Simultaneous measurements of a specimen quantitative-phase signal and its surrounding medium refractive index using quantitative-phase imaging

2020 ◽  
Vol 45 (19) ◽  
pp. 5587
Author(s):  
Bertrand de Dorlodot ◽  
Erik Bélanger ◽  
Émile Rioux-Pellerin ◽  
Pierre Marquet
2018 ◽  
Vol 26 (8) ◽  
pp. 10729 ◽  
Author(s):  
Paul Müller ◽  
Mirjam Schürmann ◽  
Salvatore Girardo ◽  
Gheorghe Cojoc ◽  
Jochen Guck

2017 ◽  
Author(s):  
Masanori Takabayashi ◽  
Hassaan Majeed ◽  
Andre Kajdacsy-Balla ◽  
Gabriel Popescu

AbstractTissue refractive index provides important information about morphology at the nanoscale. Since the malignant transformation involves both intra- and inter-cellular changes in the refractive index map, the tissue disorder measurement can be used to extract important diagnosis information. Quantitative phase imaging (QPI) provides a practical means of extracting this information as it maps the optical path-length difference (OPD) across a tissue sample with sub-wavelength sensitivity. In this work, we employ QPI to compare the tissue disorder strength between benign and malignant breast tissue histology samples. Our results show that disease progression is marked by a significant increase in the disorder strength. Since our imaging system can be added as an upgrading module to an existing microscope, we anticipate that it can be integrated easily in the pathology work flow.


2017 ◽  
Vol 25 (2) ◽  
pp. 1573 ◽  
Author(s):  
Mingguang Shan ◽  
Mikhail E. Kandel ◽  
Gabriel Popescu

2019 ◽  
Author(s):  
Geon Kim ◽  
Daewoong Ahn ◽  
Minhee Kang ◽  
YoungJu Jo ◽  
Donghun Ryu ◽  
...  

ABSTRACTFor appropriate treatments of infectious diseases, rapid identification of the pathogens is crucial. Here, we developed a rapid and label-free method for identifying common bacterial pathogens as individual bacteria by using three-dimensional quantitative phase imaging and deep learning. We achieved 95% accuracy in classifying 19 bacterial species by exploiting the rich information in three-dimensional refractive index tomograms with a convolutional neural network classifier. Extensive analysis of the features extracted by the trained classifier was carried out, which supported that our classifier is capable of learning species-dependent characteristics. We also confirmed that utilizing three-dimensional refractive index tomograms was crucial for identification ability compared to two-dimensional imaging. This method, which does not require time-consuming culture, shows high feasibility for diagnosing patients with infectious diseases who would benefit from immediate and adequate antibiotic treatment.


2016 ◽  
Author(s):  
Joonseok Hur ◽  
Kyoohyun Kim ◽  
SangYun Lee ◽  
HyunJoo Park ◽  
YongKeun Park

Here, the actions of melittin, the active molecule of apitoxin or bee venom, were investigated on human red blood cells (RBCs) using quantitative phase imaging techniques. High-resolution realtime 3-D refractive index (RI) measurements and dynamic 2-D phase images of individual melittin-bound RBCs enabled in-depth examination of melittin-induced biophysical alterations of the cells. From the measurements, morphological, biochemical, and mechanical alterations of the RBCs were analyzed quantitatively. Furthermore, leakage of haemoglobin (Hb) inside the RBCs at high melittin concentration was also investigated.


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