Browsing by Subject "Biochemical Research Methods"

Now showing 1 - 3 of 3
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    High-speed widefield photoacoustic microscopy of small-animal hemodynamics.
    (Biomedical optics express, 2018-10) Lan, Bangxin; Liu, Wei; Wang, Ya-Chao; Shi, Junhui; Li, Yang; Xu, Song; Sheng, Huaxin; Zhou, Qifa; Zou, Jun; Hoffmann, Ulrike; Yang, Wei; Yao, Junjie
    Optical-resolution photoacoustic microscopy (OR-PAM) has become a popular tool in small-animal hemodynamic studies. However, previous OR-PAM techniques variously lacked a high imaging speed and/or a large field of view, impeding the study of highly dynamic physiologic and pathophysiologic processes over a large region of interest. Here we report a high-speed OR-PAM system with an ultra-wide field of view, enabled by an innovative water-immersible hexagon-mirror scanner. By driving the hexagon-mirror scanner with a high-precision DC motor, the new OR-PAM has achieved a cross-sectional frame rate of 900 Hz over a 12-mm scanning range, which is 3900 times faster than our previous motor-scanner-based system and 10 times faster than the MEMS-scanner-based system. Using this hexagon-scanner-based OR-PAM system, we have imaged epinephrine-induced vasoconstriction in the whole mouse ear and vascular reperfusion after ischemic stroke in the mouse cortex in vivo, with a high spatial resolution and high volumetric imaging speed. We expect that the hexagon-scanner-based OR-PAM system will become a powerful tool for small animal imaging where the hemodynamic responses over a large field of view are of interest.
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    Learned sensing: jointly optimized microscope hardware for accurate image classification.
    (Biomedical optics express, 2019-12) Muthumbi, Alex; Chaware, Amey; Kim, Kanghyun; Zhou, Kevin C; Konda, Pavan Chandra; Chen, Richard; Judkewitz, Benjamin; Erdmann, Andreas; Kappes, Barbara; Horstmeyer, Roarke
    Since its invention, the microscope has been optimized for interpretation by a human observer. With the recent development of deep learning algorithms for automated image analysis, there is now a clear need to re-design the microscope's hardware for specific interpretation tasks. To increase the speed and accuracy of automated image classification, this work presents a method to co-optimize how a sample is illuminated in a microscope, along with a pipeline to automatically classify the resulting image, using a deep neural network. By adding a "physical layer" to a deep classification network, we are able to jointly optimize for specific illumination patterns that highlight the most important sample features for the particular learning task at hand, which may not be obvious under standard illumination. We demonstrate how our learned sensing approach for illumination design can automatically identify malaria-infected cells with up to 5-10% greater accuracy than standard and alternative microscope lighting designs. We show that this joint hardware-software design procedure generalizes to offer accurate diagnoses for two different blood smear types, and experimentally show how our new procedure can translate across different experimental setups while maintaining high accuracy.
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    ItemOpen Access
    Wide-field whole eye OCT system with demonstration of quantitative retinal curvature estimation
    (Biomedical Optics Express, 2019-01-01) McNabb, Ryan P; Polans, James; Keller, Brenton; Jackson-Atogi, Moseph; James, Charlene L; Vann, Robin R; Izatt, Joseph A; Kuo, Anthony N