Tailored 3D microphantoms: An essential tool for quantitative phase tomography analysis of organoids

• Autorzy: Ziemczonok Michał , Desissaire Sylvia , Neri Jérémy , Kuś Arkadiusz , Hervé Lionel , Fiche Cécile , Godefroy Guillaume , Fackeure Marie , Sery Damien , Krauze Wojciech , Padmanabhan Kiran , Paviolo Chiara , Kujawińska Małgorzata

Identyfikatory

DOI

10.1016/j.bbe.2025.03.003

• Języki publikacji: EN

Abstrakty

EN

We present a novel approach for benchmarking and validating quantitative phase tomography (QPT) systems using three-dimensional microphantoms. These microphantoms, crafted from biological and imaging data, replicate the optical and structural properties of multicellular biological samples. Their fabrication featuring refractive index modulation at sub-micrometer details is enabled by two-photon polymerization. We showcase the effectiveness of our technique via a round-robin test of healthy and tumoral liver organoid phantoms across three different QPT systems. This test reveals sample- and system-dependent errors in measuring dry mass and morphology. This approach constitutes a development of super phantoms for QPT - test objects that exist in both digital and physical form, replicate both the morphology and relevant aspects of physiology in specimens under healthy or diseased conditions, and underpin the assessment and refinement of imaging technologies and methodologies prior to clinical application.

Słowa kluczowe

EN

quantitative phase tomography; quantitative phase imaging; optical metrology; organoid phantom; two-photon polymerization

• Wydawca: Nałęcz Institute of Biocybernetics and Biomedical Engineering of the Polish Academy of Sciences ; Elsevier
• Czasopismo: Biocybernetics and Biomedical Engineering
• Rocznik: 2025
• Tom: Vol. 45, iss. 2
• Strony: 247--257
• Opis fizyczny: Bibliogr. 99 poz., rys., tab.

Twórcy

autor

Ziemczonok Michał

• Warsaw University of Technology, Institute of Micromechanics and Photonics, A. Boboli 8 Street, Warsaw, 02-525, Poland

• https://orcid.org/0000-0002-4802-1105

autor

Desissaire Sylvia

• Univ. Grenoble Alpes, CEA, Leti, 17 av. des Martyrs, Grenoble, F-38000, France

• https://orcid.org/0000-0002-7227-8031

autor

Neri Jérémy

• Institut de Génomique Fonctionnelle de Lyon, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard Lyon 1, Lyon, UMR 5242, France

autor

Kuś Arkadiusz

• Warsaw University of Technology, Institute of Micromechanics and Photonics, A. Boboli 8 Street, Warsaw, 02-525, Poland

• https://orcid.org/0000-0001-8009-4841

autor

Hervé Lionel

• Univ. Grenoble Alpes, CEA, Leti, 17 av. des Martyrs, Grenoble, F-38000, France

• https://orcid.org/0000-0002-2944-5498

autor

Fiche Cécile

• Univ. Grenoble Alpes, CEA, Leti, 17 av. des Martyrs, Grenoble, F-38000, France

autor

Godefroy Guillaume

• Univ. Grenoble Alpes, CEA, Leti, 17 av. des Martyrs, Grenoble, F-38000, France

• https://orcid.org/0000-0002-0538-7967

autor

Fackeure Marie

• Institut de Génomique Fonctionnelle de Lyon, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard Lyon 1, Lyon, UMR 5242, France

autor

Sery Damien

• Institut de Génomique Fonctionnelle de Lyon, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard Lyon 1, Lyon, UMR 5242, France

autor

Krauze Wojciech

• Warsaw University of Technology, Institute of Micromechanics and Photonics, A. Boboli 8 Street, Warsaw, 02-525, Poland

• https://orcid.org/0000-0002-5248-3986

autor

Padmanabhan Kiran

• Institut de Génomique Fonctionnelle de Lyon, Ecole Normale Supérieure de Lyon, CNRS, Université Claude Bernard Lyon 1, Lyon, UMR 5242, France

• https://orcid.org/0000-0001-7020-1682

autor

Paviolo Chiara

• Univ. Grenoble Alpes, CEA, Leti, 17 av. des Martyrs, Grenoble, F-38000, France

• https://orcid.org/0009-0006-8763-8470

autor

Kujawińska Małgorzata

• Warsaw University of Technology, Institute of Micromechanics and Photonics, A. Boboli 8 Street, Warsaw, 02-525, Poland

• https://orcid.org/0000-0001-6521-6951

Bibliografia

• [1] Zhao Z, Chen X, Dowbaj AM, Sljukic A, Bratlie K, Lin L, et al. Organoids. Nat Rev Methods Prim 2022;2(1):94.
• [2] Duval K, Grover H, Han L-H, Mou Y, Pegoraro AF, Fredberg J, et al. Modeling physiological events in 2D vs. 3D cell culture. Physiology 2017;32(4):266-77.
• [3] Moss SP, Bakirci E, Feinberg AW. Engineering the 3D structure of organoids. Stem Cell Rep 2025;20(1):102379.
• [4] Fatehullah A, Tan SH, Barker N. Organoids as an in vitro model of human development and disease. Nature Cell Biol 2016;18(3):246-54. http://dx.doi.org/10.1038/ncb3312.
• [5] Lancaster MA, Renner M, Martin C-A, Wenzel D, Bicknell LS, Hurles ME, et al. Cerebral organoids model human brain development and microcephaly. Nature 2013;501(7467):373-9. http://dx.doi.org/10.1038/nature12517.
• [6] Dutta D, Clevers H. Organoid culture systems to study host-pathogen interactions. Curr Opin Immunol 2017;48:15-22. http://dx.doi.org/10.1016/j.coi.2017.07.012.
• [7] Gong D, Mo J, Zhai M, Zhou F, Wang G, Ma S, et al. Advances, challenges and future applications of liver organoids in experimental regenerative medicine. Front Med 2025;11. http://dx.doi.org/10.3389/fmed.2024.1521851.
• [8] Mukashyaka P, Kumar P, Mellert DJ, Nicholas S, Noorbakhsh J, Brugiolo M, et al. High-throughput deconvolution of 3D organoid dynamics at cellular resolution for cancer pharmacology with Cellos. Nat Commun 2023;14(1). http://dx.doi.org/10.1038/s41467-023-44162-6.
• [9] Zeng G, Yu Y, Wang M, Liu J, He G, Yu S, et al. Advancing cancer research through organoid technology. J Transl Med 2024;22(1). http://dx.doi.org/10.1186/s12967-024-05824-1.
• [10] Chi KY, Kim G, Kim H, Kim H, Jo S, Lee J, et al. Optimization of culture conditions to generate vascularized multi-lineage liver organoids with structural complexity and functionality. Biomaterials 2025;314:122898. http://dx.doi.org/10.1016/j.biomaterials.2024.122898.
• [11] Sachs N, Clevers H. Organoid cultures for the analysis of cancer phenotypes. Curr Opin Genet Dev 2014;24:68-73. http://dx.doi.org/10.1016/j.gde.2013.11.012.
• [12] Harrison SP, Baumgarten SF, Verma R, Lunov O, Dejneka A, Sullivan GJ. Liver organoids: Recent developments, limitations and potential. Front Med 2021;8. http://dx.doi.org/10.3389/fmed.2021.574047.
• [13] Ko J, Hyung S, Cheong S, Chung Y, Li Jeon N. Revealing the clinical potential of high-resolution organoids. Adv Drug Deliv Rev 2024;207:115202. http://dx.doi.org/10.1016/j.addr.2024.115202.
• [14] Keshara R, Kim YH, Grapin-Botton A. Organoid imaging: Seeing development and function. Annu Rev Cell Dev Biol 2022;38(1):447-66. http://dx.doi.org/10.1146/annurev-cellbio-120320-035146.
• [15] Maharjan S, Ma C, Singh B, Kang H, Orive G, Yao J, et al. Advanced 3D imaging and organoid bioprinting for biomedical research and therapeutic applications. Adv Drug Deliv Rev 2024;208:115237. http://dx.doi.org/10.1016/j.addr.2024.115237.
• [16] Astratov VN, Sahel YB, Eldar YC, Huang L, Ozcan A, Zheludev N, et al. Roadmap on label-free super-resolution imaging. Laser & Photonics Rev 2023;17(12):2200029.
• [17] Zhao J, Wang X, Zhu J, Chukwudi C, Finebaum A, Zhang J, et al. PhaseFIT: live-organoid phase-fluorescent image transformation via generative AI. Light: Sci Appl 2023;12(1). http://dx.doi.org/10.1038/s41377-023-01296-y.
• [18] Weiss KR, Voigt FF, Shepherd DP, Huisken J. Tutorial: practical considerations for tissue clearing and imaging. Nat Protoc 2021;16(6):2732-48. http://dx.doi.org/10.1038/s41596-021-00502-8.
• [19] Brenna C, Simioni C, Varano G, Conti I, Costanzi E, Melloni M, et al. Optical tissue clearing associated with 3D imaging: application in preclinical and clinical studies. Histochem Cell Biology 2022;157(5):497-511. http://dx.doi.org/10.1007/s00418-022-02081-5.
• [20] Susaki EA, Takasato M. Perspective: extending the utility of three-dimensional organoids by tissue clearing technologies. Front Cell Dev Biology 2021;9:679226.
• [21] Pawley J. Handbook of biological confocal microscopy, vol. 236, Springer Science & Business Media; 2006.
• [22] Choi WJ, Choi ES. Optical coherence tomography and its recent applications for three-dimensional imaging of organoids. Organoid 2024;4:e7. http://dx.doi.org/10.51335/organoid.2024.4.e7.
• [23] Rogers W, Thulasi Seetha S, Refaee TAG, Lieverse RIY, Granzier RWY, Ibrahim A, et al. Radiomics: from qualitative to quantitative imaging. Br J Radiol 2020;93(1108). http://dx.doi.org/10.1259/bjr.20190948.
• [24] Branciforti F, Salvi M, D’Agostino F, Marzola F, Cornacchia S, De Titta MO, et al. Segmentation and multi-timepoint tracking of 3D cancer organoids from optical coherence tomography images using deep neural networks. Diagnostics 2024;14(12):1217. http://dx.doi.org/10.3390/diagnostics14121217.
• [25] Bao D, Wang L, Zhou X, Yang S, He K, Xu M. Automated detection and growth tracking of 3D bio-printed organoid clusters using optical coherence tomography with deep convolutional neural networks. Front Bioeng Biotechnol 2023;11. http://dx.doi.org/10.3389/fbioe.2023.1133090.
• [26] Ming Y, Hao S, Wang F, Lewis-Israeli YR, Volmert BD, Xu Z, et al. Longitudinal morphological and functional characterization of human heart organoids using optical coherence tomography. Biosens Bioelectron 2022;207:114136. http://dx.doi.org/10.1016/j.bios.2022.114136.
• [27] Wang B, Ganjee R, Khandaker I, Flohr K, He Y, Li G, et al. Deep learning based characterization of human organoids using optical coherence tomography. Biomed Opt Express 2024;15(5):3112. http://dx.doi.org/10.1364/boe.515781.
• [28] Sarri B, Chevrier V, Poizat F, Heuke S, Franchi F, De Franqueville L, et al. In vivo organoid growth monitoring by stimulated Raman histology. npj Imaging 2024;2(1). http://dx.doi.org/10.1038/s44303-024-00019-1.
• [29] Tebon PJ, Wang B, Markowitz AL, Davarifar A, Tsai BL, Krawczuk P, et al. Drug screening at single-organoid resolution via bioprinting and interferometry. Nat Commun 2023;14(1). http://dx.doi.org/10.1038/s41467-023-38832-8.
• [30] Ledwig P, Robles FE. Epi-mode tomographic quantitative phase imaging in thick scattering samples. Biomed Opt Express 2019;10(7):3605. http://dx.doi.org/10.1364/boe.10.003605.
• [31] Serafini CE, Charles S, Casteleiro Costa P, Niu W, Cheng B, Wen Z, et al. Non-invasive label-free imaging analysis pipeline for in situ characterization of 3D brain organoids. Sci Rep 2024;14(1). http://dx.doi.org/10.1038/s41598-024-72038-2.
• [32] Nguyen TL, Pradeep S, Judson-Torres RL, Reed J, Teitell MA, Zangle TA. Quan-titative phase imaging: recent advances and expanding potential in biomedicine. ACS Nano 2022;16(8):11516-44.
• [33] Park Y, Depeursinge C, Popescu G. Quantitative phase imaging in biomedicine. Nat Photonics 2018;12(10):578-89.
• [34] Hugonnet H, Kim YW, Lee M, Shin S, Hruban RH, Hong S-M, et al. Multiscale label-free volumetric holographic histopathology of thick-tissue slides with subcellular resolution. Adv Photonics 2021;3(02):1-8. http://dx.doi.org/10.1117/1.AP.3.2.026004, URL https://www.spiedigitallibrary.org/journals/advanced-photonics/volume-3/issue-02/026004/Multiscale-label-free-volumetric-holographic-histopathology-of-thick-tissue-slides/10.1117/1.AP.3.2.026004.full.
• [35] Yasuhiko O, Takeuchi K, Yamada H, Ueda Y. Multiple-scattering suppressive refractive index tomography for the label-free quantitative assessment of multicellular spheroids. Biomed Opt Express 2022;13(2):962. http://dx.doi.org/10.1364/boe.446622.
• [36] Stępień P, Ziemczonok M, Kujawińska M, Baczewska M, Valenti L, Cherubini A, et al. Numerical refractive index correction for the stitching procedure in tomographic quantitative phase imaging. Biomed Opt Express 2022;13(11):5709. http://dx.doi.org/10.1364/BOE.466403, URL https://opg.optica.org/abstract.cfm?URI=boe-13-11-5709.
• [37] Lee MJ, Lee J, Ha J, Kim G, Kim H-J, Lee S, et al. Long-term three-dimensional high-resolution imaging of live unlabeled small intestinal organoids via low-coherence holotomography. Exp Mol Med 2024. http://dx.doi.org/10.1038/s12276-024-01312-0.
• [38] Pierré W, Briard M, Godefroy G, Desissaire S, Dhellemmes M, Llano ED, et al. CNN-based approach for 3D artifact correction of intensity diffraction tomography images. Opt Express 2024;32(20):34825. http://dx.doi.org/10.1364/oe.523289.
• [39] Chowdhury S, Chen M, Eckert R, Ren D, Wu F, Repina N, et al. High-resolution 3D refractive index microscopy of multiple-scattering samples from intensity images. Optica 2019;6(9):1211. http://dx.doi.org/10.1364/OPTICA.6.001211, arXiv:1909.00023 URL https://www.osapublishing.org/abstract.cfm?URI=optica-6-9-1211.
• [40] Li J, Matlock A, Li Y, Chen Q, Zuo C, Tian L. High-speed in vitro intensity diffraction tomography. Adv Photonics 2019;1(06):1. http://dx.doi.org/10.1117/1.AP.1.6.066004, arXiv:1904.06004 URL https://www.spiedigitallibrary.org/journals/advanced-photonics/volume-1/issue-06/066004/High-speed-in-vitro-intensity-diffraction-tomography/10.1117/1.AP.1.6.066004.full.
• [41] Li J, Zhou N, Sun J, Zhou S, Bai Z, Lu L, et al. Transport of intensity diffraction tomography with non-interferometric synthetic aperture for three-dimensional label-free microscopy. Light: Sci Appl 2022;11(1):154. http://dx.doi.org/10.1038/s41377-022-00815-7, URL https://www.nature.com/articles/s41377-022-00815-7.
• [42] Lee D, Lee M, Kwak H, Kim YS, Shim J, Jung JH, et al. High-fidelity optical diffraction tomography of live organisms using iodixanol refractive index matching. Biomed Opt Express 2022;13(12):6404-15.
• [43] Verrier N, Debailleul M, Haeberlé O. Recent advances and current trends in transmission tomographic diffraction microscopy. Sensors 2024;24(5):1594.
• [44] Lim J, Ayoub AB, Antoine EE, Psaltis D. High-fidelity optical diffraction tomography of multiple scattering samples. Light: Sci Appl 2019;8(1):82.
• [45] Chen M, Ren D, Liu H-Y, Chowdhury S, Waller L. Multi-layer Born multiple-scattering model for 3D phase microscopy. Optica 2020;7(5):394-403.
• [46] Pham T-a, Soubies E, Ayoub A, Lim J, Psaltis D, Unser M. Three-dimensional optical diffraction tomography with Lippmann-Schwinger model. IEEE Trans Comput Imaging 2020;6:727-38.
• [47] Tong Z, Ren X, Zhang Z, Wang B, Miao Y, Meng G. Three-dimensional refractive index microscopy based on the multi-layer propagation model with obliquity factor correction. Opt Lasers Eng 2024;174:107966. http://dx.doi.org/10.1016/j.optlaseng.2023.107966.
• [48] Kamilov US, Papadopoulos IN, Shoreh MH, Goy A, Vonesch C, Unser M, et al. Learning approach to optical tomography. Optica 2015;2(6):517. http://dx.doi.org/10.1364/optica.2.000517.
• [49] Lim J, Psaltis D. MaxwellNet: Physics-driven deep neural network training based on Maxwell’s equations. APL Photonics 2022;7(1). http://dx.doi.org/10.1063/5.0071616.
• [50] Matlock A, Zhu J, Tian L. Multiple-scattering simulator-trained neural network for intensity diffraction tomography. Opt Express 2023;31(3):4094. http://dx.doi.org/10.1364/oe.477396.
• [51] Yang D, Zhang S, Zheng C, Zhou G, Hu Y, Hao Q. Refractive index tomography with a physics-based optical neural network. Biomed Opt Express 2023;14(11):5886. http://dx.doi.org/10.1364/boe.504242.
• [52] Choi G, Ryu D, Jo Y, Kim YS, Park W, Min H-s, et al. Cycle-consistent deep learning approach to coherent noise reduction in optical diffraction tomography. Opt Express 2019;27(4):4927. http://dx.doi.org/10.1364/oe.27.004927.
• [53] Liu T, de Haan K, Rivenson Y, Wei Z, Zeng X, Zhang Y, et al. Deep learning-based super-resolution in coherent imaging systems. Sci Rep 2019;9(1). http://dx.doi.org/10.1038/s41598-019-40554-1.
• [54] Zhou KC, Horstmeyer R. Diffraction tomography with a deep image prior. Opt Express 2020;28(9):12872. http://dx.doi.org/10.1364/oe.379200.
• [55] Gontarz M, Krauze W, Dutta V, Kujawińska M. Segment and support: a dual-purpose deep learning solution for limited angle holographic tomography. Opt Express 2024;32(26):46798. http://dx.doi.org/10.1364/oe.541288.
• [56] Kofler A, Haltmeier M, Schaeffter T, Kachelrieß M, Dewey M, Wald C, Kolbitsch C. Neural networks-based regularization for large-scale medical image reconstruction. Phys Med Biol 2020;65(13):135003. http://dx.doi.org/10.1088/1361-6560/ab990e.
• [57] Ossowski P, Kuś A, Krauze W, Tamborski S, Ziemczonok M, Kuźbicki Ł, et al. Near-infrared, wavelength, and illumination scanning holographic tomography. Biomed Opt Express 2022;13(11):5971-88.
• [58] Tian L, Waller L. 3D intensity and phase imaging from light field measurements in an LED array microscope. Optica 2015;2(2):104-11.
• [59] Kim J, Chowdhury S. Space-time inverse-scattering of translation-based motion. 2024, http://dx.doi.org/10.48550/ARXIV.2412.09403, arXiv URL https://arxiv.org/abs/2412.09403.
• [60] Krauze W, Kuś A, Ziemczonok M, Haimowitz M, Chowdhury S, Kujawińska M. 3D scattering microphantom sample to assess quantitative accuracy in tomographic phase microscopy techniques. Sci Rep 2022;12(1):19586.
• [61] Manohar S, Sechopoulos I, Anastasio MA, Maier-Hein L, Gupta R. Super phantoms: advanced models for testing medical imaging technologies. Commun Eng 2024;3(1):73. http://dx.doi.org/10.1038/s44172-024-00218-z, URL https://www.nature.com/articles/s44172-024-00218-z.
• [62] Broutier L, Andersson-Rolf A, Hindley CJ, Boj SF, Clevers H, Koo B-K, et al. Culture and establishment of self-renewing human and mouse adult liver and pancreas 3D organoids and their genetic manipulation. Nat Protoc 2016;11(9):1724-43.
• [63] Dekkers JF, Alieva M, Wellens LM, Ariese HCR, Jamieson PR, Vonk AM, et al. High-resolution 3D imaging of fixed and cleared organoids. Nat Protoc 2019;14(6):1756-71. http://dx.doi.org/10.1038/s41596-019-0160-8.
• [64] Stringer C, Wang T, Michaelos M, Pachitariu M. Cellpose: a generalist algorithm for cellular segmentation. Nature Methods 2021;18(1):100-6.
• [65] Wang H, Zhang W, Ladika D, Yu H, Gailevičius D, Wang H, et al. Two-photon polymerization lithography for optics and photonics: fundamentals, materials, technologies, and applications. Adv Funct Mater 2023;33(39):2214211.
• [66] Žukauskas A, Matulaitieṅe I, Paipulas D, Niaura G, Malinauskas M, Gadonas R. Tuning the refractive index in 3D direct laser writing lithography: towards GRIN microoptics. Laser & Photonics Rev 2015;9(6):706-12.
• [67] Ziemczonok M, Kujawińska M. Multiscale and multipurpose phantoms for 2D/3D quantitative phase imaging. Proc SPIE 2023;1238908:36-42.
• [68] Pierré W, Hervé L, Paviolo C, Mandula O, Remondiere V, Morales S, et al. 3D time-lapse imaging of a mouse embryo using intensity diffraction tomography embedded inside a deep learning framework. Appl Opt 2022;61(12):3337-48.
• [69] Van Roey J, Van der Donk J, Lagasse P. Beam-propagation method: analysis and assessment. J Opt Soc Am 1981;71(7):803-10.
• [70] Kamilov US, Papadopoulos IN, Shoreh MH, Goy A, Vonesch C, Unser M, et al. Optical tomographic image reconstruction based on beam propagation and sparse regularization. IEEE Trans Comput Imaging 2016;2(1):59-70.
• [71] Kuś A, Krauze W, Makowski PL, Kujawińska M. Holographic tomography: hardware and software solutions for 3D quantitative biomedical imaging. Etri J 2019;41(1):61-72.
• [72] Airola C, Pallozzi M, Cesari E, Cerrito L, Stella L, Sette C, et al. Hepatocellular-carcinoma-derived organoids: Innovation in cancer research. Cells 2024;13(20):1726. http://dx.doi.org/10.3390/cells13201726.
• [73] Ikawa-Yoshida A, Matsuo S, Kato A, Ohmori Y, Higashida A, Kaneko E, et al. Hepatocellular carcinoma in a mouse model fed a choline-deficient, L-amino acid-defined, high-fat diet. Int J Exp Pathol 2017;98(4):221-33. http://dx.doi.org/10.1111/iep.12240, arXiv:https://onlinelibrary.wiley.com/doi/pdf/10.1111/iep.12240 URL https://onlinelibrary.wiley.com/doi/abs/10.1111/iep.12240.
• [74] Tang S, Huang X, Wang X, Zhou X, Huang H, Qin L, Tao H, Wang Q, Tao Y. Vital and distinct roles of H2A.Z isoforms in hepatocellular carcinoma. OncoTargets Ther 2020;Volume 13:4319-37. http://dx.doi.org/10.2147/ott.s243823.
• [75] LaLone V, Aizenshtadt A, Goertz J, Skottvoll FS, Mota MB, You J, et al. Quantitative chemometric phenotyping of three-dimensional liver organoids by Raman spectral imaging. Cell Rep Methods 2023;3(4):100440.
• [76] Liu PY, Chin LK, Ser W, Chen H, Hsieh C-M, Lee C-H, et al. Cell refractive index for cell biology and disease diagnosis: past, present and future. Lab A Chip 2016;16(4):634-44.
• [77] Gul B, Ashraf S, Khan S, Nisar H, Ahmad I. Cell refractive index: Models, insights, applications and future perspectives. Photodiagnosis Photodyn Ther 2021;33:102096.
• [78] Yoon S, Kim M, Jang M, Choi Y, Choi W, Kang S, et al. Deep optical imaging within complex scattering media. Nat Rev Phys 2020;2(3):141-58.
• [79] Lambrou GI, Tagka A, Kotoulas A, Chatziioannou A, Matsopoulos GK. Physical and methodological perspectives on the optical properties of biological samples: A review. Photonics 2021;8(12):540.
• [80] Boothe T, Hilbert L, Heide M, Berninger L, Huttner WB, Zaburdaev V, et al. A tunable refractive index matching medium for live imaging cells, tissues and model organisms. Elife 2017;6:e27240.
• [81] Ziemczonok M, Kuś A, Kujawińska M. Optical diffraction tomography meets metrology - Measurement accuracy on cellular and subcellular level. Measurement 2022;195(January):111106. http://dx.doi.org/10.1016/j.measurement.2022.111106.
• [82] Park D, Lee D, Kim Y, Park Y, Lee Y-J, Lee JE, et al. Cryobiopsy: A breakthrough strategy for clinical utilization of lung cancer organoids. Cells 2023;12(14):1854. http://dx.doi.org/10.3390/cells12141854.
• [83] Wahle P, Brancati G, Harmel C, He Z, Gut G, del Castillo JS, et al. Multimodal spatiotemporal phenotyping of human retinal organoid development. Nature Biotechnol 2023;41(12):1765-75. http://dx.doi.org/10.1038/s41587-023-01747-2.
• [84] Kwon IH, Kim H-S, Kim DY, Lee H-J, Lee S-W. Utilizing optical phantoms for biomedical-optics technology: Recent advances and challenges. Curr Opt Photon 2024;8(4):327-44, URL https://opg.optica.org/copp/abstract.cfm?URI=copp-8-4-327.
• [85] Kedia N, Liu Z, Sochol RD, Tam J, Hammer DX, Agrawal A. 3-D printed photoreceptor phantoms for evaluating lateral resolution of adaptive optics imaging systems. Opt Lett 2019;44(7):1825. http://dx.doi.org/10.1364/ol.44.001825.
• [86] Lee H, Samiudin NM, Doh I, Lee S-W. Full layer retinal phantom mimicking three retinal vascular networks and curvature. In: Pfefer TJ, Hwang J, Vargas G, editors. Design and quality for biomedical technologies XVII. SPIE; 2024, p. 16. http://dx.doi.org/10.1117/12.3002187.
• [87] Werschler N, Quintard C, Nguyen S, Penninger J. Engineering next generation vascularized organoids. Atherosclerosis 2024;398:118529. http://dx.doi.org/10.1016/j.atherosclerosis.2024.118529.
• [88] Javed A, Khanna A, Palmer E, Wilde C, Zaman A, Orr G, et al. Optical coherence tomography angiography: a review of the current literature. J Int Med Res 2023;51(7). http://dx.doi.org/10.1177/03000605231187933.
• [89] Puyo L, Paques M, Atlan M. Retinal blood flow reversal quantitatively monitored in out-of-plane vessels with laser Doppler holography. Sci Rep 2021;11(1). http://dx.doi.org/10.1038/s41598-021-96877-5.
• [90] Donnarumma D, Brodoline A, Alexandre D, Gross M. Blood flow imaging in zebrafish by laser doppler digital holography. Microsc Res Tech 2016;81(2):153-61. http://dx.doi.org/10.1002/jemt.22678.
• [91] Schürmann M, Cojoc G, Girardo S, Ulbricht E, Guck J, Müller P. Three-dimensional correlative single-cell imaging utilizing fluorescence and refractive index tomography. J Biophotonics 2017;11(3). http://dx.doi.org/10.1002/jbio.201700145.
• [92] Kim K, Park Y. Tomographic active optical trapping of arbitrarily shaped objects by exploiting 3D refractive index maps. Nat Commun 2017;8(1). http://dx.doi.org/10.1038/ncomms15340.
• [93] Kvåle Løvmo M, Deng S, Moser S, Leitgeb R, Drexler W, Ritsch-Marte M. Ultrasound-induced reorientation for multi-angle optical coherence tomography. Nat Commun 2024;15(1). http://dx.doi.org/10.1038/s41467-024-46506-2.
• [94] Schädel-Ebner S, Hirsch O, Gladytz T, Gutkelch D, Licha K, Berger J, et al. 3D-printed tissue-simulating phantoms for near-infrared fluorescence imaging of rheumatoid diseases. J Biomed Opt 2022;27(07). http://dx.doi.org/10.1117/1.jbo.27.7.074702.
• [95] LaRochelle EPM, Streeter SS, Littler EA, Ruiz AJ. 3D-printed tumor phantoms for assessment of in vivo fluorescence imaging analysis methods. Mol Imaging Biology 2022;25(1):212-20. http://dx.doi.org/10.1007/s11307-022-01783-5.
• [96] Ruiz AJ, Garg S, Streeter SS, Giallorenzi MK, LaRochelle EPM, Samkoe KS, et al. 3D printing fluorescent material with tunable optical properties. Sci Rep 2021;11(1). http://dx.doi.org/10.1038/s41598-021-96496-0.
• [97] Yang L, Mayer F, Bunz UHF, Blasco E, Wegener M. Multi-material multi-photon 3D laser micro- and nanoprinting. Light: Adv Manuf 2021;2:1. http://dx.doi.org/10.37188/lam.2021.017, https://www.light-am.com/article/doi/10.37188/lam.2021.017.
• [98] de Boisredon d’Assier MA, Portafaix A, Vorontsov E, Le WT, Kadoury S. Image-level supervision and self-training for transformer-based cross-modality tumor segmentation. Med Image Anal 2024;97:103287. http://dx.doi.org/10.1016/j.media.2024.103287.
• [99] Nygate YN, Levi M, Mirsky SK, Turko NA, Rubin M, Barnea I, et al. Holographic virtual staining of individual biological cells. Proc Natl Acad Sci 2020;117(17):9223-31. http://dx.doi.org/10.1073/pnas.1919569117.

Uwagi

Opracowanie rekordu ze środków MNiSW, umowa nr POPUL/SP/0154/2024/02 w ramach programu "Społeczna odpowiedzialność nauki II" - moduł: Popularyzacja nauki (2025).