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Shanshan Shan
  Shanshan Shan , Tenure-track Associate Professor
Outstanding Young Scholar of Soochow University
Email: ssshan[AT]suda.edu.cn
Address: 199 Renai Road, Suzhou 215123
   Dr Shan studied at Nanjing University of Science and Technology, China during 2011-2015 and won a bachelor degree on Communication Engineering. Since 2017, she was sponsored by the China Scholarship Council to study in biomedical engineering at the University of Queensland, Australia and won a PhD degree in 2021. She then worked as a postdoctoral research fellow at the University of Sydney until Aug 2022. She joined Soochow University as an outstanding young scholar in Nov 2022. Her research focuses on MRI image distortion correction, nonlinear image reconstruction and deep learning fast imaging algorithms, aiming to provide accurate and fast MRI images for MRI-guided radiotherapy treatments. Dr Shan received the Best in Physics Award of the American Association of Physicists in Medicine (AAPM) in 2022, Dean’s Award Recommendation for Outstanding HDR Thesis from the University of Queensland in 2021, Richard Jago Memorial Prize in 2019 and multiple trainee stipends from the International Society for Magnetic Resonance in Medicine (ISMRM). Dr Shan was the leading person of fast distortion correction project on Australian MRI-Linac program (https://ausmrigrt.com/the-university-of-sydney/). Dr Shan has published around ten high-quality journal papers and twenty conference abstracts.
  Education background  
    2022.11 - present Tenure-track Associate Professor, Soochow University, China
    2020.11 - 2022.08 Postdoctoral Research Associate, University of Sydney, Australia
    2017.01 - 2021.07 PhD Degree in Biomedical Engineering, University of Queensland, Australia
    2013.08 - 2014.02 Exchange student in Electrical Engineering, Arizona State University, USA
    2011.09 - 2015.06 Bachelor Degree in Communication Engineering, Nanjing University of Science and Technology, China
       MRI image distortion correction
       MRI fast imaging algorithms
       Deep learning
       Motion correction
      MRI-guided radiotherapy treatments
    1. ReUINet: A fast GNL distortion correction approach on a 1.0 T MRI-Linac scanner
Shan S, Li M, Li M, Tang F, Crozier S, Liu F.
Medical Physics. 2021, Mar 24.
2. Gradient Field Deviation (GFD) Correction Using a Hybrid-Norm Approach With Wavelet Sub-Band Dependent Regularization: Implementation for Radial MRI at 9.4 T[J]
Shan S, Li M, Tang F, et al.
IEEE Transactions on Biomedical Engineering, 2019, 66(9): 2693-2701.
3. Geometric Distortion Characterisation and Correction for the 1.0 T Australian MRI‐Linac System Using an Inverse Electromagnetic Method[J]
Shan S, Liney G P, Tang F, et al.
Medical Physics, 2019.
4. Fast Geometrical Distortion Correction Using a Fully Connected Neural Network: Implementation for the 1 Tesla MRI-Linac System
Li, M*., Shan, S*., Chandra, S, et al.
Medical Physics, 2020. * Li, M and Shan, S contributed equally to this paper.
5. MRI-guided radiation therapy: Opportunities and challenges
Paul J Keall, Caterina Brighi, Carri Glide-Hurst, Gary Liney, Paul Z. Y. Liu, Suzanne Lydiard, Chiara Paganelli, Trang Pham, Shanshan Shan, Alison C Tree, Uulke van der Heide, David E. J. Waddington, Brendan Whelan.
Nature Reviews Clinical Oncology, 2022, Apr 19:1-3.
6. Chaotic Compressive Sampling Matrix: Where Sensing Architecture Meets Sinusoidal Iterator
Gan, H., Xiao, S., Zhang, Z., Shan, S., & Gao, Y.
Circuits, Systems, and Signal Processing, 2020, 39(3), 1581-1602.
7. Waddington DE. Distortion-Corrected Image Reconstruction with Deep Learning on an MRI-Linac
Shan S, Gao Y, Liu PZ, Whelan B, Sun H, Dong B, Liu F
arXiv preprint arXiv:2205.10993, 2022, May 23.
8. Motion-free Image Reconstruction using Unrolling Network on an MRI-Linac
Shan S, Gang Y, Liu P, et al.
July 2022 In MEDICAL PHYSICS (Vol. 48, No. 6). 111 RIVER ST, HOBOKEN 07030-5774, NJ USA: WILEY. (Best in Physics Award,top 0.5%)