The development of accurate and clinically applicable tools to assess cancer risk is essential to define candidates to undergo screening for early-stage cancers at a curable stage or provide a novel method to monitor chemoprevention treatments. With the use of our recently developed optical technology—spatial-domain low-coherence quantitative phase microscopy (SL-QPM), we have derived a novel optical biomarker characterized by structure-derived optical path length (OPL) properties from the cell nucleus on the standard histology and cytology specimens, which quantifies the nano-structural alterations within the cell nucleus at the nanoscale sensitivity, referred to as nano-morphology marker. The aim of this study is to evaluate the feasibility of the nuclear nano-morphology marker from histologically normal cells, extracted directly from the standard histology specimens, to detect early-stage carcinogenesis, assess cancer risk, and monitor the effect of chemopreventive treatment. We used a well-established mouse model of spontaneous carcinogenesis—ApcMin mice, which develop multiple intestinal adenomas (Min) due to a germline mutation in the adenomatous polyposis coli (Apc) gene. We found that the nuclear nano-morphology marker quantified by OPL detects the development of carcinogenesis from histologically normal intestinal epithelial cells, even at an early pre-adenomatous stage (six weeks). It also exhibits a good temporal correlation with the small intestine that parallels the development of carcinogenesis and cancer risk. To further assess its ability to monitor the efficacy of chemopreventive agents, we used an established chemopreventive agent, sulindac. The nuclear nano-morphology marker is reversed toward normal after a prolonged treatment. Therefore, our proof-of-concept study establishes the feasibility of the SL-QPM derived nuclear nano-morphology marker OPL as a promising, simple and clinically applicable biomarker for cancer risk assessment and evaluation of chemopreventive treatment.

We demonstrate a novel approach for the real time visualization and quantification of the 3D spatial frequencies in an image domain. Our approach is based on the spectral encoding of spatial frequency principle and permits the formation of an image as a color map in which spatially separated spectral wavelengths correspond to the dominant 3D spatial frequencies of the object. We demonstrate that our approach can visualize and analyze the dominant axial internal structure for each image point in real time and with nanoscale sensitivity to structural changes. Computer modeling and experimental results of instantaneous color visualization and quantification of 3D structures of a model system and biological samples are presented.

For any technique to be adopted into a clinical setting, it is imperative that it seamlessly integrates with well-established clinical diagnostic workflow. We recently developed an optical microscopy technique-spatial-domain low-coherence quantitative phase microscopy (SL-QPM) that can extract the refractive index of the cell nucleus from the standard histology specimens on glass slides prepared via standard clinical protocols. This technique has shown great potential in detecting cancer with a better sensitivity than conventional pathology. A major hurdle in the clinical translation of this technique is the intrinsic variation among staining agents used in histology specimens, which limits the accuracy of refractive index measurements of clinical samples. In this paper, we present a simple and easily generalizable method to remove the effect of variations in staining levels on nuclear refractive index obtained with SL-QPM. We illustrate the efficacy of our correction method by applying it to variously stained histology samples from animal model and clinical specimens.

Difference images quantify changes in the object scene over time. In this paper, we use the feature-specific imaging paradigm to present methods for estimating a sequence of difference images from a sequence of compressive measurements of the object scene. Our goal is twofold. First is to design, where possible, the optimal sensing matrix for taking compressive measurements. In scenarios where such sensing matrices are not tractable, we consider plausible candidate sensing matrices that either use the available a priori information or are nonadaptive. Second, we develop closed-form and iterative techniques for estimating the difference images. We specifically look at l 2 - and l 1 -based methods. We show that l 2 -based techniques can directly estimate the difference image from the measurements without first reconstructing the object scene. This direct estimation exploits the spatial and temporal correlations between the object scene at two consecutive time instants. We further develop a method to estimate a generalized difference image from multiple measurements and use it to estimate the sequence of difference images. For l 1 -based estimation, we consider modified forms of the total-variation method and basis pursuit denoising. We also look at a third method that directly exploits the sparsity of the difference image. We present results to show the efficacy of these techniques and discuss the advantages of each

We introduce a new technique, spectral contrast imaging microscopy (SCIM), for super-resolution microscopic imaging. Based on a novel contrast mechanism that encodes each local spatial frequency with a corresponding optical wavelength, SCIM provides a real-time high-resolution spectral contrast microscopic image with superior contrast. We show that two microscopic objects, separated by a distance smaller than the diffraction limit of the optical system, can be spatially resolved in the SCIM image as different colors. Results with numerical simulation and experiments using a high-resolution United States Air Force target are presented. The ability of SCIM for imaging biological cells is also demonstrated.