Digital holography-based 3D particle localization for single-molecule tweezer techniques

Flewellen, James L.; Minoughan, Sophie; Llorente-Garcia, Isabel; Tolar, Pavel

doi:10.1016/j.bpj.2022.06.001

Cited by 4 publications

(4 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Traditional numerical calculation methods have demonstrated notable effectiveness in the domain of particle tracking [ 9 , 10 , 11 , 12 ]. Research by Parthasarathy et al [ 9 ] has revealed that the imaging particle’s intensity is radially symmetric around its center.…”

Section: Introductionmentioning

confidence: 99%

Real-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning

Luo,

Zhang,

Tan

et al. 2024

Sensors

View full text Add to dashboard Cite

In diverse realms of research, such as holographic optical tweezer mechanical measurements, colloidal particle motion state examinations, cell tracking, and drug delivery, the localization and analysis of particle motion command paramount significance. Algorithms ranging from conventional numerical methods to advanced deep-learning networks mark substantial strides in the sphere of particle orientation analysis. However, the need for datasets has hindered the application of deep learning in particle tracking. In this work, we elucidated an efficacious methodology pivoted toward generating synthetic datasets conducive to this domain that resonates with robustness and precision when applied to real-world data of tracking 3D particles. We developed a 3D real-time particle positioning network based on the CenterNet network. After conducting experiments, our network has achieved a horizontal positioning error of 0.0478 μm and a z-axis positioning error of 0.1990 μm. It shows the capability to handle real-time tracking of particles, diverse in dimensions, near the focal plane with high precision. In addition, we have rendered all datasets cultivated during this investigation accessible.

show abstract

Section: Introductionmentioning

confidence: 99%

Real-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning

Luo,

Zhang,

Tan

et al. 2024

Sensors

View full text Add to dashboard Cite

show abstract

“…As major particles, microspheres are an essential marker in characterization in forceor torsion-dependent molecular processes [1,2], fluid dynamics [3,4], single-molecule force spectroscopy [5], airborne particulate matter research [6,7], etc. They are also a favorable tool to utilize in microscopy for super-resolution imaging [8,9].…”

Section: Introductionmentioning

confidence: 99%

“…Recently, researchers have focused their efforts on two aspects. One is to broaden the application of microsphere measurement, such as the combination of DHM with optical tweezers [13,14], DHM coupled with magnetic tweezers [5], and porous sphere model creation [15,16]. The other one is to optimize the measurement, that is, to discuss the uncertainty of particle tracking [17], to improve efficiency by combining deep learning [18,19], and to reconstruct the 3D particle field by neural network [20], etc.…”

Section: Introductionmentioning

confidence: 99%

Improving the Signal-to-Noise Ratio of Axial Displacement Measurements of Microspheres Based on Compound Digital Holography Microscopy Combined with the Reconstruction Centering Method

Zeng,

Guo,

et al. 2024

Sensors

View full text Add to dashboard Cite

In 3D microsphere tracking, unlike in-plane motion that can be measured directly by a microscope, axial displacements are resolved by optical interference or a diffraction model. As a result, the axial results are affected by the environmental noise. The immunity to environmental noise increases with measurement accuracy and the signal-to-noise ratio (SNR). In compound digital holography microscopy (CDHM)-based measurements, precise identification of the tracking marker is critical to ensuring measurement precision. The reconstruction centering method (RCM) was proposed to suppress the drawbacks caused by installation errors and, at the same time, improve the correct identification of the tracking marker. The reconstructed center is considered to be the center of the microsphere, rather than the center of imaging in conventional digital holographic microscopy. This method was verified by simulation of rays tracing through microspheres and axial moving experiments. The axial displacements of silica microspheres with diameters of 5 μm and 10 μm were tested by CDHM in combination with the RCM. As a result, the SNR of the proposed method was improved by around 30%. In addition, the method was successfully applied to axial displacement measurements of overlapped microspheres with a resolution of 2 nm.

show abstract

“…However, Spiegel et al [36] found that FM100 had the potential for droplet loss during sampling from surrounding air and calculated the loss efficiency through theoretical derivation. As a non-contact three-dimensional measurement technology, digital holographic imaging has been successfully used in the field of three-dimensional motion of particles [37][38][39][40][41][42], cloud droplets detection [43,44] and biological cell imaging [45][46][47][48][49]. Madeline et al [43] observed the space structure and droplet size distribution at the smallest turbulence by using airborne holographic imaging.…”

Section: Introductionmentioning

confidence: 99%

A Comparative Investigation of Light Scattering and Digital Holographic Imaging to Measure Liquid Phase Cloud Droplets

Zhang,

Wang,

Yang

et al. 2023

Atmosphere

View full text Add to dashboard Cite

The measurement of cloud microphysical parameters plays an important role in describing characteristics of liquid phase clouds and investigating mutual relationships between clouds and precipitation. In this paper, cloud microphysical parameters at Liupan Mountain Weather Station in Ningxia are measured with a high-resolution coaxial digital holographic imager and a fog monitor 120. There are differences in the measurement results between the two instruments. The number concentration measured by the digital holographic imager is about 1.5 times that of the fog monitor 120. However, their Pearson correlation coefficient is above 0.9. Through analysis, we found that the measurement results of the digital holographic imager and fog monitor 120 are differences in 2–4 µm and 7–50µm. For the droplets with the diameters of 4–7 µm, their measurement results have good consistency. By analyzing the influence of wind field and detection sensitivity on the measurement principle, the reasons which caused the difference are proposed. Advice is given to observe topographic clouds by using the above two instruments. In addition, the differences in liquid water content and visibility are analyzed due to the absence of small and large droplets. The study provides data support for improving the accuracy of instruments in measuring cloud droplets and is useful for research in the field of cloud microphysical processes.

show abstract

Digital holography-based 3D particle localization for single-molecule tweezer techniques

Cited by 4 publications

References 36 publications

Real-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning

Real-Time 3D Tracking of Multi-Particle in the Wide-Field Illumination Based on Deep Learning

Improving the Signal-to-Noise Ratio of Axial Displacement Measurements of Microspheres Based on Compound Digital Holography Microscopy Combined with the Reconstruction Centering Method

A Comparative Investigation of Light Scattering and Digital Holographic Imaging to Measure Liquid Phase Cloud Droplets

Contact Info

Product

Resources

About