Spinal cord injury (SCI) is a life-shattering neurological condition that affects between 250,000 and 500,000 individuals each year with an estimated two to three million people worldwide living with an SCI-related disability. The incidence in the USA and Canada is more than that in other countries with motor vehicle accidents being the most common cause, while violence being most common in the developing nations. Its incidence is two- to fivefold higher in males, with a peak in younger adults. Apart from the economic burden associated with medical care costs, SCI predominantly affects a younger adult population. Therefore, the psychological impact of adaptation of an average healthy individual as a paraplegic or quadriplegic with bladder, bowel, or sexual dysfunction in their early life can be devastating. People with SCI are two to five times more likely to die prematurely, with worse survival rates in low- and middle-income countries. This devastating disorder has a complex and multifaceted mechanism. Recently, a lot of research has been published on the restoration of locomotor activity and the therapeutic strategies. Therefore, it is imperative for the treating physicians to understand the complex underlying pathophysiological mechanisms of SCI.

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Structured-illumination microscopy delivers confocal-imaging capabilities and may be used for optical sectioning in bio-imaging applications. However, previous structured-illumination implementations are not capable of imaging molecular changes within highly scattering, biological samples in reflectance mode. Here, we present two advances which enable successful structured illumination reflectance microscopy to image molecular changes in epithelial tissue phantoms. First, we present the sine approximation algorithm to improve the ability to reconstruct the in-focus plane when the out-of-focus light is much greater in magnitude. We characterize the dependencies of this algorithm on phase step error, random noise and backscattered out-of-focus contributions. Second, we utilize a molecular-specific reflectance contrast agent based on gold nanoparticles to label disease-related biomarkers and increase the signal and signal-to-noise ratio (SNR) in structured illumination microscopy of biological tissue. Imaging results for multi-layer epithelial cell phantoms with optical properties characteristic of normal and cancerous tissue labeled with nanoparticles targeted against the epidermal growth factor receptor (EGFR) are presented. Structured illumination images reconstructed with the sine approximation algorithm compare favorably to those obtained with a standard confocal microscope; this new technique can be implemented in simple and small imaging platforms for future clinical studies.

Abstract. Oral cancer is an important global health problem. There is an urgent need for improved methods to detect oral cancer and its precursors, because early detection is the best way to reduce oral cancer mortality and morbidity. In this work, we describe simple modifications to a surgical headlight system that enables direct visualization and digital image acquisition from oral tissue in multiple imaging modalities including fluorescence, white-light reflectance, and orthogonal polarization reflectance. Images obtained with the system in-vivo demonstrate that it is an attractive technology to explore for oral cancer screening in low-resource environments where clinical expertise is often unavailable.

BackgroundThere is an important global need to improve early detection of oral cancer. Recent reports suggest that optical imaging technologies can aid in the identification of neoplastic lesions in the oral cavity; however, there is little data evaluating the use of optical imaging modalities in resource limited settings where oral cancer impacts patients disproportionately. In this article, we evaluate a simple, low-cost optical imaging system that is designed for early detection of oral cancer in resource limited settings. We report results of a clinical study conducted at Tata Memorial Hospital (TMH) in Mumbai, India using this system as a tool to improve detection of oral cancer and its precursors.MethodsReflectance images with white light illumination and fluorescence images with 455 nm excitation were obtained from 261 sites in the oral cavity from 76 patients and 90 sites in the oral cavity from 33 normal volunteers. Quantitative image features were used to develop classification algorithms to identify neoplastic tissue, using clinical diagnosis of expert observers as the gold standard.ResultsUsing the ratio of red to green autofluorescence, the algorithm identified tissues judged clinically to be cancer or clinically suspicious for neoplasia with a sensitivity of 90% and a specificity of 87%.ConclusionsResults suggest that the performance of this simple, objective low-cost system has potential to improve oral screening efforts, especially in low-resource settings.