KEYWORDS: Digital holography, 3D image reconstruction, Reverse modeling, Holograms, Deep learning, Mathematical optimization, Image restoration, Education and training
Untrained Physics-based Deep Learning (DL) methods for digital holography have gained significant attention due to their benefits, such as not requiring an annotated training dataset, and providing interpretability since utilizing the governing laws of hologram formation. However, they are sensitive to the hard-to-obtain precise object distance from the imaging plane, posing the Autofocusing challenge. Conventional solutions involve reconstructing image stacks for different potential distances and applying focus metrics to select the best results, which apparently is computationally inefficient. In contrast, recently developed DL-based methods treat it as a supervised task, which again needs annotated data and lacks generalizability. To address this issue, we propose reverse-attention loss, a weighted sum of losses for all possible candidates with learnable weights. This is a pioneering approach to addressing the Autofocusing challenge in untrained deep-learning methods. Both theoretical analysis and experiments demonstrate its superiority in efficiency and accuracy. Interestingly, our method presents a significant reconstruction performance over rival methods (i.e. alternating descent-like optimization, non-weighted loss integration, and random distance assignment) and even is almost equal to that achieved with a precisely known object distance. For example, the difference is less than 1dB in PSNR and 0.002 in SSIM for the target sample in our experiment.
Quantitative phase imaging (QPI) has emerged as a valuable method in biomedical research by providing label-free, high-resolution phase distribution of transparent cells and tissues. While QPI is limited to transparent samples, quantitative oblique back-illumination microscopy (qOBM) is a novel imaging technology that enables epi-mode 3D quantitative phase imaging and refractive index (RI) tomography of thick scattering samples. This technology employs four oblique back illumination images taken at the same focal planes, along with a rapid 2D deconvolution reconstruction algorithm, to generate 2D phase cross-sections of thick samples. Alternatively, a through-focus z-stack of oblique back illumination images can be utilized to produce 3D RI tomograms, offering enhanced RI quantitative accuracy. However, 3D RI generation requires a more computationally intensive reconstruction process, preventing its potential of a real-time 3D RI tomography. In this paper, we propose a neural network-involved reconstruction technique that significantly reduces the processing time to a third while maintaining high fidelity compared to the deconvolution-based results.
Quantitative oblique back illumination microscopy (qOBM) is a recently developed phase imaging modality that enables 3D quantitative phase imaging and refractive index (RI) tomography of thick scattering samples. The approach uses four oblique illumination images (acquired in epi-mode) at a given focal plane to obtain cross sectional quantitative information. In order to quantify the information, qOBM uses a deconvolution algorithm which requires an estimate of the angular distribution of light at the focal plane to obtain the system’s optical transfer function (OTF). This information is obtained using Monte Carlo numerical simulations which uses published scattering parameters of tissues. While this approach has shown robust results with high quantitative fidelity, the reliance on available published scattering parameters is not optimal. Here we present an experimental approach to measure the angular distribution of the back-scattered light at the focal plane. The approach simultaneously obtains information from the imaging plane and the Fourier plane to provide insight into the overall angular distribution of light at the focal plane. Together with the pupil function, given by the known numerical aperture of the system, this approach directly yields the OTF. A theoretical analysis and experimental results will be presented. This approach has the potential to widen the utility of qOBM to also include tissues and samples whose scattering properties are not well documented in the literature.
Phase imaging and fluorescence microscopy provide valuable complementary information, and individually form the basis for a significant portion of the routing biological and biomedical optical imaging performed today. While multimodal phase and fluorescence microscopy has been explored for thin transparent samples to obtain structural information based on the refractive index distribution (with phase contrast) and molecular content (with fluorescence), combining these complementary technologies to study thick samples has been challenging and remains largely unexplored. This work presents the results of a study that combines quantitative phase imaging (QPI) and refractive index (RI) tomography in thick samples—using quantitative oblique back illumination—and bright field fluorescence deconvolution microscopy. The two technologies use a simple bright field microscope configuration with epi-illumination and through-focus z-stack acquisition, along with a deconvolution algorithm, to achieve 3D imaging. Phase and RI information is acquired nearly simultaneously with the fluorescence information with inherent co-registration of the two modalities. In this work, we will present the theoretical underpinning of this multimodal approach, describe the simple multimodal system, and show imaging results of thick tissues, such as labeled mice brains. This multimodal imaging approach could help biologists and clinicians gain a more comprehensive understanding of the tissue’s morphology and molecular composition, and can be widely applied across a number of biological and biomedical disciplines, including neuroscience, pathology, and oncology.
KEYWORDS: Biological samples, Phase imaging, Light sources and illumination, In vivo imaging, Imaging systems, Biological imaging, Brain, Tumors, Real time imaging, Design
Quantitative phase imaging (QPI) offers label-free access to refractive index information of biological samples, which can achieve nanometer-level optical-path-length sensitivity with cellular/sub-cellular biophysical and histological details. Recently we introduced quantitative oblique back-illumination microscopy (qOBM) which works in epi-mode and uses multiply scattered photons within thick samples to yield quantitative phase in thick scattering tissues, thus overcoming QPI’s long-standing limitation to thin transparent samples. qOBM provides real-time quantitative phase in 3D, and can be configured in a compact form factor. Here we describe a handheld qOBM probe, suitable for in-vivo diagnostic applications such as brain tumor assessment, dermatology, and more.
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