Dynamic light scattering arising from flowing Brownian particles: analytical model in optical coherence tomography conditions

2014 ◽  
Vol 19 (12) ◽  
pp. 127004 ◽  
Author(s):  
Ivan Popov ◽  
Andrew S. Weatherbee ◽  
I. Alex Vitkin
2011 ◽  
Vol 16 (7) ◽  
pp. 070505 ◽  
Author(s):  
Golnaz Farhat ◽  
Adrian Mariampillai ◽  
Victor X. D. Yang ◽  
Gregory J. Czarnota ◽  
Michael C. Kolios

2012 ◽  
Author(s):  
Golnaz Farhat ◽  
Adrian Mariampillai ◽  
Kenneth K. C. Lee ◽  
Victor X. D. Yang ◽  
Gregory J. Czarnota ◽  
...  

2012 ◽  
Vol 20 (20) ◽  
pp. 22262 ◽  
Author(s):  
Jonghwan Lee ◽  
Weicheng Wu ◽  
James Y. Jiang ◽  
Bo Zhu ◽  
David A. Boas

2021 ◽  
Author(s):  
Golnaz Farhat ◽  
Adrian Mariampillai ◽  
Victor X. D. Yang ◽  
Gregory J. Czarnota ◽  
Michael C. Kolios

Detecting apoptosis using dynamic light scattering with optical coherence tomography


2021 ◽  
Author(s):  
Timothy Wan Hei Luk

Optical coherence tomography (OCT) is an imaging modality that uses near infrared light interferometry for non-invasive, near-histological resolution imaging at the micron level. Concepts from dynamic light scattering (DLS) can be adapted to OCT to detect and measure the motions in the target tissue. Tissue dynamics can be observed by measuring the speckle decorrelation time (DT) of the tissue. DT analysis was performed in a preclinical study to demonstrate the repeatability and feasibility of using DLS-OCT to observe mouse tumours undergoing cisplatin treatment over a 48-hour period. Differences in the average DT data were observed for control and cisplatin-injected mice. Image segmentation based on DT values was also performed to subtract the DT contributions of pixels at blood vessel locations, resulting in the improvement of average DT calculations of the tumour tissue. The results presented are a preliminary step to analyzing and monitoring tumour growth and treatment response in vivo.


2021 ◽  
Author(s):  
Nico Joseph John Arezza

Dynamic light scattering (DLS) techniques can provide information about the quantity, size, and motion of light scatterers within a volume based on temporal fluctuations in its light scattering profile. In DLS, autocorrelation functions (ACFs) are computed from light intensity vs time signals acquired from optical imaging setups. A parameter known as the decorrelation time is computed from each ACF and is inversely related to the average motion speed of scatterers within the imaging volume. Optical coherence tomography is an imaging modality that generates 2D cross-sectional images based on light backscattered from a sample, and the combination of DLS with OCT is known as dynamic light scattering optical coherence tomography (DLS-OCT). Previously, DLS-OCT has been used to detect apoptosis, a form of programmed cell death, in non-adherent leukemia cells. Cells undergoing apoptosis experience predictable morphological changes that results in an increase in intracellular motion, and therefore a decrease in decorrelation time. We applied DLS-OCT methods to quantify the decorrelation times in adherent breast cancer cell pellets that were either untreated, treated with 20 ng/mL paclitaxel for 24 or 48 hours, or deprived of media for 24 or 48 hours. The mean decorrelation times in the paclitaxel-treated and nutrient deprived groups were significantly lower than in the untreated cells (p<0.05), suggestive of increased intracellular motion due to morphological cellular changes associated with cell death. We also investigated a new model to fit to ACFs generated by DLS-OCT of cell pellets. Typically, ACFs are fit to single exponential decay curves. We developed a model that expresses the ACFs from in vitro experiments as a sum of multiple exponential decay curves using an algorithm known as CONTIN. The curves produced by CONTIN fitted the experimental data much better than the single exponential decay fits. We speculate that the CONTIN fits, each of which resembled a superposition of three exponential decay functions, may result from light scattered from three different types of scatterers within cells, such as lysosomes, mitochondria, and nuclei.


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