Literature DB >> 29399869

Deep learning with domain adaptation for accelerated projection-reconstruction MR.

Yoseob Han1, Jaejun Yoo1, Hak Hee Kim2, Hee Jung Shin2, Kyunghyun Sung3, Jong Chul Ye1.   

Abstract

Keywords:  compressed sensing; convolutional neural network; deep learning; domain adaptation; projection reconstruction MRI

Mesh：

Year:  2018        PMID： 29399869     DOI： 10.1002/mrm.27106

Source DB:  PubMed          Journal:  Magn Reson Med        ISSN： 0740-3194            Impact factor:   4.668


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  48 in total

1.  Self-supervised learning of physics-guided reconstruction neural networks without fully sampled reference data.

Authors:  Burhaneddin Yaman; Seyed Amir Hossein Hosseini; Steen Moeller; Jutta Ellermann; Kâmil Uğurbil; Mehmet Akçakaya
Journal:  Magn Reson Med       Date:  2020-07-02       Impact factor: 4.668

Review 2.  Improvement of image quality at CT and MRI using deep learning.

Authors:  Toru Higaki; Yuko Nakamura; Fuminari Tatsugami; Takeshi Nakaura; Kazuo Awai
Journal:  Jpn J Radiol       Date:  2018-11-29       Impact factor: 2.374

3.  A multi-scale residual network for accelerated radial MR parameter mapping.

Authors:  Zhiyang Fu; Sagar Mandava; Mahesh B Keerthivasan; Zhitao Li; Kevin Johnson; Diego R Martin; Maria I Altbach; Ali Bilgin
Journal:  Magn Reson Imaging       Date:  2020-09-01       Impact factor: 2.546

4.  Prospective acceleration of parallel RF transmission-based 3D chemical exchange saturation transfer imaging with compressed sensing.

Authors:  Hye-Young Heo; Xiang Xu; Shanshan Jiang; Yansong Zhao; Jochen Keupp; Kristin J Redmond; John Laterra; Peter C M van Zijl; Jinyuan Zhou
Journal:  Magn Reson Med       Date:  2019-06-17       Impact factor: 4.668

5.  Dense Recurrent Neural Networks for Accelerated MRI: History-Cognizant Unrolling of Optimization Algorithms.

Authors:  Seyed Amir Hossein Hosseini; Burhaneddin Yaman; Steen Moeller; Mingyi Hong; Mehmet Akçakaya
Journal:  IEEE J Sel Top Signal Process       Date:  2020-06-17       Impact factor: 6.856

6.  Deep Magnetic Resonance Image Reconstruction: Inverse Problems Meet Neural Networks.

Authors:  Dong Liang; Jing Cheng; Ziwen Ke; Leslie Ying
Journal:  IEEE Signal Process Mag       Date:  2020-01-20       Impact factor: 12.551

7.  SANTIS: Sampling-Augmented Neural neTwork with Incoherent Structure for MR image reconstruction.

Authors:  Fang Liu; Alexey Samsonov; Lihua Chen; Richard Kijowski; Li Feng
Journal:  Magn Reson Med       Date:  2019-06-05       Impact factor: 4.668

8.  Scan-specific robust artificial-neural-networks for k-space interpolation (RAKI) reconstruction: Database-free deep learning for fast imaging.

Authors:  Mehmet Akçakaya; Steen Moeller; Sebastian Weingärtner; Kâmil Uğurbil
Journal:  Magn Reson Med       Date:  2018-09-18       Impact factor: 4.668

9.  Image Reconstruction: From Sparsity to Data-adaptive Methods and Machine Learning.

Authors:  Saiprasad Ravishankar; Jong Chul Ye; Jeffrey A Fessler
Journal:  Proc IEEE Inst Electr Electron Eng       Date:  2019-09-19       Impact factor: 10.961

10.  Learning a variational network for reconstruction of accelerated MRI data.

Authors:  Kerstin Hammernik; Teresa Klatzer; Erich Kobler; Michael P Recht; Daniel K Sodickson; Thomas Pock; Florian Knoll
Journal:  Magn Reson Med       Date:  2017-11-08       Impact factor: 4.668
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