活动时间：2026-10-14 15:00
活动地点：数学与统计学院2432报告厅
主讲人：姜嘉骅
主讲人简介：
姜嘉骅博士2013年获中国科学技术大学学士学位，2018年获得麻省大学达特茅斯分校博士学位。2018-2020年，赴弗吉尼亚理工大学开展博士后研究。此后，担任英国伯明翰大学助理教授，现任上海科技大学长聘副教授。姜嘉骅博士主要从事模型降阶，不确定性量化，反问题在图像处理上的应用等方面的研究。在SISC, JSC, Inverse problem，Nature Communications，Laser & Photonics Reviews等多个应用数学和工程领域的核心期刊上发表论文。同时，还担任JSC，JCP等多个国际重要学术期刊的审稿人。
内容摘要：
Light scattering imposes a major obstacle for imaging objects seated deeply in turbid media, such as biological tissues and foggy air. Diffuse optical tomography (DOT) tackles scattering by volumetrically recovering the optical absorbance and has shown significance in medical imaging, remote sensing and autonomous driving. A conventional DOT reconstruction paradigm necessitates discretizing the object volume into voxels at a pre-determined resolution for modelling diffuse light propagation and the resultant spatial resolution of the reconstruction is generally limited. We propose NeuDOT, a novel DOT scheme based on neural fields (NF) to continuously encode the optical absorbance within the volume and subsequently bridge the gap between model efficiency and high resolution. Comprehensive experiments demonstrate that NeuDOT affords to resolve complex 3D objects at 14 mm depth with submillimeter lateral resolution, outperforming the state-of-the-art methods. NeuDOT is a non-invasive, high-resolution and computationally efficient tomographic method, and also unlocks further applications of NF involving light scattering.