Browsing by Author "Yurdakul, C."

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    High-throughput, high-resolution interferometric light microscopy of biological nanoparticles
    (American Chemical Society, 2020-01) Yurdakul, C.; Avcı, O.; Matlock, A.; Devaux, A. J.; Quintero, M. V.; Özbay, Ekmel; Davey, R. A.; Connor, J. H.; Karl, W. C.; Tian, L.; Ünlü, M. Selim
    Label-free, visible light microscopy is an indispensable tool for studying biological nanoparticles (BNPs). However, conventional imaging techniques have two major challenges: (i) weak contrast due to low-refractive-index difference with the surrounding medium and exceptionally small size and (ii) limited spatial resolution. Advances in interferometric microscopy have overcome the weak contrast limitation and enabled direct detection of BNPs, yet lateral resolution remains as a challenge in studying BNP morphology. Here, we introduce a wide-field interferometric microscopy technique augmented by computational imaging to demonstrate a 2-fold lateral resolution improvement over a large field-of-view (>100 × 100 μm2 ), enabling simultaneous imaging of more than 104 BNPs at a resolution of ∼150 nm without any labels or sample preparation. We present a rigorous vectorial-optics-based forward model establishing the relationship between the intensity images captured under partially coherent asymmetric illumination and the complex permittivity distribution of nanoparticles. We demonstrate high-throughput morphological visualization of a diverse population of Ebola virus-like particles and a structurally distinct Ebola vaccine candidate. Our approach offers a low-cost and robust label-free imaging platform for high-throughput and high-resolution characterization of a broad size range of BNPs.
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    Resolution enhancement of wide-field interferometric microscopy by coupled deep autoencoders
    (OSA - The Optical Society, 2018) Işıl, Ç.; Yorulmaz, M.; Solmaz, B.; Turhan, Adil Burak; Yurdakul, C.; Ünlü, S.; Özbay, Ekmel; Koç, A.
    Wide-field interferometric microscopy is a highly sensitive, label-free, and low-cost biosensing imaging technique capable of visualizing individual biological nanoparticles such as viral pathogens and exosomes. However, further resolution enhancement is necessary to increase detection and classification accuracy of subdiffraction-limited nanoparticles. In this study, we propose a deep-learning approach, based on coupled deep autoencoders, to improve resolution of images of L-shaped nanostructures. During training, our method utilizes microscope image patches and their corresponding manual truth image patches in order to learn the transformation between them. Following training, the designed network reconstructs denoised and resolution-enhanced image patches for unseen input.