Noninvasive virtual biopsy using micro-registered optical coherence tomography (OCT) in human subjects.

Histological hematoxylin and eosin-stained (H&E) tissue sections are used as the gold standard for pathologic detection of cancer, tumor margin detection, and disease diagnosis. Producing H&E sections, however, is invasive and time-consuming. While deep learning has shown promise in virtual staining of unstained tissue slides, true virtual biopsy requires staining of images taken from intact tissue. In this work, we developed a micron-accuracy coregistration method [micro-registered optical coherence tomography (OCT)] that can take a two-dimensional (2D) H&E slide and find the exact corresponding section in a 3D OCT image taken from the original fresh tissue. We trained a conditional generative adversarial network using the paired dataset and showed high-fidelity conversion of noninvasive OCT images to virtually stained H&E slices in both 2D and 3D. Applying these trained neural networks to in vivo OCT images should enable physicians to readily incorporate OCT imaging into their clinical practice, reducing the number of unnecessary biopsy procedures.

Self-Organizing Maps for Cellular In Silico Staining and Cell Substate Classification.

Cellular composition and structural organization of cells in the tissue determine effective antitumor response and can predict patient outcome and therapy response. Here we present Seg-SOM, a method for dimensionality reduction of cell morphology in H&E-stained tissue images. Seg-SOM resolves cellular tissue heterogeneity and reveals complex tissue architecture. We leverage a self-organizing map (SOM) artificial neural network to group cells based on morphological features like shape and size. Seg-SOM allows for cell segmentation, systematic classification, and in silico cell labeling. We apply the Seg-SOM to a dataset of breast cancer progression images and find that clustering of SOM classes reveals groups of cells corresponding to fibroblasts, epithelial cells, and lymphocytes. We show that labeling the Lymphocyte SOM class on the breast tissue images accurately estimates lymphocytic infiltration. We further demonstrate how to use Seq-SOM in combination with non-negative matrix factorization to statistically describe the interaction of cell subtypes and use the interaction information as highly interpretable features for a histological classifier. Our work provides a framework for use of SOM in human pathology to resolve cellular composition of complex human tissues. We provide a python implementation and an easy-to-use docker deployment, enabling researchers to effortlessly featurize digitalized H&E-stained tissue.

Angular compounding for speckle reduction in optical coherence tomography using geometric image registration algorithm and digital focusing.

Optical coherence tomography (OCT) suffers from speckle noise due to the high spatial coherence of the utilized light source, leading to significant reductions in image quality and diagnostic capabilities. In the past, angular compounding techniques have been applied to suppress speckle noise. However, existing image registration methods usually guarantee pure angular compounding only within a relatively small field of view in the focal region, but produce spatial averaging in the other regions, resulting in resolution loss and image blur. This work develops an image registration model to correctly localize the real-space location of every pixel in an OCT image, for all depths. The registered images captured at different angles are fused into a speckle-reduced composite image. Digital focusing, based on the convolution of the complex OCT images and the conjugate of the point spread function (PSF), is studied to further enhance lateral resolution and contrast. As demonstrated by experiments, angular compounding with our improved image registration techniques and digital focusing, can effectively suppress speckle noise, enhance resolution and contrast, and reveal fine structures in ex-vivo imaged tissue.

Gold Nanobipyramids as Second Near Infrared Optical Coherence Tomography Contrast Agents for in Vivo Multiplexing Studies.

Developing contrast-enhanced optical coherence tomography (OCT) techniques is important for specific imaging of tissue lesions, molecular imaging, cell-tracking, and highly sensitive microangiography and lymphangiography. Multiplexed OCT imaging in the second near-infrared (NIR-II) window is highly desirable since it allows simultaneous imaging and tracking of multiple biological events in high resolution with deeper tissue penetration in vivo. Here we demonstrate that gold nanobipyramids can function as OCT multiplexing contrast agents, allowing high-resolution imaging of two separate lymphatic flows occurring simultaneously from different drainage basins into the same lymph node in a live mouse. Contrast-enhanced multiplexed lymphangiography of a melanoma tumor in vivo shows that the peritumoral lymph flow upstream of the tumor is unidirectional, and tumor is accessible to such flow. Whereas the lymphatic drainage coming out from the tumor is multidirectional. We also demonstrate real-time tracking of the contrast agents draining from a melanoma tumor specifically to the sentinel lymph node of the tumor and the three-dimensional distribution of the contrast agents in the lymph node.

Femtosecond X-ray induced changes of the electronic and magnetic response of solids from electron redistribution.

Resonant X-ray absorption, where an X-ray photon excites a core electron into an unoccupied valence state, is an essential process in many standard X-ray spectroscopies. With increasing X-ray intensity, the X-ray absorption strength is expected to become nonlinear. Here, we report the onset of such a nonlinearity in the resonant X-ray absorption of magnetic Co/Pd multilayers near the Co L[Formula: see text] edge. The nonlinearity is directly observed through the change of the absorption spectrum, which is modified in less than 40 fs within 2 eV of its threshold. This is interpreted as a redistribution of valence electrons near the Fermi level. For our magnetic sample this also involves mixing of majority and minority spins, due to sample demagnetization. Our findings reveal that nonlinear X-ray responses of materials may already occur at relatively low intensities, where the macroscopic sample is not destroyed, providing insight into ultrafast charge and spin dynamics.

Speckle modulation enables high-resolution wide-field human brain tumor margin detection and in vivo murine neuroimaging.

Current in vivo neuroimaging techniques provide limited field of view or spatial resolution and often require exogenous contrast. These limitations prohibit detailed structural imaging across wide fields of view and hinder intraoperative tumor margin detection. Here we present a novel neuroimaging technique, speckle-modulating optical coherence tomography (SM-OCT), which allows us to image the brains of live mice and ex vivo human samples with unprecedented resolution and wide field of view using only endogenous contrast. The increased visibility provided by speckle elimination reveals white matter fascicles and cortical layer architecture in brains of live mice. To our knowledge, the data reported herein represents the highest resolution imaging of murine white matter structure achieved in vivo across a wide field of view of several millimeters. When applied to an orthotopic murine glioblastoma xenograft model, SM-OCT readily identifies brain tumor margins with resolution of approximately 10 µm. SM-OCT of ex vivo human temporal lobe tissue reveals fine structures including cortical layers and myelinated axons. Finally, when applied to an ex vivo sample of a low-grade glioma resection margin, SM-OCT is able to resolve the brain tumor margin. Based on these findings, SM-OCT represents a novel approach for intraoperative tumor margin detection and in vivo neuroimaging.

Gold Nanoprisms as Optical Coherence Tomography Contrast Agents in the Second Near-Infrared Window for Enhanced Angiography in Live Animals.

Optical coherence tomography angiography (OCTA) is an important tool for investigating vascular networks and microcirculation in living tissue. Traditional OCTA detects blood vessels via intravascular dynamic scattering signals derived from the movements of red blood cells (RBCs). However, the low hematocrit and long latency between RBCs in capillaries make these OCTA signals discontinuous, leading to incomplete mapping of the vascular networks. OCTA imaging of microvascular circulation is particularly challenging in tumors due to the abnormally slow blood flow in angiogenic tumor vessels and strong attenuation of light by tumor tissue. Here, we demonstrate in vivo that gold nanoprisms (GNPRs) can be used as OCT contrast agents working in the second near-infrared window, significantly enhancing the dynamic scattering signals in microvessels and improving the sensitivity of OCTA in skin tissue and melanoma tumors in live mice. With GNPRs as contrast agents, the postinjection OCT angiograms showed 41 and 59% more microvasculature than preinjection angiograms in healthy mouse skin and melanoma tumors, respectively. By enabling better characterization of microvascular circulation in vivo, GNPR-enhanced OCTA could lead to better understanding of vascular functions during pathological conditions, more accurate measurements of therapeutic response, and improved patient prognoses.

Femtosecond X-ray magnetic circular dichroism absorption spectroscopy at an X-ray free electron laser.

X-ray magnetic circular dichroism spectroscopy using an X-ray free electron laser is demonstrated with spectra over the Fe L(3,2)-edges. The high brightness of the X-ray free electron laser combined with high accuracy detection of incident and transmitted X-rays enables ultrafast X-ray magnetic circular dichroism studies of unprecedented sensitivity. This new capability is applied to a study of all-optical magnetic switching dynamics of Fe and Gd magnetic sublattices in a GdFeCo thin film above its magnetization compensation temperature.

Optimal vaccination in a stochastic epidemic model of two non-interacting populations.

Developing robust, quantitative methods to optimize resource allocations in response to epidemics has the potential to save lives and minimize health care costs. In this paper, we develop and apply a computationally efficient algorithm that enables us to calculate the complete probability distribution for the final epidemic size in a stochastic Susceptible-Infected-Recovered (SIR) model. Based on these results, we determine the optimal allocations of a limited quantity of vaccine between two non-interacting populations. We compare the stochastic solution to results obtained for the traditional, deterministic SIR model. For intermediate quantities of vaccine, the deterministic model is a poor estimate of the optimal strategy for the more realistic, stochastic case.