2023 ISMRM and ISMRT Annual Meeting and Exhibition
3 June 2023 – 8 June 2023 · Toronto, Canada
1370 ISMRM Abstract

SRNR: Training neural networks forSuper-Resolution MRI usingNoisy high-resolutionReference data

1370 · ML/AI for Analysis, Post-Processing, Disease Diagnosis & Prediction · Thursday, 8 June, 4:00 PM–6:00 PM · 701B
Keywords: Data Analysis Machine Learning/Artificial Intelligence
Accepted
Jiaxin Xiao 1, Zihan Li2, Berkin Bilgic3,4, Jonathan R Polimeni3,4, Susie Huang3,4, Qiyuan Tian3,4
1Department of Electronic Engineering, Tsinghua University, Beijing, China
2Department of Biomedical Engineering, Tsinghua University, Beijing, China
3Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Boston, United States of America
4Harvard Medical School, Boston, United States of America
Presenting Author: Jiaxin Xiao
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References

1. Pham C-H, Ducournau A, Fablet R, Rousseau F. Brain MRI super-resolution using deep 3D convolutional networks. IEEE; 2017:197-200.
2. Chaudhari AS, Fang Z, Kogan F, et al. Super ‐ resolution musculoskeletal MRI using deep learning. Magnetic resonance in medicine . 2018;80(5):2139-2154.
3. Chen Y, Shi F, Christodoulou AG, Xie Y, Zhou Z, Li D. Efficient and accurate MRI super-resolution using a generative adversarial network and 3D multi-level densely connected network. Springer; 2018:91-99.
4. Tian Q, Bilgic B, Fan Q, et al. Improving in vivo human cerebral cortical surface reconstruction using data-driven super-resolution. Cerebral Cortex . 2021;31(1):463-482.
5. Lehtinen J, Munkberg J, Hasselgren J, et al. Noise2Noise: Learning image restoration without clean data. arXiv preprint arXiv:180304189 . 2018; 6. Glasser MF, Sotiropoulos SN, Wilson JA, et al. The minimal preprocessing pipelines for the Human Connectome Project. Neuroimage . 2013;80:105-124.
6. Glasser MF, Smith SM, Marcus DS, et al. The human connectome project's neuroimaging approach. Nature neuroscience . 2016;19(9):1175-1187.
7. Reuter M, Rosas HD, Fischl B. Highly accurate inverse consistent registration: a robust approach. Neuroimage . 2010;53(4):1181-1196.
8. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis: II: inflation, flattening, and a surface-based coordinate system. NeuroImage . 1999;9(2):195-207.
9. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis: I. Segmentation and surface reconstruction. NeuroImage . 1999;9(2):179-194.
10. Fischl B. FreeSurfer. NeuroImage . 2012;62(2):774-781.

Cite this abstract

http://echo.ismrm.org/p/ISMRM2023/1370