KEYWORDS: Tissues, Microscopy, Tumors, Standards development, In vivo imaging, Education and training, Data modeling, 3D imaging standards, Spatial resolution, Phase imaging
Quantitative oblique back-illumination microscopy (qOBM) is a novel technology for label-free imaging of thick (unsectioned) tissue specimens, demonstrating high spatial resolution and 3-D capabilities. The grayscale contrast however, is unfamiliar to pathologist and histotechnicians without familiarization, limiting its adoption. We used deep learning techniques to convert qOBM into virtual H&E, observing successful conversion of both healthy and tumor thick (unsectioned) specimens. Transfer learning was demonstrated on a second collection of qOBM and H&E images of human astrocytoma specimens. With some improvement in robustness and generalizability, we anticipate that this approach can find clinical application.
KEYWORDS: 3D image processing, Biological imaging, Tissues, Stereoscopy, Real time imaging, Light sources and illumination, Biomedical applications, Phase contrast, In vivo imaging, 3D applications
Quantitative oblique-back-illumination microscopy (qOBM) enables quantitative phase imaging (QPI) with epi-illumination, and thus permits the use of phase contrast in applications that were previously out-of-reach for QPI, including clinical medicine. Here, I will discuss our latest efforts to apply qOBM for clinical applications, specifically tissue imaging for non-invasive diagnostics and image guided therapy. Our approach uses an unsupervised cycle generative adversarial networks to translate 3D phase images of thick fresh tissues to appear like H&E-stained tissue sections. This work paves the way for non-invasive, label-free, real-time 3D H&E imaging which can be transformative for disease detection and guided therapy.
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.
Quantitative oblique back-illumination microscopy (qOBM) is a label-free imaging technique that enables tomographic phase imaging of thick scattering samples with epi-illumination. Here, we propose the use of two forms of functional imaging with qOBM to study tissue and cell cultures. In doing so, we obtain the spatiotemporal and quantitative functional information associated with the phase values extrapolated from qOBM imaging. We have applied this process to study the efficacy of individual immune T cells to kill glioblastoma spheroid cultures in 3D spheroids. Data show that we can effectively distinguish between cell phenotypes and characterize the dynamic motion of these cells in 3D cultures. This work offers a distinct advantage in tracking 3D cellular dynamics in thick tissue as many function imaging modalities are limited to 2D samples. Further, this technology can be expanded to analyze a wide variety of cellular and subcellular dynamics non-invasively in thick tissue.
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.
Quantitative oblique back-illumination microscopy (qOBM) enables quantitative phase imaging (QPI) in thick samples using epi-illumination. While qOBM offers unprecedented access to refractive index (RI) information in arbitrarily thick scattering samples, QPI-based (or RI index based) imaging still suffers from low cell nuclear contrast, which important for disease detection, including cancer. In this work, we use the acetowhitening effect of acetic acid to enhance the nuclear phase contrast of thick fresh tissue samples. Imaging results from brain samples are presented. Acetic acid phase staining may have important implications for in-vivo QPI-based disease detection
Quantitative oblique back-illumination microscopy (qOBM) is a label-free imaging technique that enables threedimensional phase imaging of thick samples with epi-illumination. Here, we present a preliminary study using qOBM to monitor sickle cell disease in mice. We have used qOBM to image the brains of recently sacrificed PBS-perfused control mice and mice with sickle cell disease. Quantitative phase images revealed morphological differences in the blood vessel structure coupled with blockages of cortex vessels where potential strokes occurred. We demonstrate that qOBM enables visualizing these differences with future applications for in-vivo monitoring of sickle cell blood flow.
Quantitative phase imaging (QPI) enables label-free optical-path-length measurement of biological samples with nanometer-scale sensitivity, which offers unparalleled access to important histological and biophysical properties of cells and tissues. However, traditional QPI methods require a transmission-based optical geometry and are thus restricted to thin samples, which prevents the use of QPI for in-vivo applications. In this work, we present the design, characterization, and experimental validation of a handheld rigid probe for QPI with epi-illumination, using an optimized lighting configuration to achieve high phase-contrast sensitivity. The approach is based on a recently developed technology called quantitative oblique back illumination microscopy (qOBM). We demonstrate the real-time operation of our system with the future goal of applying it to help guide human brain tumor margin assessment intraoperatively in vivo, among many other potential applications.
Slide-free microscopy techniques have been proposed for accelerating standard histopathology and intraoperative guidance. One such technology is quantitative oblique back-illumination microscopy (qOBM), which enables real-time, label-free quantitative phase imaging of thick, unsectioned in-vivo and ex-vivo tissues. However, the grayscale phase contrast provided by qOBM differs from the colored histology images familiar to pathologists and clinicians, limiting its current potential for adoption. Here we demonstrate the application of unsupervised deep learning using a Cycleconsistent Generative Adversarial Network (CycleGAN) model to transform qOBM images into virtual hematoxylin and eosin (H&E)-stained images. The models were trained on a dataset of qOBM and H&E images of similar regions in excised brain tissue from a 9 L gliosarcoma rat tumor model. We observed successful qOBM-to-H&E conversion of both uninvolved and tumor-containing specimens, as demonstrated by a classifier test. We describe several crucial preprocessing steps that improve the quality of conversion, such as intensity inversion, pixel harmonization, and color normalization. This unsupervised deep learning framework does exhibit occasional subpar performance; for example, as with GANs in general, it can create so-called “hallucinations”, displaying features not actually present in the original qOBM images. We anticipate that this behavior can be minimized with more extensive training and deployment of advanced ML techniques, and that virtual-H&E-converted qOBM imaging will prove safe and appropriate for rapid tissue imaging applications.
Quantitative oblique back-illumination microscopy (qOBM) is a label-free imaging technique that enables tomographic phase imaging of thick scattering samples with epi-illumination. Here, we apply qOBM to image three-dimensional brain organoid cell cultures of tuberous sclerosis complex (TSC) disease. We identify quantitative differences that occur between the TSC organoids and a control cell line, and discuss the implications of these differences on our understanding the development of TSC organoid cultures. These differences include disruptions in the tubular processes in the organoid, a higher degree of folding and non-spherical cell growth, and differences in the proliferating cell structures between the two groups.
Quantitative oblique back-illumination microscopy (qOBM) is a novel microscopy technology that enables real-time, label-free quantitative phase imaging (QPI) of thick and intact tissue specimens. This approach has the potential to address a number of important biomedical challenges. In particular, qOBM could enable in-situ/in-vivo imaging of tissue during surgery for intraoperative guidance, as opposed to the technically challenging and often unsatisfactory ex-vivo approach of frozen-section-based histology. However, the greyscale phase contrast provided by qOBM differ from the colorized histological contrasts most familiar to pathologists and clinicians, limiting potential adoption in the medical field. Here, we demonstrate the use of a CycleGAN (generative adversarial network), an unsupervised deep learning framework, to transform qOBM images into virtual H&E. We trained CycleGAN models on a collection of qOBM and H&E images of excised brain tissue from a 9L gliosarcoma rat tumor model. We observed successful mode conversion of both healthy and tumor specimens, faithfully replicating features of the qOBM images in the style of traditional H&E. Some limitations were observed however, including attention-based constraints in the CycleGAN framework that occasionally allowed the model to ‘hallucinate’ features not actually present in the qOBM images used. Strategies for preventing these hallucinations, comprising both improved hardware capabilities and more stringent software constraints, will be discussed. Our results indicate that deep learning could potentially bridge the gap between qOBM and traditional histology, an outcome that could be transformative for image-guided therapy.
Short-wave infrared imaging in tissue in the 1000-2000 nm range is characterized by reduced photon scatter and comparable or higher absorption compared to the NIR-I regime. These characteristics have implications for the performance of fluorescence molecular tomography (FMT) techniques, potentially improving the resolution of subsurface structure, possibly at the expense of depth sensitivity. To examine these questions, we have developed a SWIR small animal fluorescence tomography system. This instrument acquires multi-angle SWIR projection images of a stationary platform through a rotating gantry technique. These images are then processed for tomographic reconstruction of the SWIR fluorescence activity. Herein, we describe the development of this system and show multi-angle images from a mouse carcass containing a SWIR-specific fluorophore inclusion.
Magnetic resonance imaging (MRI) of gadolinium (Gd)-based contrast agents plays a central role in managing the treatment of intracranial tumors. These images are involved in diagnosis, surgical planning, surgical navigation, and postoperative assessment of extent of resection. Replicating the information from Gd-MRI in the visual surgical field using fluorescent agents that behave similar to gadolinium in vivo would represent a major advance for surgical intervention of these tumors, and could provide robust compensation information to update pre-operative MRI images during surgery. In this paper, we examine the uptake of a Gd-based contrast agent in orthotopic tumor models and compare this behavior to two fluorescein-based contrast agents; specifically, clinical-grade sodium fluorescein (NaFl) and a 900 Da pegylated form of fluorescein. We show that the pegylated form of fluorescein is a more promising Gd-analog candidate.
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