@article{rai_li_ghashghaei_greenbaum_2023, title={Deep learning-based adaptive optics for light sheet fluorescence microscopy}, volume={14}, ISSN={["2156-7085"]}, DOI={10.1364/BOE.488995}, abstractNote={Light sheet fluorescence microscopy (LSFM) is a high-speed imaging technique that is often used to image intact tissue-cleared specimens with cellular or subcellular resolution. Like other optical imaging systems, LSFM suffers from sample-induced optical aberrations that decrement imaging quality. Optical aberrations become more severe when imaging a few millimeters deep into tissue-cleared specimens, complicating subsequent analyses. Adaptive optics are commonly used to correct sample-induced aberrations using a deformable mirror. However, routinely used sensorless adaptive optics techniques are slow, as they require multiple images of the same region of interest to iteratively estimate the aberrations. In addition to the fading of fluorescent signal, this is a major limitation as thousands of images are required to image a single intact organ even without adaptive optics. Thus, a fast and accurate aberration estimation method is needed. Here, we used deep-learning techniques to estimate sample-induced aberrations from only two images of the same region of interest in cleared tissues. We show that the application of correction using a deformable mirror greatly improves image quality. We also introduce a sampling technique that requires a minimum number of images to train the network. Two conceptually different network architectures are compared; one that shares convolutional features and another that estimates each aberration independently. Overall, we have presented an efficient way to correct aberrations in LSFM and to improve image quality.}, number={6}, journal={BIOMEDICAL OPTICS EXPRESS}, author={Rai, Mani Ratnam and Li, Chen and Ghashghaei, H. Troy and Greenbaum, Alon}, year={2023}, month={Jun}, pages={2905–2919} }
 @article{li_rai_ghashghaei_greenbaum_2022, title={Illumination angle correction during image acquisition in light-sheet fluorescence microscopy using deep learning}, volume={13}, ISSN={["2156-7085"]}, DOI={10.1364/BOE.447392}, abstractNote={Light-sheet fluorescence microscopy (LSFM) is a high-speed imaging technique that provides optical sectioning with reduced photodamage. LSFM is routinely used in life sciences for live cell imaging and for capturing large volumes of cleared tissues. LSFM has a unique configuration, in which the illumination and detection paths are separated and perpendicular to each other. As such, the image quality, especially at high resolution, largely depends on the degree of overlap between the detection focal plane and the illuminating beam. However, spatial heterogeneity within the sample, curved specimen boundaries, and mismatch of refractive index between tissues and immersion media can refract the well-aligned illumination beam. This refraction can cause extensive blur and non-uniform image quality over the imaged field-of-view. To address these issues, we tested a deep learning-based approach to estimate the angular error of the illumination beam relative to the detection focal plane. The illumination beam was then corrected using a pair of galvo scanners, and the correction significantly improved the image quality across the entire field-of-view. The angular estimation was based on calculating the defocus level on a pixel level within the image using two defocused images. Overall, our study provides a framework that can correct the angle of the light-sheet and improve the overall image quality in high-resolution LSFM 3D image acquisition.}, number={2}, journal={BIOMEDICAL OPTICS EXPRESS}, author={Li, Chen and Rai, Mani Ratnam and Ghashghaei, H. Troy and Greenbaum, Alon}, year={2022}, month={Feb}, pages={888–901} }
 @article{rai_li_greenbaum_2022, title={Quantitative analysis of illumination and detection corrections in adaptive light sheet fluorescence microscopy}, volume={13}, ISSN={["2156-7085"]}, DOI={10.1364/BOE.454561}, abstractNote={Light-sheet fluorescence microscopy (LSFM) is a high-speed, high-resolution and minimally phototoxic technique for 3D imaging of in vivo and in vitro specimens. LSFM exhibits optical sectioning and when combined with tissue clearing techniques, it facilitates imaging of centimeter scale specimens with micrometer resolution. Although LSFM is ubiquitous, it still faces two main challenges that effect image quality especially when imaging large volumes with high-resolution. First, the light-sheet illumination plane and detection lens focal plane need to be coplanar, however sample-induced aberrations can violate this requirement and degrade image quality. Second, introduction of sample-induced optical aberrations in the detection path. These challenges intensify when imaging whole organisms or structurally complex specimens like cochleae and bones that exhibit many transitions from soft to hard tissue or when imaging deep (> 2 mm). To resolve these challenges, various illumination and aberration correction methods have been developed, yet no adaptive correction in both the illumination and the detection path have been applied to improve LSFM imaging. Here, we bridge this gap, by implementing the two correction techniques on a custom built adaptive LSFM. The illumination beam angular properties are controlled by two galvanometer scanners, while a deformable mirror is positioned in the detection path to correct for aberrations. By imaging whole porcine cochlea, we compare and contrast these correction methods and their influence on the image quality. This knowledge will greatly contribute to the field of adaptive LSFM, and imaging of large volumes of tissue cleared specimens.}, number={5}, journal={BIOMEDICAL OPTICS EXPRESS}, author={Rai, Mani Ratnam and Li, Chen and Greenbaum, Alon}, year={2022}, month={May}, pages={2960–2974} }