- 标题
- 摘要
- 关键词
- 实验方案
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low-coherent optical diffraction tomography by angle-scanning illumination
摘要： Temporally low-coherent optical diffraction tomography (ODT) is proposed and demonstrated based on angle-scanning Mach–Zehnder interferometry. Using a digital micromirror device based on diffractive tilting, the full-field interference of incoherent light is successfully maintained during every angle scanning sequences. Further, current ODT reconstruction principles for temporally incoherent illuminations are thoroughly reviewed and developed. Several limitations of incoherent illumination are also discussed, such as the nondispersive assumption, optical sectioning capacity, and illumination angle limitation. Using the proposed setup and reconstruction algorithms, low-coherent ODT imaging of plastic microspheres, human red blood cells, and rat pheochromocytoma cells is experimentally demonstrated.
关键词： quantitative phase imaging,low-coherent,optical diffraction tomography,coherent noise
更新于2025-09-23 15:23:52
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Label-Free Identification of Lymphocyte Subtypes Using Three-Dimensional Quantitative Phase Imaging and Machine Learning
摘要： We describe here a protocol for the label-free identification of lymphocyte subtypes using quantitative phase imaging and machine learning. Identification of lymphocyte subtypes is important for the study of immunology as well as diagnosis and treatment of various diseases. Currently, standard methods for classifying lymphocyte types rely on labeling specific membrane proteins via antigen-antibody reactions. However, these labeling techniques carry the potential risks of altering cellular functions. The protocol described here overcomes these challenges by exploiting intrinsic optical contrasts measured by 3D quantitative phase imaging and a machine learning algorithm. Measurement of 3D refractive index (RI) tomograms of lymphocytes provides quantitative information about 3D morphology and phenotypes of individual cells. The biophysical parameters extracted from the measured 3D RI tomograms are then quantitatively analyzed with a machine learning algorithm, enabling label-free identification of lymphocyte types at a single-cell level. We measure the 3D RI tomograms of B, CD4+ T, and CD8+ T lymphocytes and identified their cell types with over 80% accuracy. In this protocol, we describe the detailed steps for lymphocyte isolation, 3D quantitative phase imaging, and machine learning for identifying lymphocyte types.
关键词： lymphocyte identification,machine learning,holotomography,immune cell,immunology,Immunology and Infection,Quantitative phase imaging,optical diffraction tomography,holographic microscopy,label-free imaging
更新于2025-09-04 15:30:14