Self-Mixing Interferometry-Based Micro Flow Cytometry System for Label-Free Cells Classification

Zhao, Yu; Shen, Xuefei; Zhang, Menglei; Yu, Jingwen; Li, Jintao; Wang, Xiuhong; Perchoux, Julien; Moreira, Raul da Costa; Chen, Tao

doi:10.3390/app10020478

Cited by 12 publications

(7 citation statements)

References 38 publications

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“…It is also worth mentioning the contribution of SMI to flow cytometry (FC) for cell type discrimination and size classification using SMI. Unlike most conventional Flow Cytometry (FC) techniques, the proposed system does not require of a fluorescence labeling pretreatment with expensive dyes that can also damage the cells samples under study [ 88 ]. Despite the existing well-established label-free FC schemes, the requirement of expensive devices, such as high-speed cameras and ultrasound transducers, and the complexity of the existing technology, are shortcomings that an SMI-based flow cytometer is able to overcome.…”

Section: Self-mixing Interferometrymentioning

confidence: 99%

Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

Rey-Barroso

Peña-Gutiérrez

Yáñez

et al. 2021

Sensors

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The worldwide incidence of skin cancer has risen rapidly in the last decades, becoming one in three cancers nowadays. Currently, a person has a 4% chance of developing melanoma, the most aggressive form of skin cancer, which causes the greatest number of deaths. In the context of increasing incidence and mortality, skin cancer bears a heavy health and economic burden. Nevertheless, the 5-year survival rate for people with skin cancer significantly improves if the disease is detected and treated early. Accordingly, large research efforts have been devoted to achieve early detection and better understanding of the disease, with the aim of reversing the progressive trend of rising incidence and mortality, especially regarding melanoma. This paper reviews a variety of the optical modalities that have been used in the last years in order to improve non-invasive diagnosis of skin cancer, including confocal microscopy, multispectral imaging, three-dimensional topography, optical coherence tomography, polarimetry, self-mixing interferometry, and machine learning algorithms. The basics of each of these technologies together with the most relevant achievements obtained are described, as well as some of the obstacles still to be resolved and milestones to be met.

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Section: Self-mixing Interferometrymentioning

confidence: 99%

Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

Rey-Barroso

Peña-Gutiérrez

Yáñez

et al. 2021

Sensors

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“…2) Obtaining 512 discrete samples by sampling 0 ( ) I t and 1 ( ) I t over a rising or falling part of / 2 T  , as shown by (6) and (7). Applying 512-point FFT on the two discrete sequences 0 ( ) s I nT and 1 ( ) s I nT , as shown by (8) and (12). 3) Using (9) and (13) , where 0 d = 100 nm and t f = 10 Hz.…”

Section: A Simulation Testmentioning

confidence: 99%

“…Various SMI-based sensing applications have been reported, including the measurement of displacement, velocity, vibration, laser related parameters, thickness, mechanical resonance [1][2][3][4][5][6][7], etc. Recently, SMI-based sensing has been extended for imaging, material parameter measurement, near-field microscopy, chaotic radar, acoustic detection, biomedical applications [8][9][10][11][12], etc.…”

Section: Introductionmentioning

confidence: 99%

A New Algorithm for Displacement Measurement Using Self-Mixing Interferometry With Modulated Injection Current

Wang

Ruan

et al. 2020

IEEE Access

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Using self-mixing interferometry (SMI) with a periodical modulated injection current, high resolution displacement sensing can be achieved by retrieving the initial phase of an SMI signal at each modulation period. However, the existing initial-phase-based detection methods can only obtain a single point measurement of displacement within each single modulation period. Thus, they are only effective when the target is subject to slow movement, or the injection current is modulated by a signal of very high frequency, which are not practical in many applications. In this work, a new method is proposed to tackle the problem. Firstly, a reference signal is obtained by setting the target still. Then Fast Fourier Transform and its inverse (FFT/IFFT) are applied to the reference signal and the SMI signal, leading to a formulation to obtain the SMI signal phase, which enables the SMI system to retrieve the time varying displacement in each modulation period. As the proposed method is able to measure displacement at multiple discrete time instances (dependent on the number of samples for FFT), the measurement resolution is significantly improved over existing method. Hence, the measurement capability of the SMI system is enhanced greatly. Both simulation and experiments are conducted and the results are presented to verify the proposed algorithm.

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“…Lee et al reported high-throughput IFC by combining a quantitative phase imaging platform with time-stretch optical microscopy for the classification of human leukemic cell types (28). In addition, Zhao et al presented an optical microfluidic cytometry scheme for label-free detection of cells, which is based on self-mixing interferometry technique (29). Merola et al demonstrated that by exploiting the rolling of cells while they flow along a microfluidic channel, it is possible to obtain single-cell interferometric tomography for red blood cells (30).…”

Section: Introductionmentioning

confidence: 99%

Label‐free discrimination and selection of cancer cells from blood during flow using holography‐induced dielectrophoresis

Dudaie

Nissim

Barnea

et al. 2020

Journal of Biophotonics

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We present a method for label-free imaging and sorting of cancer cells in blood, which is based on a dielectrophoretic microfluidic chip and labelfree interferometric phase microscopy. The chip used for imaging has been embedded with dielectrophoretic electrodes, and therefore it can be used to sort the cells based on the decisions obtained during the cell flow by the label-free quantitative imaging method. Hence, we obtained a real-time, automatic, label-free imaging flow cytometry with the ability to sort the cells during flow. To validate our model, we combined into the label-free imaging interferometer a fluorescence imaging channel that indicated the correctness of the label-free sorting. We have achieved above 98% classification success and 69% sorting accuracy at flow rates of 4 to 7 μL hr −1. In the future, this method is expected to help in label-free sorting of circulating tumor cells in blood following an initial state-of-the-art cell enrichment.

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Self-Mixing Interferometry-Based Micro Flow Cytometry System for Label-Free Cells Classification

Cited by 12 publications

References 38 publications

Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

Optical Technologies for the Improvement of Skin Cancer Diagnosis: A Review

A New Algorithm for Displacement Measurement Using Self-Mixing Interferometry With Modulated Injection Current

Label‐free discrimination and selection of cancer cells from blood during flow using holography‐induced dielectrophoresis

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