... -
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- Cascaded Residual Dense Networks for Dynamic MR Imaging with Edge-Enhanced Loss Constraint
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Ke Z, Zhu Y, Liang D
- KMID: 2510451
- Investig Magn Reson Imaging.
- 2020 Dec;24(4):214-222.
- doi: 10.13104/imri.2020.24.4.214
Dynamic magnetic resonance (MR) imaging has generated great research interest, because it can provide both spatial and temporal information for clinical diagnosis. However, slow imaging speed or long scanning time is... -
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- A Comparative Study of Unsupervised Deep Learning Methods for MRI Reconstruction
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He Z, Quan C, Wang S, Zhu Y, Zhang M, Zhu Y, Liu Q
- KMID: 2510448
- Investig Magn Reson Imaging.
- 2020 Dec;24(4):179-195.
- doi: 10.13104/imri.2020.24.4.179
Recently, unsupervised deep learning methods have shown great potential in image processing. Compared with a large-amount demand for paired training data of supervised methods with a specific task, unsupervised methods... -
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- A Self-Supervised Learning Framework for Under-Sampling Pattern Design Using Graph Convolution Network
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Li Y, Chen H
- KMID: 2510453
- Investig Magn Reson Imaging.
- 2020 Dec;24(4):232-240.
- doi: 10.13104/imri.2020.24.4.223
Purpose: To generate the under-sampling pattern using a self-supervised learning framework based on a graph convolutional network. Materials and Methods: We first decoded the k-space data into the graph and put... -
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- Unsupervised Deformable Image Registration Using Polyphase UNet for 3D Brain MRI Volumes
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Martin AD, Kim B, Ye JC
- KMID: 2510452
- Investig Magn Reson Imaging.
- 2020 Dec;24(4):223-231.
- doi: 10.13104/imri.2020.24.4.223
Purpose: Image registration is a fundamental task in various medical imaging studies and clinical image analyses, such as comparison of patient data with anatomical structures. In order to solve the... -
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- qMTNet+ , an Improved qMTNet with Residual Connection for Accelerated Quantitative Magnetization Transfer Imaging
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Luu HM, Kim DH, Choi SH, Park SH
- KMID: 2510454
- Investig Magn Reson Imaging.
- 2020 Dec;24(4):241-251.
- doi: 10.13104/imri.2020.24.4.241
Purpose: To develop qMTNet+ , an improved version of a recently proposed neural network called qMTNet, to accelerate quantitative magnetization transfer (qMT) imaging acquisition and processing. Materials and Methods:... -
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- Exploring Generalization Capacity of Artificial Neural Network for Myelin Water Imaging
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Lee J, Choi JY, Shin D, Kim EY, Oh SH, Lee J
- KMID: 2510450
- Investig Magn Reson Imaging.
- 2020 Dec;24(4):207-213.
- doi: 10.13104/imri.2020.24.4.207
Purpose: To understand the effects of datasets with various parameters on pretrained network performance, the generalization capacity of the artificial neural network for myelin water imaging (ANN-MWI) is explored by... -
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