时间：6月29日（周四）上午10:00

地点：学术报告厅D533

主持人：冯远静

张帆报告题目：

Computational Diffusion MRI Analysis Using Deep Learning

张帆报告摘要：

Diffusion MRI is an advanced imaging technique that measures the random molecular motion or diffusion of water molecules. It enables estimation of the underlying tissue microstructure and allows in vivo mapping of the brain’s white matter connections. Recent advances in deep learning have witnessed great success in computational diffusion MRI, which has shown great potential in clinical and research applications, e.g., study of the brains in health and disease and neurosurgical planning research. In this talk, Dr. Zhang will introduce several machine/deep learning techniques that he and his colleagues developed for computational diffusion MRI tasks as well as applications of his methods in clinically relevant tasks.

张帆简介：

Fan Zhang is a national young talent, a Professor at the University of Electronic Science and Technology of China (UESTC), and an adjunct researcher at Harvard Medical School (HMS). From 2015 to 2023, he was a visiting scholar, a postdoctoral researcher, and then a faculty member at HMS in the USA. In 2016, he received my PhD degree in Computer Science from The University of Sydney (USYD) in Australia. Fan Zhang’s research focuses on developing advanced AI-based technologies for medical image analysis and neuroimage computing, with the goal of providing clinicians with the tools and information needed to make accurate diagnoses and to guide effective treatments, ultimately improving patient outcomes and quality of life. Fan Zhang has published over 120 peer-reviewed papers in leading international journals and proceedings at top international conferences such as MedIA, IEEE TMI, MICCAI, CVPR. Fan is an Associate Editor for Frontiers in Radiology, Editorial Board Member for Meta-Radiology and Brain-X, and an Area Chair for MICCAI 2023. Fan was an organizer of the CDMRI Workshops (2019, 2020) and the MUDI and Super-MUDI Challenges. Fan also serves as a regular reviewer for over 40 journals and conferences.

吴烨报告题目：

面向脑发育的扩散磁共振成像新范式

吴烨报告摘要：

儿童大脑的特点是动态发展变化，这些变化在心理、生物和社会层面启动了学习过程。这些学习过程塑造了他们的认知、动机和决策。这导致出现行为模式，可以以目标为导向的方式灵活调整。然而，如果发展出现问题，异常学习可能会导致失控行为模式。脑科学和人工智能正在解决这些重大问题。近几年，脑科学和人工智能取得了高速进展，极大促进了对儿童大脑发育、功能和疾病的了解，推动了儿童健康向前发展。其中，扩散磁共振成像(dMRI)是唯一无创检测脑微观组织和宏观连接的技术，是研究脑智发育重要手段。针对dMRI扫描时间过长且对婴幼儿脑智发育研究不敏感的科学问题，立足多学科交叉融合，面向dMRI新理论和方法，探讨新型快速成像序列，突破dMRI成像在婴幼儿扫描与分析瓶颈，为早期诊断与预后评估提供精准高效的影像依据。

吴烨简介：

吴烨，南京理工大学计算机学院教授，江苏省引进高层次青年人才、入选江苏特聘教授。曾先后在哈佛医学院联合培养、在北卡罗莱纳州大学从事博士后研究。长期从事脑神经科学与人工智能交叉研究，围绕扩散磁共振成像、脑纤维图谱绘制、个性化精准医疗等研究方向开展了系统的研究工作。近五年在Nature Methods, Nature Communications, NeuroImage, Medical Image Analysis, IEEE TMI, MICCAI, IPMI等国际顶级期刊和会议发表学术论文50余篇，Google学术引用1700余次，研究成果荣获Medical Image Analysis最佳论文奖、国际磁共振学会(ISMRM)卓越论文奖、国际脑成像协会(OHBM)杰出论文奖、MICCAI-NIH奖、国际“互联网+”全国金奖、国家科技创新团队奖等。研究成果被纳入美国“Baby Connectome Project”计划。主持国家自然科学基金、中央高校科研基金、江苏省海外高层次人才基金等。