Quantitative phase imaging andcomputational specificity (Popescu)

定量相位成像和计算特异性（Popescu）

基本信息

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
项目类别：
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

项目摘要

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.

概括 TRD 1旨在通过通过通过计算和深度学习。定量相成像（QPI）正在作为一种强大的，无标签的方法出现成像细胞和组织，尤其是因为它结合了显微镜，全息和光中发现的质量散射技术：纳米级对形态和动力学的敏感性，2d，3d和4d（即时间分辨层析成像）完全透明结构的非破坏性成像，以及基于定量信号固有的对比度。这些功能允许QPI成功地应用于众多生物医学应用，包括在组织病理学和细胞疗法中的癌症诊断。最近，我们扩展了QPI 首次通过发展梯度光干扰来进行厚实的结构，例如胚胎和球体微拷贝（Glim，2018年显微镜当今年度方法）。但是，尽管进展巨大，当前的QPI技术实际上是在体内和POC应用中不存在的。我们将把QPI技术推向共聚焦反射几何形状，从而增强了焦点光线排斥和改善厚组织结构的高分辨率3D成像。具体来说，我们将首先定位成像皮肤胶原蛋白的3D方向。我们将开发无标签的内窥镜系统（Eglim）能够在保持纳米路径长度的同时，能够下空间和毫秒的时间分辨率灵敏度。我们将使用计算特异性（PIC）将相成像推进到体内的实时操作来自CPT（AIM 1）和Eglim（AIM 2）的数据。具体来说，在与TRD 3密切合作，我们将开发用于分割球体中细胞和亚细胞结构的计算工具，识别胶原蛋白纤维从体内CPT皮肤数据中，开发无标记的无标记病毒测试，无损的活细胞测定法，标签 - 自由细胞周期阶段识别。