Chang Gao, Ph.D.

Research

My Ph.D. research focused on leveraging deep learning networks to push the boundaries of MRI image reconstruction.

Project Highlights

3D Unrolled Network for Cardiac Cine MRI Reconstruction
UCLA | 2022 – 2023 | Paper | Presented at ISMRM 2022 & ISMRM 2023

  • Developed a spatiotemporal unrolled network to reconstruct cine images (VSNet)
  • Achieved 10-16× image accelerations
  • Network trained using contrast-free images can generalize to ferumoxytol-enhanced images

K-Space Transformer Network for Radial MRI reconstruction
UCLA | 2021 – 2023 | Paper | Presented at ISMRM 2022

  • Proposed to rearrange the radial spokes to sequential data based on the chronological order of acquisition
  • Developed a projection-based k-space transformer network (PKT) to predict unacquired k-space data
  • Achieved 4× acceleration of 2D radial acquisition and state-of-the-art performance

Artifact Reduction using a Deep Adversarial Network
UCLA | 2019 – 2021 | Paper | Presented at ISMRM 2021 & ISMRM 2022

  • Developed a generative adversarial network (GAN) to reduce undersampling artifacts and preserve image sharpness
  • Achieved 3-5× accelerations and best image quality and sharpness

Teaching / Invited Talks

Guest Lecture: AI in MRI image reconstruction and image analysis

  • Cedars-Sinai Graduate School of Biomedical Sciences, Summer 2025
  • MRM 533 Advanced Imaging and AI

Guest Lectures: MRI sequence programming in Siemens IDEA environment

  • Cedars-Sinai Graduate School of Biomedical Sciences, Spring 2025
  • MRM 624 MR Technical Developments & Advanced Imaging

Invited Talk: Clinical Solutions in Body MRI – Siemens Perspective

Publications

  • Zhong X, Nickel MD, Kannengiesser SAR, Dale BM, Han F, Gao C, et al. Accelerated free-breathing liver fat and R2* quantification using multi-echo stack-of-radial MRI with motion-resolved multidimensional regularized reconstruction: Initial retrospective evaluation. Magn Reson Med. 2024; 92(3): 1149-1161. doi: 10.1002/mrm.30117
  • Ming Z, Pogosyan A, Gao C, et al. ECG-free cine MRI with data-driven clustering of cardiac motion for quantification of ventricular function. NMR in Biomedicine. 2024; 37(4):e5091. doi:10.1002/nbm.5091
  • Gao C, Ming Z, Nguyen KL, et al. Ferumoxytol-Enhanced Cardiac Cine MRI Reconstruction Using a Variable-Splitting Spatiotemporal Network. J Magn Reson Imaging. 2024 Mar 4. doi: 10.1002/jmri.29295
  • Gao C, Ghodrati V, Shih SF, et al. Undersampling artifact reduction for free-breathing 3D stack-of-radial MRI based on a deep adversarial learning network. Magn Reson Imaging. 2023 Jan;95:70-79. doi: 10.1016/j.mri.2022.10.010
  • Gao C, Shih SF, Finn JP, Zhong X (2022). A Projection-Based K-space Transformer Network for Undersampled Radial MRI Reconstruction with Limited Training Subjects. In: Wang, L., Dou, Q., Fletcher, P.T., Speidel, S., Li, S. (eds) Medical Image Computing and Computer Assisted Intervention – MICCAI 2022. MICCAI 2022. Lecture Notes in Computer Science, vol 13436. Springer, Cham. https://doi.org/10.1007/978-3-031-16446-0_69
  • Zhong X, Armstrong T, Gao C, et al. Accelerated k-space shift calibration for free-breathing stack-of-radial MRI quantification of liver fat and R2*. Magn Reson Med. 2021; 87: 281–291. https://doi.org/10.1002/mrm.28981

For a complete list, please visit my Google Scholar profile.

Patents

  • V Ghodrati, C Gao, P Hu, X Zhong, J Wetzl, J Pang. Method and apparatus for accelerated acquisition and reconstruction of cine mri using a deep learning based convolutional neural network. US Patent App. 17/814,877, 2024
  • C Gao, JP Finn, X Zhong. Method and apparatus for accelerated acquisition and artifact reduction of undersampled mri using a k-space transformer network. US Patent App. 17/934,618, 2023
  • P Hu, X Zhong, C Gao. Method and system for accelerated acquisition and artifact reduction of undersampled mri using a deep learning based 3d generative adversarial network. US Patent 12,196,833