Cellulose Nanoparticles are a Biodegradable Photoacoustic Contrast Agent for Use in Living Mice.

Molecular imaging with photoacoustic ultrasound is an emerging field that combines the spatial and temporal resolution of ultrasound with the contrast of optical imaging. However, there are few imaging agents that offer both high signal intensity and biodegradation into small molecules. Here we describe a cellulose-based nanoparticle with peak photoacoustic signal at 700 nm and an in vitro limit of detection of 6 pM (0.02 mg/mL). Doses down to 0.35 nM (1.2 mg/mL) were used to image mouse models of ovarian cancer. Most importantly, the nanoparticles were shown to biodegrade in the presence of cellulase both through a glucose assay and electron microscopy

A background correction method to compensate illumination variation in hyperspectral imaging.

Hyperspectral imaging (HSI) can measure both spatial (morphological) and spectral (biochemical) information from biological tissues. While HSI appears promising for biomedical applications, interpretation of hyperspectral images can be challenging when data is acquired in complex biological environments. Variations in surface topology or optical power distribution at the sample, encountered for example during endoscopy, can lead to errors in post-processing of the HSI data, compromising disease diagnostic capabilities. Here, we propose a background correction method to compensate for such variations, which estimates the optical properties of illumination at the target based on the normalised spectral profile of the light source and the measured HSI intensity values at a fixed wavelength where the absorption characteristics of the sample are relatively low (in this case, 800 nm). We demonstrate the feasibility of the proposed method by imaging blood samples, tissue-mimicking phantoms, and ex vivo chicken tissue. Moreover, using synthetic HSI data composed from experimentally measured spectra, we show the proposed method would improve statistical analysis of HSI data. The proposed method could help the implementation of HSI techniques in practical clinical applications, where controlling the illumination pattern and power is difficult

Contrast agents for molecular photoacoustic imaging.

Photoacoustic imaging (PAI) is an emerging tool that bridges the traditional depth limits of ballistic optical imaging and the resolution limits of diffuse optical imaging. Using the acoustic waves generated in response to the absorption of pulsed laser light, it provides noninvasive images of absorbed optical energy density at depths of several centimeters with a resolution of ∼100 μm. This versatile and scalable imaging modality has now shown potential for molecular imaging, which enables visualization of biological processes with systemically introduced contrast agents. Understanding the relative merits of the vast range of contrast agents available, from small-molecule dyes to gold and carbon nanostructures to liposome encapsulations, is a considerable challenge. Here we critically review the physical, chemical and biochemical characteristics of the existing photoacoustic contrast agents, highlighting key applications and present challenges for molecular PAI.This work was supported by CRUK (Career Establishment Award no. C47594/A16267 to J.W. and S.E.B., Core Funding C14303/A17197 to J.W. and S.E.B.), the European Commission (CIG FP7-PEOPLE- 2013-CIG-630729 to J.W. and S.E.B.), the EPSRC-CRUK Cancer Imaging Centre in Cambridge and Manchester (C197/A16465 to J.W. and S.E.B.), King’s College London and University College London Comprehensive Cancer Imaging Centre Cancer Research UK & Engineering and Physical Sciences Research Council, in association with the Medical Research Council and the Department of Health, UK (P.B.), and the European Union (project FAMOS FP7 ICT, contract 317744 to P.B.).This is the author accepted manuscript. The final version is available from Nature Publishing Group via http://dx.doi.org/10.1038/nmeth.392

Hyperspectral imaging in biomedical applications

No abstract available

Single-pixel phase-corrected fiber bundle endomicroscopy with lensless focussing capability.

In this paper a novel single-pixel method for coherent imaging through an endoscopic fiber bundle is presented. The use of a single-pixel detector allows greater sensitivity over a wider range of wavelengths, which could have significant applications in endoscopic fluorescence microscopy. First, the principle of lensless focussing at the distal end of a coherent fiber bundle is simulated to examine the impact of pixelation at microscopic scales. Next, an experimental optical correlator system using spatial light modulators (SLMs) is presented. A simple contrast imaging method of characterizing and compensating phase aberrations introduced by fiber bundles is described. Experimental results are then presented showing that our phase compensation method enables characterization of the optical phase profile of individual fiberlets. After applying this correction, early results demonstrating the ability of the system to electronically adjust the focal plane at the distal end of the fiber bundle are presented. The structural similarity index (SSIM) between the simulated image and the experimental focus-adjusted image increases noticeably when the phase correction is applied and the retrieved image is visually recognizable. Strategies to improve image quality are discussed.G. Gordon would like to acknowledge support from a Henslow Research Fellowship from the Cambridge Philosophical Society, as well as research funding from the Cambridge Cancer Centre and Cancer Research UK. S. Bohndiek would like to acknowledge research funding from a Cancer Research UK Career Establishment Award and the CRUK-EPSRC Cancer Imaging Centre in Cambridge and Manchester.This is the final version of the article. It first appeared from IEEE via http://dx.doi.org/10.1109/JLT.2015.243681

Deep learning applied to hyperspectral endoscopy for online spectral classification

Abstract: Hyperspectral imaging (HSI) is being explored in endoscopy as a tool to extract biochemical information that may improve contrast for early cancer detection in the gastrointestinal tract. Motion artefacts during medical endoscopy have traditionally limited HSI application, however, recent developments in the field have led to real-time HSI deployments. Unfortunately, traditional HSI analysis methods remain unable to rapidly process the volume of hyperspectral data in order to provide real-time feedback to the operator. Here, a convolutional neural network (CNN) is proposed to enable online classification of data obtained during HSI endoscopy. A five-layered CNN was trained and fine-tuned on a dataset of 300 hyperspectral endoscopy images acquired from a planar Macbeth ColorChecker chart and was able to distinguish between its 18 constituent colors with an average accuracy of 94.3% achieved at 8.8 fps. Performance was then tested on a set of images simulating an endoscopy environment, consisting of color charts warped inside a rigid tube mimicking a lumen. The algorithm proved robust to such variations, with classification accuracies over 90% being obtained despite the variations, with an average drop in accuracy of 2.4% being registered at the points of longest working distance and most inclination. For further validation of the color-based classification system, ex vivo videos of a methylene blue dyed pig esophagus and images of different disease stages in the human esophagus were analyzed, showing spatially distinct color classifications. These results suggest that the CNN has potential to provide color-based classification during real-time HSI in endoscopy

Deep learning applied to hyperspectral endoscopy for online spectral classification

Abstract: Hyperspectral imaging (HSI) is being explored in endoscopy as a tool to extract biochemical information that may improve contrast for early cancer detection in the gastrointestinal tract. Motion artefacts during medical endoscopy have traditionally limited HSI application, however, recent developments in the field have led to real-time HSI deployments. Unfortunately, traditional HSI analysis methods remain unable to rapidly process the volume of hyperspectral data in order to provide real-time feedback to the operator. Here, a convolutional neural network (CNN) is proposed to enable online classification of data obtained during HSI endoscopy. A five-layered CNN was trained and fine-tuned on a dataset of 300 hyperspectral endoscopy images acquired from a planar Macbeth ColorChecker chart and was able to distinguish between its 18 constituent colors with an average accuracy of 94.3% achieved at 8.8 fps. Performance was then tested on a set of images simulating an endoscopy environment, consisting of color charts warped inside a rigid tube mimicking a lumen. The algorithm proved robust to such variations, with classification accuracies over 90% being obtained despite the variations, with an average drop in accuracy of 2.4% being registered at the points of longest working distance and most inclination. For further validation of the color-based classification system, ex vivo videos of a methylene blue dyed pig esophagus and images of different disease stages in the human esophagus were analyzed, showing spatially distinct color classifications. These results suggest that the CNN has potential to provide color-based classification during real-time HSI in endoscopy

Grayscale-to-Color: Scalable Fabrication of Custom Multispectral Filter Arrays.

Snapshot multispectral image (MSI) sensors have been proposed as a key enabler for a plethora of multispectral imaging applications, from diagnostic medical imaging to remote sensing. With each application requiring a different set, and number, of spectral bands, the absence of a scalable, cost-effective manufacturing solution for custom multispectral filter arrays (MSFAs) has prevented widespread MSI adoption. Despite recent nanophotonic-based efforts, such as plasmonic or high-index metasurface arrays, large-area MSFA manufacturing still consists of many-layer dielectric (Fabry-Perot) stacks, requiring separate complex lithography steps for each spectral band and multiple material compositions for each. It is an expensive, cumbersome, and inflexible undertaking, but yields optimal optical performance. Here, we demonstrate a manufacturing process that enables cost-effective wafer-level fabrication of custom MSFAs in a single lithographic step, maintaining high efficiencies (∼75%) and narrow line widths (∼25 nm) across the visible to near-infrared. By merging grayscale (analog) lithography with metal-insulator-metal (MIM) Fabry-Perot cavities, whereby exposure dose controls cavity thickness, we demonstrate simplified fabrication of MSFAs up to N-wavelength bands. The concept is first proven using low-volume electron beam lithography, followed by the demonstration of large-volume UV mask-based photolithography with MSFAs produced at the wafer level. Our framework provides an attractive alternative to conventional MSFA manufacture and metasurface-based spectral filters by reducing both fabrication complexity and cost of these intricate optical devices, while increasing customizability

Raman micro-spectroscopy for accurate identification of primary human bronchial epithelial cells.

Live cell Raman micro-spectroscopy is emerging as a promising bioanalytical technique for label-free discrimination of a range of different cell types (e.g. cancer cells and fibroblasts) and behaviors (e.g. apoptosis). The aim of this study was to determine whether confocal Raman micro-spectroscopy shows sufficient sensitivity and specificity for identification of primary human bronchial epithelial cells (HBECs) to be used for live cell biological studies in vitro. We first compared cell preparation substrates and media, considering their influence on lung cell proliferation and Raman spectra, as well as methods for data acquisition, using different wavelengths (488 nm, 785 nm) and scan protocols (line, area). Evaluating these parameters using human lung cancer (A549) and fibroblast (MRC5) cell lines confirmed that line-scan data acquisition at 785 nm using complete cell media on a quartz substrate gave optimal performance. We then applied our protocol to acquisition of data from primary human bronchial epithelial cells (HBEC) derived from three independent sources, revealing an average sensitivity for different cell types of 96.3% and specificity of 95.2%. These results suggest that Raman micro-spectroscopy is suitable for delineating primary HBEC cell cultures, which in future could be used for identifying different lung cell types within co-cultures and studying the process of early carcinogenesis in lung cell culture