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Three-dimensional (3D) refractive index (RI) tomography has recently become an exciting new tool for biological studies. However, its limitation to (1) thin samples resulting from a need of transmissive illumination and (2) small fields of view (typically $\sim <\#comment/>50\text{µ<\#comment/>}\mathrm{m}\times <\#comment/>50\text{µ<\#comment/>}\mathrm{m}$) has hindered its utility in broader biomedical applications. In this work, we demonstrate 3D RI tomography with a large field of view in opaque, arbitrarily thick scattering samples (unsuitable for imaging with conventional transmissive tomographic techniques) with a penetration depth of ca. one mean free scattering path length ( $\sim <\#comment/>100\text{µ<\#comment/>}\mathrm{m}$in tissue) using a simple, low-cost microscope system with epi-illumination. This approach leverages a solution to the inverse scattering problem via the general non-paraxial 3D optical transfer function of our quantitative oblique back-illumination microscopy (qOBM) optical system. A theoretical analysis is presented along with simulations and experimental validations using polystyrene beads, and rat and human thick brain tissues. This work has significant implications for the investigation of optically thick, semi-infinite samples in a non-invasive and label-free manner. This unique 3D qOBM approach can extend the utility of 3D RI tomography for translational and clinical medicine. 

Brain tumor surgery involves a delicate balance between maximizing the extent of tumor resection while minimizing damage to healthy brain tissue that is vital for neurological function. However, differentiating between tumor, particularly infiltrative disease, and healthy brain in-vivo remains a significant clinical challenge. Here we demonstrate that quantitative oblique back illumination microscopy (qOBM)—a novel label-free optical imaging technique that achieves tomographic quantitative phase imaging in thick scattering samples—clearly differentiates between healthy brain tissue and tumor, including infiltrative disease. Data from a bulk and infiltrative brain tumor animal model show that qOBM enables quantitative phase imaging of thick fresh brain tissues with remarkable cellular and subcellular detail that closely resembles histopathology using hematoxylin and eosin (H&E) stained fixed tissue sections, the gold standard for cancer detection. Quantitative biophysical features are also extracted from qOBM which yield robust surrogate biomarkers of disease that enable (1) automated tumor and margin detection with high sensitivity and specificity and (2) facile visualization of tumor regions. Finally, we develop a low-cost, flexible, fiber-based handheld qOBM device which brings this technology one step closer to in-vivo clinical use. This work has significant implications for guiding neurosurgery by paving the way for a tool that delivers real-time, label-free, in-vivo brain tumor margin detection. 

BACKGROUNDUmbilical cord blood has become an important source of hematopoietic stem and progenitor cells for therapeutic applications. However, cord blood banking (CBB) grapples with issues related to economic viability, partially due to high discard rates of cord blood units (CBUs) that lack sufficient total nucleated cells for storage or therapeutic use. Currently, there are no methods available to assess the likelihood of CBUs meeting storage criteria noninvasively at the collection site, which would improve CBB efficiency and economic viability. MATERIALS AND METHODSTo overcome this limitation, we apply a novel label‐free optical imaging method, called quantitative oblique back‐illumination microscopy (qOBM), which yields tomographic phase and absorption contrast to image blood inside collection bags. An automated segmentation algorithm was developed to count white blood cells and red blood cells (RBCs) and assess hematocrit. Fifteen CBUs were measured. RESULTSqOBM clearly differentiates between RBCs and nucleated cells. The cell‐counting analysis shows an average error of 13% compared to hematology analysis, with a near‐perfect, one‐to‐one relationship (slope = 0.94) and strong correlation coefficient (r = 0.86). Preliminary results to assess hematocrit also show excellent agreement with expected values. Acquisition times to image a statistically significant number of cells per CBU were approximately 1 minute. CONCLUSIONqOBM exhibits robust performance for quantifying blood inside collection bags. Because the approach is automated and fast, it can potentially quantify CBUs within minutes of collection, without breaching the CBUs' sterile environment. qOBM can reduce costs in CBB by avoiding processing expenses of CBUs that ultimately do not meet storage criteria.