NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions-Reference-Cited by-同舟云学术

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NVAM-Net: deep learning networks for reconstructing high-quality fiber orientation distributions

Published:2024-04-02 Issue:7 Volume:66 Page:1177-1187
ISSN:0028-3940
Container-title:Neuroradiology
language:en
Short-container-title:Neuroradiology

Author:

Li Jiahao,Ai Lingmei^ORCID,Yao Ruoxia

Funder

National Natural Science Foundation of China

National Science and Technology Major Project

Excellent Graduate Training Program of Shaanxi Normal University

Natural Science Basic Research Program of Shaanxi Province

Publisher

Springer Science and Business Media LLC

Link

https://link.springer.com/content/pdf/10.1007/s00234-024-03341-y.pdf

Reference31 articles.

1. Jeurissen B, Tournier J-D, Dhollander T, Connelly A, Sijbers J (2014) Multitissue constrained spherical deconvolution for improved analysis of multi-shell diffusion mri data. Neuroimage 103:411–426

2. Schmahmann JD, Pandya DN, Wang R, Dai G, D’Arceuil HE, De Crespigny AJ, Wedeen VJ (2007) Association fibre pathways of the brain: parallel observations from diffusion spectrum imaging and autoradiography. Brain 130(3):630–653

3. Scherrer B, Gholipour A, Warfield SK (2011) Super-resolution in diffusion-weighted imaging. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2011: 14th International Conference, Toronto, Canada, September 18–22, 2011, Proceedings, Part II 14. Springer, Berlin Heidelberg, pp 124–132. https://doi.org/10.1007/978-3-642-23629-7_16

4. Scherrer B, Gholipour A, Warfield SK (2012) Super-resolution reconstruction to increase the spatial resolution of diffusion weighted images from orthogonal anisotropic acquisitions. Med Image Anal 16(7):1465–1476

5. Alexander DC, Zikic D, Zhang J, Zhang H, Criminisi A (2014) Image quality transfer via random forest regression: applications in diffusion MRI. In: Medical Image Computing and Computer-Assisted Intervention–MICCAI 2014: 17th International Conference, Boston, MA, USA, September 14–18, 2014, Proceedings, Part III 17. Springer International Publishing, pp 225–232. https://doi.org/10.1007/978-3-319-10443-0_29

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