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Lei Ren, PhD

Academic Title:

Professor

Primary Appointment:

Radiation Oncology

Administrative Title:

Associate Chief of Physics Research in the Department of Radiation Oncology

Education and Training

  • Tsinghua University, Beijing, China                                                1999- 2003
    Academic Talent Program, School of Science
    B.S. in Physics and Mathematics

  • Duke University, Durham, NC                                                        2005- 2009
    Ph.D. in Medical Physics

Biosketch

Dr. Ren is a clinical medical physicist certified by ABR and a leading scientist in medical imaging and radiation therapy. His seminal research focuses on the image-guided radiation therapy (IGRT) and development and application of AI in radiation therapy.

Dr. Ren conducted some of the first studies on developing digital tomosynthesis (DTS) for fast, low-dose target localization in radiation therapy, which led to the development of the limited-angle intrafraction verification (LIVE) system for intrafraction verification in radiotherapy. His group also developed novel cone-beam CT (CBCT) scatter reduction and correction methods, DTS/CBCT/MRI image reconstruction methods using prior information and motion modeling, and 4D-CBCT sorting and reconstruction algorithms.

In recent years, Dr. Ren's group has focused on developing and implementing AI for radiation therapy applications. His research areas include developing novel AI, especially deep learning, techniques for deformable image registration, image synthesis, image reconstruction, image augmentation, 4D imaging, radiomics, clinical decision making, and digital phantom generation. They have been developing techniques to use deep learning and biomechanical modeling to generate on-board hybrid virtual-MRI/CBCT images to substantially improve the soft tissue contrast in CBCT for target localization in liver radiotherapy. In addition, his group is actively developing AI technologies for synthesizing highly realistic eXtended Modular ANthropomorphic (XMAN) phantoms for motion management and virtual clinical trials in radiation therapy. Recently, his group has been developing image reconstruction and processing techniques for novel imaging modalities such as prompt gamma imaging and protoaoustic imaging for proton dose verification.

The overall goal of Dr. Ren's research is to develop novel imaging and therapy technologies to improve the precision and outcome of radiation therapy treatments with high efficiency and minimal imaging dose. 

Dr. Ren's lab is actively seeking postdoc fellows. Please feel free to reach out if interested.

Research/Clinical Keywords

Image-guided radiation therapy (IGRT), AI, Deep learning, digital tomosynthesis, dose reduction, CBCT, scatter correction, 4D imaging, prior information, motion modeling, biomechanical modeling, deformable image registration, image synthesis, image reconstruction, image augmentation, radiomics, clinical decision making, digital phantom generation.

Highlighted Publications

Y. Cao, P. Sutera, ... & L. Ren, “Machine learning predicts conventional imaging metastasis-free survival (MFS) for oligometastatic castration-sensitive prostate cancer (omCSPC) using prostate-specific membrane antigen (PSMA) PET Radiomics”, Radiotherapy and Oncology, 110443, 2024

Y. Zhang, Z. Jiang, Y. Zhang, L. Ren, “A review on 4D cone-beam CT (4D-CBCT) in radiation therapy: technical advances and clinical applications”, Medical Physics (Journal cover paper), 51(8), 5164-5180, 2024

Y. Lang, Z. Jiang, L. Sun, P. Tran, S. Mossashebi, L. Xiang, L. Ren, “Patient Specific Deep Learning for 3D Protoacoustic Image Reconstruction and Dose Verification in Proton Therapy”, Medical Physics, 51(7), 7425-7438, 2024 

X. Ling, G. Alexander, J. Molitoris, J. Choi, L. Schumaker, P. Tran, R. Mehra, D. Gaykalova, L. Ren, “Radiomic Biomarkers of Locoregional Recurrence: Prognostic Insights from Oral Cavity Squamous Cell Carcinoma preoperative CT scans”, Frontiers in Oncology, 14, 2024

Y. Lang, Z. Jiang, L. Sun, L. Xiang, L. Ren, “Hybrid-Supervised Deep Learning for Domain Transfer 3D Protoacoustic Image Reconstruction”, Physics in Medicine and Biology, 69(8), 085007, 2024

X. Ling, G. Alexander, J. Molitoris, J. Choi, L. Schumaker, R. Mehra, D. Gaykalova, L. Ren, “Identification of CT-based non-invasive Radiographic Biomarkers for Overall Survival Stratification in Oral Cavity Squamous Cell Carcinoma”, Nature Scientific Report, 13, 21774, 2023

Z. Jiang, S. Wang, Y. Xu, L. Sun, G. Gonzalez, Y. Chen, J. Wu, L. Xiang, L. Ren, “Radiation-induced Acoustic Signal Denoising using a Supervised Deep Learning Framework for Imaging and Therapy Monitoring”, Physics in Medicine and Biology, 68, 235010, 2023

Z. Jiang, J. Polf, C. Barajas, M. Gobbert, L. Ren, “A Feasibility Study of Enhanced Prompt Gamma Imaging for Range Verification in Proton Therapy using Deep Learning”, Physics in Medicine and Biology, 68(7), 075001, 2023

Y. Cao, D. Kunaprayoon, L. Ren, “Interpretable AI-assisted Clinical Decision Making (CDM) for dose prescription in radiosurgery of brain metastases”, Radiotherapy and Oncology, 187, 109842, 2023

Y. Cao, D. Kunaprayoon, J. Xu, L. Ren, “AI-assisted Clinical Decision Making (CDM) for dose prescription in radiosurgery of brain metastases using three-path three-dimensional CNN”, Clinical and Translational Radiation Oncology, 39, 100565, 2023

Z. Jiang, L. Sun, W. Yao, J. Wu, L. Xiang, L. Ren, “3D in vivo dose verification in prostate proton therapy with deep learning-based proton-acoustic imaging”, Physics in Medicine and Biology, 67(21), 215012, 2022

Z. Jiang, Y. Chang, Z. Zhang, F. Yin, L. Ren, “Fast Four-dimensional Cone-beam Computed Tomography Reconstruction using Deformable Convolutional Networks”, Medical Physics, 49(10), 6461-6476, 2022

Z. Zhang, Z. Jiang, H. Zhong, K. Lu, F. Yin, L. Ren, “Patient-specific Synthetic MRI Generation from CBCT for image guidance in liver SBRT”, Precision Radiation Oncology, 6, 110-118, 2022

Z. Zhang, M. Huang, Z. Jiang, Y. Chang, K. Lu, F. Yin, P. Tran, D. Wu, C. Beltran, L. Ren, “Patient-specific deep learning model to enhance 4D-CBCT Image for radiomics analysis”, Physics in Medicine and Biology, 67(8), 085003, 2022

Z. Zhang, M. Huang, Z. Jiang, Y. Chang, J. Torok, F. Yin, L. Ren, “4D Radiomics: Impact of 4D-CBCT Image Quality on Radiomic Analysis”, Physics in Medicine and Biology, 66(4), 045023, 2021.

Z. Jiang, F. Yin, Y. Ge, L. Ren, “Enhancing Digital Tomosynthesis (DTS) for Lung Radiotherapy Guidance using Patient-Specific Deep Learning Model”, Physics in Medicine and Biology, 66(3), 035009, 2021.

Y. Chang, K. Lafata, P. Segars, F. Yin, L. Ren, “Development of realistic Multi-contrast Textured XCAT (MT-XCAT) phantoms using a dual-discriminator conditional-generative adversarial network (D-CGAN)”, Physics in Medicine and Biology, 65(6), 065009, 2020

Z. Jiang, F. Yin, Y. Ge, L. Ren, “A multi-scale framework with unsupervised joint training of convolutional neural networks for pulmonary deformable image registration”, Physics in Medicine and Biology, 65(1), 015011, 2020

X. Jia, L. Ren, J. Cai, “Point/Counterpoint: Clinical implementation of AI technologies will require interpretable AI models”, Medical Physics, 47(1), 1-4, 2020

Z. Jiang, Y. Chen, Y. Zhang, Y. Ge, F. Yin, L. Ren, “Augmentation of CBCT Reconstructed from Under-sampled Projections using Deep Learning”, IEEE Transactions on Medical Imaging, 38(11), 2705-2715, 2019

J. Pham, W. Harris, W. Sun, Z. Yang, F. Yin, L. Ren, “Predicting Real-Time 3D Deformation Field Maps (DFM) based on Volumetric Cine MRI (VC-MRI) and Artificial Neural Networks for On-board 4D Target Tracking: A feasibility study”, Physics in Medicine and Biology, 64(16), 165016, 2019

C. Shieh, Y. Gonzalez, B. Li, X. Jia, S. Rit, C. Mory, M. Riblett, G. Hugo, Y. Zhang, L. Ren, P. Keall, “SPARE: SPArse-view REconstruction challenge for 4D cone-beam CT from a one-minute scan”, Medical Physics, 46(9), 3799-3811, 2019

G. Ding, Y. Zhang, L. Ren, “Imaging dose resulting from optimized procedures with Limited-angle Intra-fractional Verification (LIVE) system during SBRT lung treatment”, Medical Physics, 46(6), 2709-2715, 2019

W. Harris, C. Wang, F.F. Yin, J. Cai, L. Ren, “A Novel Method to Generate On-board 4D MRI Using Prior 4D MRI and On-board kV Projections From a Conventional LINAC for Target Localization in Liver SBRT”, Medical Physics, 45(7), 3238-3245, 2018. (Medical Physics Letter)

Y. Chen, F.F. Yin, Y. Zhang, Y. Zhang, L. Ren, “Low dose CBCT reconstruction via prior contour based total variation regularization (PCTV): a feasibility study”, Physics in Medicine and Biology, 63(8), 085014, 2018

A. Dubey, A. Iliopoulos, X. Sun, F.F. Yin, L. Ren, “Iterative Inversion of Deformation Vector Fields with Feedback Control”, Medical Physics, 45(7), 3147-3160, 2018

Y. Zhang, X. Deng, F.F. Yin, L. Ren, “Image Acquisition Optimization of a Limited-Angle Intrafraction Verification (LIVE) System for Lung Radiotherapy”, Medical Physics, 45(1), 340-351, 2018  (Editor’s Pick)

Y. Zhang, F.F. Yin, Y. Zhang, L. Ren, “Reducing scan angle using adaptive prior knowledge for a limited-angle intrafraction verification (LIVE) system for conformal arc radiotherapy,” Physics in Medicine and Biology, 62(9), 3859-3882, 2017

C. Wang, F. Yin, W. Segars, Z. Chang, L. Ren, “Development of a Computerized 4D MRI Phantom for Liver Motion Study”, Technology in Cancer Research & Treatment, 16(6), 1051-1059, 2017

Y. Zhang, F.F. Yin, T. Pan, I. Vergalasova, and L. Ren, “Preliminary clinical evaluation of a 4D-CBCT estimation technique using prior information and limited-angle projections,” Radiotherapy and Oncology, 115(1), 22-29, 2015

L. Ren, Y. Zhang, and F.F. Yin, “Medical Physics Letter: A limited-angle intrafraction verification (LIVE) system for radiation therapy,” Med. Phys., 41(2), 020701, 2014. (Medical Physics Letter)

Y. Zhang, F.F. Yin, W.P. Segars, and L. Ren, “A technique for estimating 4D-CBCT using prior knowledge and limited-angle projections,” Med. Phys., 40(12), 121701, 2013

L. Ren, I. Chetty, J. Zhang, J. Jin, Q.J. Wu, H. Yan, D. Brizel, W.R. Lee, C. Willett, B. Movsas, and F.F. Yin, “Development and clinical evaluation of a three-dimensional cone-beam computed tomography estimation method using a deformation field map,” Int. J. Radiat. Oncol. Biol. Phys., 82(5), 1584-93, 2012

L. Ren, F. Yin, I. Chetty, D. Jaffray, and J. Jin, “Feasibility study of a synchronized-moving-grid (SMOG) system to improve image quality in Cone-Beam Computed Tomography (CBCT)”, Med. Phys., 39(8), 5099-5110, 2012

L. Ren, D.J. Godfrey, H. Yan, Q.J. Wu, and F.F. Yin, “Automatic registration between reference and on-board digital tomosynthesis images for positioning verification,” Med. Phys. 35, 664-672, 2008

L. Ren, J. Zhang, D. Thongphiew, D.J. Godfrey, Q.J. Wu, S. Zhou, and F.F. Yin, “A novel digital tomosynthesis (DTS) reconstruction method using a deformation field map,” Med. Phys. 35, 3110-3115, 2008. (Medical Physics Letter)

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