Quantitative phase imaging andcomputational specificity (Popescu)

定量相位成像和计算特异性（Popescu）

• 批准号: 10705170
• 负责人: Rohit Bhargava
• 金额: $ 18.03万
• 依托单位: UNIVERSITY OF ILLINOIS AT URBANA-CHAMPAIGN
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-09-30 至 2027-06-20
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10705170
• 关键词: 3-Dimensional; Action Potentials; Address; Biophotonics; Brain; Calibration; Cancer Prognosis; Cell Count; Cell Cycle; Cell Therapy; Cells; Cellular Assay; Cellular Structures; Chemicals; Classification; Collaborations; Collagen; Collagen Fiber; Computer software; Data; Detection; Diffusion; Embryo; Endoscopes; Fiber; Geometry; Histopathology; Holography; Human; Image; Imaging Techniques; Imaging technology; In Vitro; Label; Lasers; Length; Light; Measurement; Methods; Microscope; Microscopy; Microspheres; Modeling; Morphology; Mus; Neurons; Optics; Organoids; Pathology; Penetration; Phase; Physics; Procedures; Resolution; Retrieval; Sampling; Scanning; Scheme; Signal Transduction; Skin; Slide; Specificity; Speed; Stains; Structure; Subcellular structure; Surface; System; Techniques; Testing; Thick; Three-Dimensional Imaging; Time; Tissue imaging; Tissues; Translating; Viral; artificial intelligence algorithm; cancer diagnosis; cellular imaging; clinical application; computerized tools; deep learning; design; improved; in vivo; instrument; light scattering; meter; millisecond; nanometer; nanoscale; operation; programs; reconstruction; submicron; temporal measurement; tomography; tool; viral detection; virtual

SUMMARY TRD 1 aims to translate QPI technology to in-vivo and deep-tissue imaging with specific markers developed via computation and deep-learning. Quantitative phase imaging (QPI) is emerging as a powerful, label-free approach to imaging cells and tissues, especially because it combines qualities found in microscopy, holography, and light scattering techniques: nanoscale sensitivity to morphology and dynamics, 2D, 3D, and 4D (i.e., time-resolved tomography) nondestructive imaging of completely transparent structures, and quantitative signals based on intrinsic contrast. These capabilities have allowed QPI to be successfully applied in numerous biomedical applications, including cancer diagnosis in histopathology and cell therapy. Recently, we have expanded QPI for the first time to thick structures, such as embryos and spheroids, by developing gradient light interference microcopy (GLIM, the 2018 Microscopy Today Method of the Year). However, despite enormous progress, current QPI techniques are virtually absent from in-vivo and POC applications. We will advance the QPI technology to a confocal reflection geometry, thus, boosting the out of focus light rejection and improving high-resolution 3D imaging of thick tissue structures. Specifically, we will target first imaging the 3D orientation of skin collagen in-vivo. We will develop a label-free endoscopic system (eGLIM) capable of sub-micron spatial and millisecond temporal resolution, while maintaining nanometer pathlength sensitivity. We will advance phase imaging with computational specificity (PICS) to real-time operation on in-vivo data from CPT (Aim 1) and eGLIM (Aim 2). Specifically, in close collaboration with TRD 3, we will develop computational tools for segmenting cellular and subcellular structures in spheroids, identifying collagen fibers from in-vivo CPT skin data, developing rapid label-free viral testing, nondestructive live/dead cell assays, label- free cell cycle phase identification.

总结 TRD 1旨在将QPI技术转化为体内和深层组织成像，通过开发特定标记物， 计算和深度学习。定量相位成像（QPI）是一种功能强大的无标记方法 对细胞和组织进行成像，特别是因为它结合了显微镜，全息术和光的特性 散射技术：对形态和动力学的纳米级灵敏度，2D，3D，和4D（即，时间分辨 完全透明结构的非破坏性成像，以及基于 内在对比。这些能力使QPI成功应用于许多生物医学领域。 应用，包括组织病理学和细胞治疗中的癌症诊断。最近，我们扩大了QPI， 第一次对厚的结构，如胚胎和球体，通过发展梯度光干涉 显微镜（GLIM，2018年显微镜今日年度方法）。然而，尽管取得了巨大进展， 目前的QPI技术实际上没有用于体内和POC应用。 我们将把QPI技术推进到共焦反射几何结构，从而提高焦外光的强度。 拒绝和改善厚组织结构的高分辨率3D成像。具体来说，我们将首先针对 对体内皮肤胶原蛋白的3D取向进行成像。我们将开发一种无标签内窥镜系统（eGLIM） 具有亚微米空间和毫秒时间分辨率，同时保持纳米路径长度 灵敏度我们将推进相位成像与计算特异性（PICS）的实时操作在体内 数据来自CPT（目标1）和eGLIM（目标2）。具体而言，我们将与TRD 3密切合作， 计算工具，用于分割球体中的细胞和亚细胞结构，识别胶原纤维 从体内CPT皮肤数据，开发快速无标记病毒检测，无损活/死细胞检测，标记， 游离细胞周期时相鉴定。