IP Library › Granted Patent US 12,625,057
Granted Patent B2
US 12,625,057 · App. 18/557,517 · Granted May 12, 2026

Multi-spectral digital inline holography for biological particle classification

Inventors: Jiarong Hong (New Brighton, MN); Ruichen He (Minneapolis, MN)
Assignee: Regents of the University of Minnesota
G01N15/1434B01L3/502715G01N15/01G06V10/143G06V10/26G06V10/774G06V10/82G06V20/693G06V20/695G06V20/698B01L2300/0819B01L2300/168
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Quick Facts
Patent No.
US 12,625,057
App. No.
18/557,517
Granted
May 12, 2026
Kind
B2
Abstract

A system and method for characterizing biological particles. A multi-spectral digital inline holographic includes a computing system, a camera and a light source having a. coherent multi-spectral beam of light. Tire light source illuminates a sample having one or more biological particles and the camera captures holograms produced by interference of (i) light from the coherent multi-spectral beam of light that was scattered by the sample with (ii) light from the coherent multi-spectral beam of light that was not scattered, by the sample, the captured holograms including holograms from two or more spectral bands. The computing system applies a machine learning model to the captured holograms to extract features of the biological particles m the sample from the captured holograms.

Claims (10)

1 . A method for characterizing biological particles, the method comprising:

illuminating a sample with a coherent multi-spectral beam of light, the sample including one or more biological particles;

capturing holograms defining a spectral response of the sample, the holograms produced by interference of (i) light from the coherent multi-spectral beam of light that was scattered by the sample with (ii) light from the coherent multi-spectral beam of light that was not scattered by the sample, wherein the captured holograms including holograms from two or more spectral bands; and

applying a machine learning model to the spectral response defined by the captured holograms to extract features of the biological particles in the sample from the captured holograms.

2 . The method of claim 1 , wherein the features include one or more of biological particle localization, morphology characterization including size and shape, classification of different biological particle types, or results of biochemical analysis of the biological particles.

3 . The method of claim 2 , wherein classifications of different biological particle types including classifications of different species and different strains of the same species.

4 . The method of claim 2 , wherein results include viability and vitality of the biological particles.

5 . The method of claim 1 , wherein illuminating the sample with the coherent multi-spectral beam of light comprises feeding the multi-spectral beam of light into beam combining optics.

6 . The method of claim 1 , wherein applying a machine learning model to the spectral response of the holograms includes applying a trained convolutional neural network (CNN) to the holograms.

7 . The method of claim 1 , wherein applying a machine learning model to the spectral response of the holograms includes applying a trained you-only-look-once (YOLO) model to the holograms.

Assignments (1)
ASSIGNMENT OF ASSIGNOR'S INTEREST Recorded Feb 19, 2024
From: HONG, JIARONG; HE, RUICHEN
To: REGENTS OF THE UNIVERSITY OF MINNESOTA
Reel/Frame 066491/0280 →
Continuity (2)
Provisional Application 63201477 · Apr 30, 2021
Related Publication 20240219287A1 · Jul 4, 2024
References Cited (65)
US 10895843B2 · Hong et al. · 2021 [cited by applicant]
US 11150605B1 · Xiao · 2021 [cited by examiner]
US 20140286478A1 · Paulus · 2014 [cited by examiner]
US 20170031318A1 · Richard et al. · 2017 [cited by applicant]
US 20180018106A1 · Lee · 2018 [cited by applicant]
US 20190011882A1 · Gusyatin · 2019 [cited by applicant]
US 20200096434A1 · Deran · 2020 [cited by applicant]
US 20200285194A1 · Abdulkadir et al. · 2020 [cited by applicant]
US 20230343108A1 · Hemantharaja · 2023 [cited by examiner]
CN 110455799A · 2019 [cited by applicant]
CN 111780684A · 2019 [cited by applicant]
EP 3671176A1 · 2020 [cited by applicant]
KR 20180105332 · 2018 [cited by applicant]
WO 2019171453A1 · 2019 [cited by applicant]
Amalfitano et al., “Responses of Benthic Bacteria to Experimental Drying in Sediments from Mediterranean Temporary Rivers”, Microb Ecol, vol. 55, Jun. 2007, pp. 270-279. [cited by applicant]
Anto et al., “Algae as green energy reserve: Technological outlook on biofuel production”, Chemosphere, vol. 242, Mar. 2020, pp. 1-16. [cited by applicant]
Ashida et al., “Isolation of functional single cells from environments using a micromanipulator: application to study denitrifying bacteria”, Appl Microbio Biotechnol, Nov. 20, 2009, pp. 1211-1217. [cited by applicant]
Barer et al., “Refractometry of living cells”, Nature, Apr. 25, 1953, pp. 720-724. [cited by applicant]
Bertevello et al., “Lipid identification and transcriptional analysis of controlling enzymes in bovine ovarian follicle”, International Journal of Molecular Sciences, Oct. 2018, 31 pp. [cited by applicant]
Bian et al., “Portable multi-spectral lens-less microscope with wavelength-self-calibrating imaging sensor”, Optics and Lasers in Engineering, vol. 111, Dec. 2018, pp. 25-33. [cited by applicant]
Bista et al., “Quantification of nanoscale nuclear refractive index changes during the cell cycle”, Journal of Biomedical Optics, Jul. 2011, 4 pp. [cited by applicant]
Bochkovskiy et al., “Yolov4: Optimal speed and accuracy of object detection”, arXiv preprint, Apr. 23, 2020, 17 pp. [cited by applicant]
Bosshard et al., “Solar disinfection (SODIS) and subsequent dark storage of [cited by applicant]
Chen et al., “Deep learning in label-free cell classification”, Scientific Reports, Mar. 2016, 16 pp. [cited by applicant]
Choi et al., “Full-field optical coherence microscopy for identifying live cancer cells by quantitative measurement of refractive index distribution”, Full-field optical coherence microscopy for identifying live cancer … [cited by applicant]
Comandatore et al., “Modeling the Life Cycle of the Intramitochondrial Bacterium “ [cited by applicant]
Delvigne et al., “Microbial heterogeneity affects bioprocess robustness: Dynamic single-cell analysis contributes to understanding of microbial populations”, Biotechnology Journal, Sep. 2013, pp. 61-72. [cited by applicant]
Dharmawan et al., “Nonmechanical parfocal and autofocus features based on wave propagation distribution in lensfree holographic microscopy”, Scientific Reports, vol. 11, No. 3213, Feb. 5, 2021, 16 pp. [cited by applicant]
Feizi et al., “Rapid, portable and cost-effective yeast cell viability and concentration analysis using lensfree on-chip microscopy and machine learning”, Lap Chip, vol. 16, No. 22, Sep. 2016, pp. 4350-4358. [cited by applicant]
Feng et al., “An Optimized SYBR Green I/PI Assay for Rapid Viability Assessment and Antibiotic Susceptibility Testing for Borrelia burgdorferi”, PLOS One, vol. 9, No. 11, Nov. 2014, 8 pp. [cited by applicant]
Go et al., “Label-free sensor for automatic identification of erythrocytes using digital in-line holographic microscopy and machine learning”, Biosensors and Bioelectronics, vol. 103, Apr. 2018, pp. 12-18. [cited by applicant]
Go et al., “Machine learning-based in-line holographic sensing of unstained malaria-infected red blood cells”, Journal of Biophotonics, Apr. 19, 2018, 16 pp. [cited by applicant]
Guo et al., “High-quality multi-wavelength lensfree microscopy based on nonlinear optimization”, Optics and Lasers in Engineering, vol. 137, Feb. 2021, 8 pp. [cited by applicant]
Gurunathan et al., “Review of the isolation, characterization, biological function, and multifarious therapeutic approaches of exosomes”, vol. 8, No. 4, Apr. 3, 2019, 36 pp. [cited by applicant]
Herve et al., “Alternation of inverse problem approach and deep learning for lens-free microscopy image reconstruction”, Scientific Reports, vol. 10, No. 20207, Nov. 19, 2020, 12 pp. [cited by applicant]
Hu et al., “Biological Aerosol Particles in Polluted Regions”, Current Pollution Reports, Feb. 22, 2020, 25 pp. [cited by applicant]
International Preliminary Report on Patentability from International Application No. PCT/US2022/072030 dated Nov. 9, 2023, 9 pp. [cited by applicant]
International Search Report and Written Opinion of International Application No. PCT/US2022/072030 dated Sep. 1, 2022, 13 pp. [cited by applicant]
Jaye et al., “Translational applications of flow cytometry in clinical practice”, The Journal of Immunology, May 15, 2012, pp. 4715-4719. [cited by applicant]
Jo et al., “Label-free identification of individual bacteria using Fourier transform light scattering”, Optics express, vol. 23, No. 12, Jun. 15, 2015, 14 pp. [cited by applicant]
Katz et al., “Applications of holography in fluid mechanics and particle dynamics”, Annual Review of Fluid Mechanics, Jan. 2010, pp. 531-555. [cited by applicant]
Kim et al., “Rapid and label-free identification of individual bacterial pathogens exploiting three-dimensional quantitative phase imaging and deep learning”, BioRxiv, Apr. 2019, 20 pp. [cited by applicant]
Kumar et al., “Digital Fresnel reflection holography for highresolution 3D near-wall flow measurement”, vol. 26, No. 10, May 14, 2018, 11 pp. [cited by applicant]
Lee et al., “Rapid profiling of bovine and human milk gangliosides by matrix-assisted laser desorption/ionization Fourier transform ion cyclotron resonance mass spectrometry”, International journal of mass spectrometry,… [cited by applicant]
Li et al., “Accurate label-free 3-part leukocyte recognition with single cell lens-free imaging flow cytometry”, Comput Biol Med, May 1, 2018, pp. 147-156. [cited by applicant]
Li et al., “Overview of primary biological aerosol particles from a Chinese boreal forest: Insight into morphology, size, and mixing state at microscopic scale”, Science of the Total Environment, vol. 719, Jun. 2020, 14… [cited by applicant]
Lindstrom et al., “The role of physiological heterogeneity in microbial population behavior”, Nature Chemical Biology, vol. 6, Sep. 17, 2010, pp. 705-712. [cited by applicant]
Molaei et al., “Imaging bacterial 3D motion using digital in-line holographic microscopy and correlation-based de-noising algorithm”, Optics Express, vol. 22, No. 26, Dec. 2014, pp. 321119-32137. [cited by applicant]
Nonejuie et al., “Bacterial cytological profiling rapidly identifies the cellular pathways targeted by antibacterial molecules”, PNAS, vol. 110, No. 40, Oct. 1, 2013, pp. 16169-16174. [cited by applicant]
Rastogi et al., “Holographic optical element based digital holographic interferometer for label-free imaging of [cited by applicant]
Robertson et al., “Visible light alters yeast metabolic rhythms by inhibity respiration”, Proceedings of the National Academy of Sciences, vol. 110, No. 52, Dec. 2013, pp. 211130-21135. [cited by applicant]
Schneider et al., “Neural network for blood cell classification in a holographic microscopy system”, 2015 17th International Conference, Jul. 2015, 4 pp. [cited by applicant]
Shao et al., “Machine learning holography for measuring 3D particle distribution”, Arxiv.org, Dec. 2019, 14 pp. [cited by applicant]
Shapiro et al., “Practical Flow Cytometry”, John Wiley & Sons, Feb. 2005, 724 pp. [cited by applicant]
Singh et al., “Lablel-free, high-throughput holographic screening and enumeration of tumor cells in blood”, Lap on a Chip, vol. 17, No. 17, 2017, pp. 2920-2932, (Applicant points out, in accordance with MPEP 609.04(a), … [cited by applicant]
Song et al., “Indentification of suitable reference genes for qPCR analysis of serum microRNA in gastric cancer patients”, Digestive Diseases and Sciences, vol. 57, No. 4, Apr. 2012, pp. 897-904. [cited by applicant]
Sun et al., “In-situ DNA hybridization detection with a reflective microfiber grating biosensor”, Biosensors and Bioelectronics, vol. 61, Jun. 2014, pp. 541-546. [cited by applicant]
Sung et al., “Stain-Free Quantification of Chromosomes in Live Cells Using Regularized Tomographic Phase Microscopy”, PLOS One, vol. 7, No. 11, Nov. 2012, 7 pp. [cited by applicant]
Suresh, “Biomechanics and biophysics of cancer cells”, Acta Biomaterialia, vol. 3, No. 4, Jul. 2007, pp. 413-438. [cited by applicant]
Yan et al., “Virtual optofluidic time-stretch quantitative phase imaging”, APL Photonics, vol. 5, No. 4, Apr. 2020, 11 pp. [cited by applicant]
Yao et al., “Distinct Single-Cell Morphological Dynamics under Beta-Lactam Antibiotics”, Molecular Cell, vol. 48, Dec. 14, 2012, pp. 705-712. [cited by applicant]
Zabed et al., “Bioethanol production from fermentable sugar juice”, The Scientific World Journal, Mar. 2014, 12 pp. [cited by applicant]
Zhang et al., “The unreasonable effectiveness of deep features as a perceptual metric”, In Proceedings of the IEEE conference on Computer Vision and Pattern Recognition, Jun. 2018, pp. 586-595. [cited by applicant]
Zhou et al., “ASCII: ASsisted Classification with Ignorance Interchange”, arXiv preprint arXiv, Oct. 21, 2020, 12 pp. [cited by applicant]
Zhuang et al., “Dynamic production and loss of flagellar filaments during the bacterial life cycle”, bioRxiv, Sep. 2019, 31 pp. [cited by applicant]