Multiplane quantitative phase imaging using a wavelength-multiplexed diffractive optical processor

Che-Yung Shen; Jingxi Li; Yuhang Li; Tianyi Gan; Langxing Bai; Mona Jarrahi; Aydogan Ozcan

doi:10.1117/1.AP.6.5.056003

[1] G. Popescu. Quantitative Phase Imaging of Cells and Tissues(2011).

[2] Y. Park, C. Depeursinge, G. Popescu. Quantitative phase imaging in biomedicine. Nat. Photonics, 12, 578-589(2018).

[3] J. Park et al. Artificial intelligence-enabled quantitative phase imaging methods for life sciences. Nat. Methods, 20, 1645-1660(2023).

[4] P. Marquet et al. Digital holographic microscopy: a noninvasive contrast imaging technique allowing quantitative visualization of living cells with subwavelength axial accuracy. Opt. Lett., 30, 468-470(2005).

[5] T. Ikeda et al. Hilbert phase microscopy for investigating fast dynamics in transparent systems. Opt. Lett., 30, 1165-1167(2005).

[6] G. A. Dunn, D. Zicha. Phase-shifting interference microscopy applied to the analysis of cell behaviour, 91-106(1993).

[7] S. K. Debnath, Y. Park. Real-time quantitative phase imaging with a spatial phase-shifting algorithm. Opt. Lett., 36, 4677-4679(2011).

[8] G. Popescu et al. Fourier phase microscopy for investigation of biological structures and dynamics. Opt. Lett., 29, 2503-2505(2004).

[9] B. Bhaduri et al. Diffraction phase microscopy with white light. Opt. Lett., 37, 1094-1096(2012).

[10] G. Popescu. Quantitative phase imaging of nanoscale cell structure and dynamics. Methods Cell Biol., 90, 87-115(2008).

[11] R. Kasprowicz, R. Suman, P. O’Toole. Characterising live cell behaviour: traditional label-free and quantitative phase imaging approaches. Int. J. Biochem. Cell Biol., 84, 89-95(2017).

[12] P. Wang et al. Nanoscale nuclear architecture for cancer diagnosis beyond pathology via spatial-domain low-coherence quantitative phase microscopy. J. Biomed. Opt., 15, 066028(2010).

[13] R. Horstmeyer et al. Digital pathology with Fourier ptychography. Comput. Med. Imaging Graphics, 42, 38-43(2015).

[14] Y. Rivenson et al. PhaseStain: the digital staining of label-free quantitative phase microscopy images using deep learning. Light Sci. Appl., 8, 23(2019).

[15] K. C. M. Lee et al. Quantitative phase imaging flow cytometry for ultra-large-scale single-cell biophysical phenotyping. Cytometry A, 95, 510-520(2019).

[16] G. Popescu et al. Optical imaging of cell mass and growth dynamics. Am. J. Physiol.-Cell Physiol., 295, C538-C544(2008).

[17] L. Kastl et al. Quantitative phase imaging for cell culture quality control. Cytometry A, 91, 470-481(2017).

[18] Z. El-Schich, A. Leida Mölder, A. Gjörloff Wingren. Quantitative phase imaging for label-free analysis of cancer cells—focus on digital holographic microscopy. Appl. Sci., 8, 1027(2018).

[19] D. Roitshtain et al. Quantitative phase microscopy spatial signatures of cancer cells. Cytometry A, 91, 482-493(2017).

[20] H. Wang et al. Early detection and classification of live bacteria using time-lapse coherent imaging and deep learning. Light Sci. Appl., 9, 118(2020).

[21] T. Liu et al. Rapid and stain-free quantification of viral plaque via lens-free holography and deep learning. Nat. Biomed. Eng., 7, 1040-1052(2023).

[22] K. C. Lee et al. Multi-ATOM: ultrahigh-throughput single-cell quantitative phase imaging with subcellular resolution. J. Biophotonics, 12, e201800479(2019).

[23] O. Mudanyali et al. Wide-field optical detection of nanoparticles using on-chip microscopy and self-assembled nanolenses. Nat. Photonics, 7, 247-254(2013).

[24] L. Zhong et al. Formation of monatomic metallic glasses through ultrafast liquid quenching. Nature, 512, 177-180(2014).

[25] R. Stoian, J.-P. Colombier. Advances in ultrafast laser structuring of materials at the nanoscale. Nanophotonics, 9, 4665(2020).

[26] Z. Wang et al. Spatial light interference microscopy (SLIM). Opt. Express, 19, 1016-1026(2011).

[27] T. H. Nguyen et al. Gradient light interference microscopy for 3D imaging of unlabeled specimens. Nat. Commun., 8, 210(2017).

[28] G. Zheng et al. Concept, implementations and applications of Fourier ptychography. Nat. Rev. Phys., 3, 207-223(2021).

[29] F. Charrière et al. Cell refractive index tomography by digital holographic microscopy. Opt. Lett., 31, 178-180(2006).

[30] Y. Sung et al. Optical diffraction tomography for high resolution live cell imaging. Opt. Express, 17, 266-277(2009).

[31] A. Matlock, L. Tian. High-throughput, volumetric quantitative phase imaging with multiplexed intensity diffraction tomography. Biomed. Opt. Express, 10, 6432-6448(2019).

[32] M. H. Jenkins, T. K. Gaylord. Three-dimensional quantitative phase imaging via tomographic deconvolution phase microscopy. Appl. Opt., 54, 9213-9227(2015).

[33] Y. Rivenson et al. Phase recovery and holographic image reconstruction using deep learning in neural networks. Light Sci. Appl., 7, 17141(2018).

[34] Y. Jo et al. Quantitative phase imaging and artificial intelligence: a review. IEEE J. Sel. Top. Quantum Electron., 25, 6800914(2018).

[35] Y. Rivenson, Y. Wu, A. Ozcan. Deep learning in holography and coherent imaging. Light Sci. Appl., 8, 85(2019).

[36] Y. Jo et al. Holographic deep learning for rapid optical screening of anthrax spores. Sci. Adv., 3, e1700606(2017).

[37] F. Yi, I. Moon, B. Javidi. Automated red blood cells extraction from holographic images using fully convolutional neural networks. Biomed. Opt. Express, 8, 4466-4479(2017).

[38] V. Ayyappan et al. Identification and staging of B-cell acute lymphoblastic leukemia using quantitative phase imaging and machine learning. ACS Sens., 5, 3281-3289(2020).

[39] F. Wang et al. Phase imaging with an untrained neural network. Light Sci. Appl., 9, 77(2020).

[40] H. Chen et al. Fourier Imager Network (FIN): a deep neural network for hologram reconstruction with superior external generalization. Light Sci. Appl., 11, 254(2022).

[41] L. Huang et al. Self-supervised learning of hologram reconstruction using physics consistency. Nat. Mach. Intell., 5, 895-907(2023).

[42] D. Pirone et al. Speeding up reconstruction of 3D tomograms in holographic flow cytometry via deep learning. Lab. Chip, 22, 793(2022).

[43] H. Chen et al. eFIN: enhanced Fourier imager network for generalizable autofocusing and pixel super-resolution in holographic imaging. IEEE J. Sel. Top. Quantum Electron., 29, 1-10(2023).

[44] D. Pirone et al. Label-free liquid biopsy through the identification of tumor cells by machine learning-powered tomographic phase imaging flow cytometry. Sci. Rep., 13, 6042(2023).

[45] T. Nguyen et al. Automatic phase aberration compensation for digital holographic microscopy based on deep learning background detection. Opt. Express, 25, 15043-15057(2017).

[46] V. K. Lam et al. Quantitative assessment of cancer cell morphology and motility using telecentric digital holographic microscopy and machine learning. Cytometry A, 93, 334-345(2018).

[47] Y. Wu et al. Extended depth-of-field in holographic imaging using deep-learning-based autofocusing and phase recovery. Optica, 5, 704-710(2018).

[48] H. Byeon, T. Go, S. J. Lee. Deep learning-based digital in-line holographic microscopy for high resolution with extended field of view. Opt. Laser Technol., 113, 77-86(2019).

[49] Y. Wu et al. Bright-field holography: cross-modality deep learning enables snapshot 3D imaging with bright-field contrast using a single hologram. Light Sci. Appl., 8, 25(2019).

[50] A. Matlock, J. Zhu, L. Tian. Multiple-scattering simulator-trained neural network for intensity diffraction tomography. Opt. Express, 31, 4094-4107(2023).

[51] I. Kang, A. Goy, G. Barbastathis. Dynamical machine learning volumetric reconstruction of objects’ interiors from limited angular views. Light Sci. Appl., 10, 74(2021).

[52] R. Liu et al. Recovery of continuous 3D refractive index maps from discrete intensity-only measurements using neural fields. Nat. Mach. Intell., 4, 781-791(2022).

[53] G. Choi et al. Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography. Opt. Express, 27, 4927-4943(2019).

[54] X. Lin et al. All-optical machine learning using diffractive deep neural networks. Science, 361, 1004-1008(2018).

[55] Y. Luo et al. Design of task-specific optical systems using broadband diffractive neural networks. Light Sci. Appl., 8, 112(2019).

[56] D. Mengu et al. Analysis of diffractive optical neural networks and their integration with electronic neural networks. IEEE J. Sel. Top. Quantum Electron., 26, 1-14(2019).

[57] D. Mengu et al. Misalignment resilient diffractive optical networks. Nanophotonics, 9, 4207-4219(2020).

[58] O. Kulce et al. All-optical information-processing capacity of diffractive surfaces. Light Sci. Appl., 10, 25(2021).

[59] O. Kulce et al. All-optical synthesis of an arbitrary linear transformation using diffractive surfaces. Light Sci. Appl., 10, 196(2021).

[60] D. Mengu, A. Ozcan. All-optical phase recovery: diffractive computing for quantitative phase imaging. Adv. Opt. Mater., 10, 2200281(2022).

[61] B. Bai et al. To image, or not to image: class-specific diffractive cameras with all-optical erasure of undesired objects. eLight, 2, 14(2022).

[62] J. Li et al. Massively parallel universal linear transformations using a wavelength-multiplexed diffractive optical network. Adv. Photonics, 5, 016003(2023).

[63] J. Li et al. Unidirectional imaging using deep learning–designed materials. Sci. Adv., 9, eadg1505(2023).

[64] D. Mengu et al. Snapshot multispectral imaging using a diffractive optical network. Light Sci. Appl., 12, 86(2023).

[65] C.-Y. Shen et al. Multispectral quantitative phase imaging using a diffractive optical network. Adv. Intell. Syst., 5, 2300300(2023).

[66] M. S. Sakib Rahman, A. Ozcan. Computer-free, all-optical reconstruction of holograms using diffractive networks. ACS Photonics, 8, 3375-3384(2021).

[67] J. Li et al. Spectrally encoded single-pixel machine vision using diffractive networks. Sci. Adv., 7, eabd7690(2021).

[68] Y. Li et al. Quantitative phase imaging (QPI) through random diffusers using a diffractive optical network. Light Adv. Manuf., 4, 17(2023).

[69] S. Dottermusch et al. Exposure-dependent refractive index of Nanoscribe IP-Dip photoresist layers. Opt. Lett., 44, 29(2019).

[70] D. Mengu et al. Snapshot multispectral imaging using a diffractive optical network. Light Sci. Appl., 12, 86(2023).

[71] B. Chen, J. J. Stamnes. Validity of diffraction tomography based on the first Born and the first Rytov approximations. Appl. Opt., 37, 2996-3006(1998).

[72] Y. Li et al. Universal polarization transformations: spatial programming of polarization scattering matrices using a deep learning-designed diffractive polarization transformer. Adv. Mater., 35, 2303395(2023).

[73] C.-Y. Shen et al. All-optical phase conjugation using diffractive wavefront processing. Nat. Commun., 15, 4989(2024).

[74] M. S. S. Rahman et al. Universal linear intensity transformations using spatially incoherent diffractive processors. Light Sci. Appl., 12, 195(2023).

[75] X. Yang et al. Complex-valued universal linear transformations and image encryption using spatially incoherent diffractive networks. Adv. Photonics Nexus, 3, 016010(2024).

[76] E. Goi et al. Nanoprinted high-neuron-density optical linear perceptrons performing near-infrared inference on a CMOS chip. Light Sci. Appl., 10, 40(2021).

[77] H. Chen et al. Diffractive deep neural networks at visible wavelengths. Engineering, 7, 1483-1491(2021).

[78] B. Bai et al. Data‐class‐specific all‐optical transformations and encryption. Adv. Mater., 35, e2212091(2023).

[79] C. Zuo et al. Lensless phase microscopy and diffraction tomography with multi-angle and multi-wavelength illuminations using a LED matrix. Opt. Express, 23, 14314-14328(2015).

[80] W. Luo et al. Pixel super-resolution using wavelength scanning. Light Sci. Appl., 5, e16060(2016).

[81] X. Wu et al. Wavelength-scanning pixel-super-resolved lens-free on-chip quantitative phase microscopy with a color image sensor. APL Photonics, 9, 016111(2024).

微信扫一扫：分享

微信扫一扫：分享