Jie Fu, PhD


Jie Fu received his B.S. in Physics from Shandong University in 2014 and M.S. in Physics from the University of British Columbia in 2016. He then obtained his Ph.D. in Physics and Biology in Medicine from UCLA in 2021. His doctoral research mainly focused on applying deep learning approaches for improving MR-guided radiation therapy.

Jie joined the Stanford University Medical Physics Residency in 2021.


Fu, J., Singhrao, K., Zhong, X., Gao, Y., Qi, S. X., Yang, Y., … Lewis, J. H. (2021). An automatic deep learning-based workflow for glioblastoma survival prediction using pre-operative multimodal MR images: a feasibility study. Advances in Radiation Oncology, 100746. doi:10.1016/j.adro.2021.100746

Fu, J., Singhrao, K., Qi, X. S., Yang, Y., Ruan, D., & Lewis, J. H. (2021). Three‐dimensional multipath DenseNet for improving automatic segmentation of glioblastoma on pre‐operative multimodal MR images. Medical Physics, 48(6), 2859–2866. doi:10.1002/mp.14800

Gao, Y., Ghodrati, V., Kalbasi, A., Fu, J., Ruan, D., Cao, M., … Yang, Y. (2021). Prediction of soft tissue sarcoma response to radiotherapy using longitudinal diffusion MRI and a deep neural network with generative adversarial network‐based data augmentation. Medical Physics, 48(6), 3262–3372. doi:10.1002/mp.14897

Singhrao, K., Fu, J., Parikh, N. R., Mikaeilian, A. G., Ruan, D., Kishan, A. U., & Lewis, J. H. (2020). A generative adversarial network‐based (GAN‐based) architecture for automatic fiducial marker detection in prostate MRI‐only radiotherapy simulation images. Medical Physics, 47(12), 6405–6413. doi:10.1002/mp.14498

Gao, Y., Kalbasi, A., Hsu, W., Ruan, D., Fu, J., Shao, J., … Yang, Y. (2020). Treatment effect prediction for sarcoma patients treated with preoperative radiotherapy using radiomics features from longitudinal diffusion-weighted MRIs. Physics in Medicine & Biology, 65(17), 175006. doi:10.1088/1361-6560/ab9e58

Singhrao, K., Fu, J., Gao, Y., Wu, H. H., Yang, Y., Hu, P., & Lewis, J. H. (2020). A generalized system of tissue-mimicking materials for computed tomography (CT) and magnetic resonance imaging (MRI). Physics in Medicine & Biology. doi:10.1088/1361-6560/ab86d4

Fu, J., Zhong, X., Li, N., Van Dams, R., Lewis, J., Sung, K., … Qi, X. S. (2020). Deep learning-based radiomic features for improving neoadjuvant chemoradiation response prediction in locally advanced rectal cancer. Physics in Medicine & Biology, 65(7), 075001. doi:10.1088/1361-6560/ab7970

Singhrao, K., Fu, J., Wu, H. H., Hu, P., Kishan, A. U., Chin, R. K., & Lewis, J. H. (2020). A novel anthropomorphic multimodality phantom for MRI‐based radiotherapy quality assurance testing. Medical Physics, 47(4), 1443–1451. doi:10.1002/mp.14027

Singhrao, K., Ruan, D., Fu, J., Gao, Y., Chee, G., Yang, Y., … Lewis, J. H. (2020). Quantification of fiducial marker visibility for MRI-only prostate radiotherapy simulation. Physics in Medicine & Biology, 65(3), 035015. doi:10.1088/1361-6560/ab65db

Fu, J., Singhrao, K., Cao, M., Yu, V., Santhanam, A. P., Yang, Y., … Lewis, J. H. (2020). Generation of abdominal synthetic CTs from 0.35T MR images using generative adversarial networks for MR-only liver radiotherapy. Biomedical Physics & Engineering Express, 6(1), 015033. doi:10.1088/2057-1976/ab6e1f

Fu, J., Yang, Y., Singhrao, K., Ruan, D., Chu, F., Low, D. A., & Lewis, J. H. (2019). Deep learning approaches using 2D and 3D convolutional neural networks for generating male pelvic synthetic computed tomography from magnetic resonance imaging. Medical Physics, 46(9), 3788–3798. doi:10.1002/mp.13672

Guo, M., Chee, G., O’Connell, D., Dhou, S., Fu, J., Singhrao, K., … Lewis, J. H. (2019). Reconstruction of a high‐quality volumetric image and a respiratory motion model from patient CBCT projections. Medical Physics. doi:10.1002/mp.13595