任无畏博士曾就读于浙江大学（2010）、瑞典皇家理工学院（2012）、瑞士苏黎世联邦理工学院（2018）并分别获得学士、硕士、博士学位。2016年，任博士曾赴英国伦敦大学学院医学图像计算中心访学交流，后在瑞士苏黎世大学的附属医院及药学院从事博士后研究工作。他的学术成果发表在IEEE TBME，Light，OE，BOE，JBP，Inv Prob & Img，Neurophotonics等医学成像领域主流期刊，并担任欧洲分子成像年会分会主席。任博士自主研发的光学成像原型机曾斩获VentureKick等欧洲知名创业奖项，并在2019年度汉诺威工业展瑞士创新馆展示。他开发的光学仿真及重建软件已注册国际专利（PCT）。他曾获瑞典政府颁发的STINT全额奖学金，瑞士创新科技署BRIDGE成果转化基金（首位华人得奖者），及瑞士国家科学基金会基础项目基金，并入选上海市“青年东方学者”人才计划和上海市领军人才计划。任无畏博士目前在上海科技大学信息学院担任助理教授、研究员、博导。他领导的分子成像及生物光学实验室将致力于促进医工交叉融合，为疾病早期诊断、新药开发及手术导航提供新一代医学成像工具。

教育经历

2012-2018      博士，生物医学工程专业，瑞士苏黎世联邦理工学院

2010-2012      硕士，生物医学工程专业，瑞典皇家理工学院

2006-2010      学士，生物医学工程专业，浙江大学

职业经历

2020.9-now                助理教授，上海科技大学

2020.1-2020.9            博士后，生物医学工程专业，瑞士苏黎世联邦理工学院

2018.8-2020.1            博士后，瑞士苏黎世大学医院

2016.7-2016.8            访问学者，英国伦敦大学学院

研究兴趣

光学成像

图像处理以及图像重建

多模态成像（光学-MRI）

荧光手术导航和小动物成像

学术活动

分会主席, 欧洲分子成像年会 (EMIM), 2021, 2020

ESMI, OSA, and SPIE 会员

创始人, 中瑞医疗器械协会, 2016-2020

奖项

上海市“青年东方学者”, 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

方向一：多对比度光学成像系统

我们的目标是开发一种能够可视化生物组织中多个对比度参数的新型光学成像平台。 该平台集成了波长可调脉冲激光器和飞行时间相机等最先进的硬件组件，以及新颖的重建算法和精确的校准方法。 这种平台的主要应用包括小动物成像和图像引导手术。

参考文献：

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).

方向二：光学-MRI混合系统

多模态成像已成为生物医学研究和临床使用的一种强大方法。与单一模态相比，可以获得更全面的生理信息。我们将光学和 MRI 相结合有多方面的优点。 首先，光学成像，特别是荧光成像，具有高灵敏度和特异性，与MRI高度互补。 其次，MRI 可以提供准确的解剖学参考，有助于定位荧光信号。 第三，MRI和光学成像都可以避免电离辐射。 我们的团队此前开发了世界上第一台荧光断层扫描-MRI系统和光声断层扫描-MRI系统。

参考文献：

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).

方向三：AI赋能的图像重建

3D 重建的概念通常用于光学显微镜，例如共聚焦显微镜和双光子显微镜。 然而，由于组织中严重的光散射，可视化深入组织（> 1 毫米）的物体变得具有挑战性。 众所周知，散射介质中的这种宏观重建是高度不适定的逆问题。 我们正在寻求基于先进计算技术和新颖机器学习方法的快速、稳健、3D、高分辨率图像重建算法。

参考文献：

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).

• 数字信号处理, ShanghaiTech-EE152, 2021 Spring, 2022 Spring, 2023 Spring

• 数字图像处理, ShanghaiTech-CS270, 2021 Fall, 2022 Fall

• 多模态脑机接口, ShanghaiTech-SI361, 2021 Spring, 2022 Spring

期刊论文Journal papers: 

1. Zhu X, Gu L, Li R, Chen L, Chen, J, Zhou N*, Ren W* (2023), MiniMounter: a low‐cost miniaturized microscopy development toolkit for image quality control and enhancement. Journal of Biophotonics, e202300214

2. Gao S, Zhang J, Hu Y, Wu Y, Li L, Hu Q, Lou X, Zhu X, Jiang J*, Ren W* (2023), Multifunctional Optical Tomography System with High-fidelity Surface Extraction based on a Single Programmable Scanner and Unified Pinhole Modeling, IEEE Transactions on Biomedical Engineering, 1-13

3. Ren W, Dean-Ben Xl, Skachokova Z, Augath M, Ni R, Chen Z, Razansky D* (2023) Monitoring mouse brain perfusion with hybrid magnetic resonance optoacoustic tomography. Biomed Opt Express 14: 1192-1204

4. Ren W*, Li L, Zhang J, Vaas M, Klohs J, Ripoll J, Wolf M, Ni R, Rudin M (2022) 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

5. Ren W*, Ji B, Guan Y, Cao L, Ni R* (2022) Recent Technical Advances in Accelerating the Clinical Translation of Small Animal Brain Imaging: Hybrid Imaging, Deep Learning, and Transcriptomics. Front Med (Lausanne) 9: 771982

6. Hu Y, Lafci B, Luzgin A, Wang H, Klohs J, Dean-Ben XL, Ni R, Razansky D*, Ren W*(2022) Deep learning facilitates fully automated brain image registration of optoacoustic tomography and magnetic resonance imaging. Biomed Opt Express 13: 4817-4833

7. Chen Z, Gezginer I, Augath MA, Ren W, Liu YH, Ni R, Dean-Ben XL, Razansky D* (2022) Hybrid magnetic resonance and optoacoustic tomography (MROT) for preclinical neuroimaging. Light Sci Appl 11: 332

8. Zhou R, Ji B, Kong Y, Qin L, Ren W, Guan Y, Ni R* (2021) PET Imaging of Neuroinflammation in Alzheimer's Disease. Front Immunol 12: 739130

9. Ren W, Dean-Ben XL, Augath MA, Razansky D* (2021) Development of concurrent magnetic resonance imaging and volumetric optoacoustic tomography: A phantom feasibility study. J Biophotonics 14: e202000293

10. Ren W, Cui S, Alini M, Grad S, Zhou Q, Li Z, Razansky D* (2021) Noninvasive multimodal fluorescence and magnetic resonance imaging of whole-organ intervertebral discs. Biomed Opt Express 12: 3214-3227

11. Ren W*, Jiang J, Costanzo Mata AD, Kalyanov A, Ripoll J, Lindner S, Charbon E, Zhang C, Rudin M, Wolf M (2020) Multimodal imaging combining time-domain near-infrared optical tomography and continuous-wave fluorescence molecular tomography. Opt Express 28: 9860-9874

12. Ren W, Isler H, Wolf M, Ripoll J, Rudin M* (2020) Smart Toolkit for Fluorescence Tomography: Simulation, Reconstruction, and Validation. IEEE Trans Biomed Eng 67: 16-26

13. Liu Y, Ren W*, Ammari H (2020) Robust reconstruction of fluorescence molecular tomography with an optimized illumination pattern. Inverse Problems & Imaging 14: 535-568

14. Ren W, Skulason H, Schlegel F, Rudin M, Klohs J, Ni R* (2019) Automated registration of magnetic resonance imaging and optoacoustic tomography data for experimental studies. Neurophotonics 6: 025001

15. Ni R, Vaas M, Ren W, Klohs J* (2018) Noninvasive detection of acute cerebral hypoxia and subsequent matrix-metalloproteinase activity in a mouse model of cerebral ischemia using multispectral-optoacoustic-tomography. Neurophotonics 5: 015005

16. Ren W, Elmer A, Buehlmann D, Augath M-A, Vats D, Ripoll J, Rudin M* (2016) Dynamic Measurement of Tumor Vascular Permeability and Perfusion using a Hybrid System for Simultaneous Magnetic Resonance and Fluorescence Imaging. Mol Imaging Biol 18: 191-200

会议论文Proceeding papers:

1. Hu Y, Lafci B, Luzgin A, Wang H, Klohs J, Dean-Ben XL, Ni R, Razansky D, Ren W* (2022) Automatic image registration of optoacoustic tomography and magnetic resonance imaging based on deep learning. Proc. SPIE, First Conference on Biomedical Photonics and Cross-Fusion, 1246103, doi: doi.org/10.1117/12.2655214

2. Ren W, Dean-Ben XL, Augath M, Razansky D* (2021) Feasibility study on concurrent optoacoustic tomography and magnetic resonance imaging. Proc. SPIE, doi: (oral & Best Paper Nomination in SPIE Photonics West,  ranking top 5%)

3. Ren W*, Jiang J, Di Costanzo MA, Kalyanov A, Ripoll J, Lindner S, Charbon S, Zhang C, Rudin M, Wolf M (2020) Time-domain Near Infrared Optical Tomography Guided Fluorescence Molecular Tomography, Biophotonics Congress: Biomedical Optic. OSA

4. Jiang J, Ren W, Isler H,  Kalyanov A, Lindner S, Mata Aldo D. C, Rudin M, Wolf M* (2020), Validation and Comparison of Monte Carlo and Finite Element Method in Forward Modeling for Near Infrared Optical Tomography. In Oxygen Transport to Tissue XLI (pp. 307-313). Springer, Charm

5. Polatoglu M N, Liu Y, Ni R, Ripoll J, Rudin M, Wolf M, Ren W* (2019), Simulation of fluorescence molecular tomography using a registered digital mouse atlas, Proc. SPIE, doi: /10.1117/12.2525366

6. Ren W, Skulason H, Schlegel F, Rudin M, Klohs J, Ni R* (2019), Automated registration for optoacoustic tomography and MRI, In Optical Molecular Probes, Imaging and Drug Delivery. OSA

7. Ni R, Vaas M, Ren W, Klohs J* (2018), Non-invasive detection of matrix-metalloproteinase activity in a mouse model of cerebral ischemia using multispectral optoacoustic tomography, Proc. SPIE, doi: 10.1117/12.2286313

8. Ren W, Elmer A, Augath M, Rudin M* (2016), FEM-based Simulation of a Fluorescence Tomography Experiment using Anatomical MR Images, Proc. SPIE, doi:10.1117/12.2216454

9. Ren W, Valastyán I, Colarieti-Tosti M* (2012), Stationary SPECT with multi-layer Multiple-Pinhole-Arrays, IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), doi: 10.1109/NSSMIC.2012.6551593 

10. Valastyán I*, Colarieti-Tostia M, Ren W, Turco, A, Kereka A (2012), Monte Carlo simulation of a dental Positron Emission Tomograph and image reconstruction of scatter and true coincidence events, IEEE Nuclear Science Symposium and Medical Imaging Conference (NSS/MIC), doi: 10.1109/NSSMIC.2012.6551795

专利Patent:

1. Ren W, Rudin M, Method for selecting an illumination pattern for a fluorescence molecular tomography measurement, (PCT/EP2020/051779)

2. 任无畏，高圣宇，吴亚男，连续波光源非接触式的光学断层扫描系统及扫描方法(CN202210859035.5)