Dr. Wuwei Ren received his bachelor, master, and PhD degrees from Zhejiang University China (2010), KTH Sweden (2012), and ETH Zurich Switzerland (2018), respectively. Dr. Ren received his postdoctoral training at University of Zurich, Switzerland. He was also a visiting scholar at University College London, UK. Currently, Dr. Ren serves as a tenure-track assistant professor at ShanghaiTech University. He was awarded STINT scholarship by the Swedish government, the Swiss Innovation and Technology Agency BRIDGE Fellowship, and SNF Project Fund. In 2020, he was awarded the ‘Shanghai Youth Eastern Scholar’ Title. His work has been published in top journals such as IEEE TBME, Light, OE, BOE, JBP, Inv Prob & Img, Neurophotonics, etc. He has been actively organizing the European Molecular Imaging Meetings (EMIMs) and serving as a sub-category chair in EMIM2020 and a category chair in EMIM2021. His optical imaging prototype has won several European entrepreneurial awards including VentureKick award and displayed in Swiss Innovation Pavilion, Hannover Messe 2019. His work in system and software development has led to a PCT patent. Now he is the head of The Molecular Imaging and Biophotonics Laboratory with the aim of pioneering next-generation medical imaging tools for early diagnostics, drug development, and image-guided surgery.
Education
2012-2018 PhD in Biomedical Engineering, ETH Zurich, Switzerland
2010-2012 M. S. in Medical imaging, Royal Institute of Technology KTH, Sweden
2006-2010 B. S. in Biomedical Engineering, Zhejiang University, China
Experience
2020.9-now Assistant Professor, ShanghaiTech University, China
2020.1-2020.9 Postdoc Researcher, ETH Zurich, Switzerland
2018.8-2020.1 BRIDGE Fellow, University Hospital Zurich, Switzerland
2016.7-2016.8 Visiting Scholar, University College London, UK
Research Interests
Optical imaging (Fluorescence imaging, Optoacoustic imaging, Diffuse optical imaging, etc.)
Optical simulation, Image reconstruction & Image processing
Multimodal imaging, specialized in MRI-optics hybrid systems
Image-guided surgery & Preclinical imaging
Professional Societies
Category Chair, European Molecular Imaging Meeting (EMIM), 2021, 2020
Member of ESMI, OSA, and SPIE
President & co-founder, Swiss-Sino Medical Technology Association, 2016-2020
Honors & Awards
Shanghai Oriental Professorship, Shanghai, 2020-2023
BRIDGE fellowship, Swiss National Science Foundation, 2018-2020
Biophotonics congress travel award, OSA, 2019
STINT scholarship for academic excellence, Swedish government, 2010-2012
Outstanding student leadership award, Zhejiang University, 2010
Direction 1: Multi-contrast optical imaging platform
Description: We are aiming at developing a novel optical imaging platform capable of visualizing multiple contrast parameters in biological tissues. The platform integrates the state-of-the-art hardware components such as wavelength-tunable pulse laser and time-of-flight cameras, as well as novel reconstruction algorithms and accurate calibration methods. Main application of such a platform includes small animal imaging and image-guided surgery.
Reference:
1. W. Ren, J. Jiang, A. D. Costanzo Mata, A. Kalyanov, J. Ripoll, S. Lindner, E. Charbon, C. Zhang, M. Rudin, and M. Wolf, Multimodal imaging combining time-domain near-infrared optical tomography and continuous-wave fluorescence molecular tomography, Opt Express 28, 9860-9874 (2020).
2. W. Ren, H. Isler, M. Wolf, J. Ripoll, and M. Rudin, Smart Toolkit for Fluorescence Tomography: Simulation, Reconstruction, and Validation, IEEE Trans Biomed Eng 67, 16-26 (2020).
3. W. Ren, L. Li, J. Zhang, M. Vaas, J. Klohs, J. Ripoll, M. Wolf, R. Ni, and M. Rudin, Non-invasive visualization of amyloid-beta deposits in Alzheimer amyloidosis mice using magnetic resonance imaging and fluorescence molecular tomography, Biomed Opt Express 13, 3809-3822 (2022).
Direction 2: Optics-MRI hybrid system
Description: Multimodal imaging has emerged as a powerful method for biomedical research and clinical usage, as more comprehensive physiological information can be achieved compared with a standalone modality. In our lab, we combine optics and MRI due to several reasons. First, optical imaging, fluorescence imaging in particular, features high sensitivity and specificity which is complementary to MRI. Second, MRI can provides as an accurate anatomical reference that is useful for localizing the fluorescence signal. Third, both MRI and optical imaging can avoid ionizing radiation. Our team has previously developed the world’s first fluorescence tomography-MRI system and optoacoustic tomography-MRI system.
Reference:
1. W. Ren, X. L. Dean-Ben, M. A. Augath, and D. Razansky, Development of concurrent magnetic resonance imaging and volumetric optoacoustic tomography: A phantom feasibility study, J Biophotonics 14, e202000293 (2021).
2. Y. Hu, B. Lafci, A. Luzgin, H. Wang, J. Klohs, X. L. Dean-Ben, R. Ni, D. Razansky, and W. Ren, Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging, Biomed Opt Express 13, 4817-4833 (2022).
3. W. Ren, B. Ji, Y. Guan, L. Cao, and R. Ni, Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics, Front Med (Lausanne) 9, 771982 (2022).
Direction 3: AI-empowered image reconstruction
Description: The conception of 3D reconstruction is commonly used in optical microscopy, e.g., in confocal microscopy and two-photon microscopy. However, visualizing objects deeply seated in tissue (> 1 mm) becomes challenging due to severe light scattering in tissue. Such a macroscopic reconstruction in scattering media is well known as a highly ill-posed inverse problem. We are seeking for fast, robust, 3D, high-resolution image reconstruction algorithms based on advanced computational techniques and novel machine learning methods.
Reference:
1. Y. Liu, W. Ren, and H. Ammari, Robust reconstruction of fluorescence molecular tomography with an optimized illumination pattern, Inverse Problems & Imaging 14, 535-568 (2020).
2. W. Ren, H. Isler, M. Wolf, J. Ripoll, and M. Rudin, Smart Toolkit for Fluorescence Tomography: Simulation, Reconstruction, and Validation, IEEE Trans Biomed Eng 67, 16-26 (2020).
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