Deep learning‐based method for reducing residual motion effects in diffusion parameter estimation-Reference-Cited by-同舟云学术

Deep learning‐based method for reducing residual motion effects in diffusion parameter estimation

Published:2020-10-15 Issue:4 Volume:85 Page:2278-2293
ISSN:0740-3194
Container-title:Magnetic Resonance in Medicine
language:en
Short-container-title:Magn. Reson. Med.

Author:

Gong Ting¹²,Tong Qiqi¹,Li Zhiwei³,He Hongjian¹^ORCID,Zhang Hui²,Zhong Jianhui¹⁴

Affiliation:

1. Center for Brain Imaging Science and Technology Key Laboratory for Biomedical Engineering of Ministry of Education College of Biomedical Engineering and Instrumental Science Zhejiang University Hangzhou China

2. Department of Computer Science & Centre for Medical Image Computing University College London London UK

3. Department of Instrument Science & Technology Zhejiang University Hangzhou China

4. Department of Imaging Sciences University of Rochester Rochester NY USA

Funder

National Natural Science Foundation of China

Publisher

Wiley

Subject

Radiology, Nuclear Medicine and imaging

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/mrm.28544

Reference50 articles.

1. Estimation of the Effective Self-Diffusion Tensor from the NMR Spin Echo

2. Diffusional kurtosis imaging: The quantification of non-gaussian water diffusion by means of magnetic resonance imaging

3. MRI quantification of non-Gaussian water diffusion by kurtosis analysis

4. NODDI: Practical in vivo neurite orientation dispersion and density imaging of the human brain

5. Composite hindered and restricted model of diffusion (CHARMED) MR imaging of the human brain

Cited by 8 articles. 订阅此论文施引文献订阅此论文施引文献，注册后可以免费订阅5篇论文的施引文献，订阅后可以查看论文全部施引文献

1. Deep learning-based compressed SENSE improved diffusion-weighted image quality and liver cancer detection: A prospective study;Magnetic Resonance Imaging;2024-09

2. SwinDTI: swin transformer-based generalized fast estimation of diffusion tensor parameters from sparse data;Neural Computing and Applications;2023-12-02

3. Deep Learning for Image Enhancement and Correction in Magnetic Resonance Imaging—State-of-the-Art and Challenges;Journal of Digital Imaging;2022-11-02

4. Potential of Deep Learning in Quantitative Magnetic Resonance Imaging for Personalized Radiotherapy;Seminars in Radiation Oncology;2022-10

5. Effect of number of diffusion‐encoding directions in diffusion metrics of 5‐year‐olds using tract‐based spatial statistical analysis;European Journal of Neuroscience;2022-08-15