CAREER: Computational structured light imaging for widefield mapping of histologic primitives

这个CAREER项目的研究目标是创建新的光学和计算工具，以改善未加工组织的组织学成像。组织学在大多数癌症的诊断和治疗中是必不可少的，但其核心工作流程在世纪中没有改变。这项研究探索了图案化紫外光和人工智能方法直接在大面积上成像组织学信息的潜力。这种“无载玻片”方法可以提高诊断的速度和准确性，同时改善各种癌症的手术和评估。该研究计划与一项教育计划紧密结合，该计划旨在培养本科工程师解决病理学的重要临床需求，通过大型黑客马拉松将病理学家与工程师联系起来，并向高中生介绍医疗设备设计。传统的空间频域成像（SFDI）提供了大组织的定量光学特性图，但是由于调制深度的减小而遭受低分辨率和光学切片，增加空间频率。该计划将创建一个新的宏观，项目紫外线照射在非常高的空间频率，以恢复高分辨率，表面的光学性质的散装组织地图。将通过紫外激发显微镜（MUSE）测量量化细胞核形态、细胞构成和其他临床相关特征的组织学原始图。将获取离体人类皮肤样本的成对光学特性和组织学原始图，用于训练和测试机器学习模型。将研究新的多模态深度学习架构，以直接从宏观输入预测这些组织学基元，并联合优化宏观硬件和预测算法。从这项研究中获得的知识将是重要的1）弥合扩散光学成像和显微镜之间的知识差距，2）了解如何协同设计人工智能模型与成像硬件，和3）该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的学术价值和更广泛的影响审查标准。

The research goal of this CAREER project is to create new optical and computational tools to improve histological imaging of unprocessed tissues. Histology is essential in the diagnosis and treatment of most cancers, yet its core workflow has not changed in over a century. This research explores the potential of a patterned ultraviolet light and artificial intelligence approach to directly image histological information over large areas. This “slide-free” approach could increase speed and accuracy of diagnosis while improving surgery and assessment for a wide range of cancers. This research plan is closely integrated with an educational program that aims to train undergraduate engineers to solve important clinical needs in pathology, connect pathologists with engineers through large hackathons, and introduce high school students to medical device design.Conventional spatial frequency domain imaging (SFDI) provides quantitative optical property maps of large tissues but suffers from poor resolution and optical sectioning due to decreasing modulation depths with increasing spatial frequencies. This program will create a novel macroscope that projects ultraviolet illumination at very high spatial frequencies to recover high-resolution, superficial optical property maps of bulk tissues. Histologic primitive maps that quantify nuclei morphology, cellularity, and other clinically relevant features, will be measured via Microscopy by Ultraviolet Excitation (MUSE). Paired optical property and histologic primitive maps of ex-vivo human skin samples will be acquired for training and testing machine learning models. New multimodal deep learning architectures will be researched to predict these histologic primitives directly from macroscopy inputs and also to jointly optimize the macroscope hardware and prediction algorithm. The knowledge gained from this research will be important for 1) bridging the knowledge gap between diffuse optical imaging and microscopy, 2) understanding how to co-design artificial intelligence models with imaging hardware, and 3) creating a foundation for innovative histology tools that could dramatically improve pathology workflows.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.