Ergonomics of the Operative Field in Paediatric Minimal Access Surgery

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Coherent narrow-band light source for miniature endoscopes.

In this work, we report the successful implementation of a coherent narrow-band light source for miniature endoscopy applications. An RGB laser module that provides much higher luminosity than traditional incoherent white light sources is used for illumination, taking advantages of the laser light's high spatial coherence for efficient light coupling. Notably, the narrow spectral band of the laser light sources also enables spectrally resolved imaging, to distinguish certain biological tissues or components. A monochrome CMOS camera is employed to synchronize with the time lapsed RGB laser module illumination for color image acquisition and reconstruction, which provides better spatial resolution than a color CMOS camera of comparable pixel number, in addition to spectral resolving

Developing fibre optic Raman probes for applications in clinical spectroscopy

Raman spectroscopy has been shown by various groups over the last two decades to have significant capability in discriminating disease states in bodily fluids, cells and tissues. Recent development in instrumentation, optics and manufacturing approaches has facilitated the design and demonstration of various novel in vivo probes, which have applicability for myriad of applications. This review focusses on key considerations and recommendations for application specific clinical Raman probe design and construction. Raman probes can be utilised as clinical tools able to provide rapid, non-invasive, real-time molecular analysis of disease specific changes in tissues. Clearly the target tissue location, the significance of spectral changes with disease and the possible access routes to the region of interest will vary for each clinical application considered. This review provides insight into design and construction considerations, including suitable probe designs and manufacturing materials compatible with Raman spectroscopy

Development of wide-field fluorescence lifetime imaging for biomedical applications

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Raman imaging through a single multimode fibre

UK Engineering and Physical Sciences Research Council (EPSRC) (EP/J01771/X); European Union project FAMOS (FP7 ICT no. 317744); PreDiCT-TB consortium (IMI 115337); European Union’s Horizon 2020 Marie Sklodowska-Curie Actions (MSCA) (707084).Vibrational spectroscopy is a widespread, powerful method of recording the spectra of constituent molecules within a sample in a label-free manner. As an example, Raman spectroscopy has major applications in materials science, biomedical analysis and clinical studies. The need to access deep tissues and organs in vivo has triggered major advances in fibre Raman probes that are compatible with endoscopic settings. However, imaging in confined geometries still remains out of reach for the current state of art fibre Raman systems without compromising the compactness and flexibility. Here we demonstrate Raman spectroscopic imaging via complex correction in single multimode fibre without using any additional optics and filters in the probe design. Our approach retains the information content typical to traditional fibre bundle imaging, yet within an ultra-thin footprint of diameter 125 µm which is the thinnest Raman imaging probe realised to date. We are able to acquire Raman images, including for bacteria samples, with fields of view exceeding 200 µm in diameter.Publisher PDFPeer reviewe

Computational Tools for Image Processing, Integration, and Visualization of Simultaneous OCT-FLIM Images of Tissue

Multimodal imaging systems have emerged as robust methods for the characterization of atherosclerotic plaques and early diagnosis of oral cancer. Multispectral wide-field Fluorescence Lifetime Imaging Microscopy (FLIM) has been shown to be a capable optical imaging modality for biomedical diagnosis oral cancer. A fiber-based endoscope combined with an intensified charge-coupled device (ICCD) allows to collect and split the fluorescence emission into multiple bands, from which the fluorescence lifetime decay in each spectral channel can be calculated separately. However, for accurate calculations, it is necessary to gather multiple gates increasing the imaging time. Since this time is critical for real-time in vivo applications. This study presents a novel approach to using Rapid Lifetime Determination (RLD) methods to considerably shorten this time period. Moreover, the use of a dual-modality system, incorporating Optical Coherence Tomography (OCT) and FLIM, which simultaneously characterizes 3-D tissue morphology and biochemical composition of tissue, leads to the development of robust computational tools for image processing, integration, and visualization of these imaging techniques. OCTFLIM systems provide 3D structural and 2D biochemical tissue information, which the software tools developed in this work properly integrate to assist the image processing, characterization, and visualization of OCT-FLIM images of atherosclerotic plaques. Additionally, plaque characterization is performed by visual assessment and requires a trained expert for interpretation of the large data sets. Here, we present two novel computational methods for automated intravascular (IV) OCT plaque characterization. The first method is based on the modeling of each A-line of an IV-OCT data set as a linear combination of a number of depth profiles. After estimating these depth profiles by means of an alternating least square optimization strategy, they are automatically classified to predefined tissue types based on their morphological characteristics. The second method is intended to automatically identify macrophage/foam cell clusters in atherosclerotic plaques. Vulnerable plaques are characterized by presenting a necrotic core below a thin fibrous cap, and extensive infiltration of macrophages/foam cells. Thus, the degree of macrophage accumulation is an indicator in determining plaque progression and probability of rupture. In this work, two texture features are introduced, the normalized standard deviation ratio (NSDRatio) and the entropy ratio (ENTRatio), to effectively classify areas in the plaque with macrophage/foam cell infiltration. Since this methodology has low complexity and computational cost, it could be implemented for in vivo real time identification of macrophage/foam cell presence

Computational Tools for Image Processing, Integration, and Visualization of Simultaneous OCT-FLIM Images of Tissue

Multimodal imaging systems have emerged as robust methods for the characterization of atherosclerotic plaques and early diagnosis of oral cancer. Multispectral wide-field Fluorescence Lifetime Imaging Microscopy (FLIM) has been shown to be a capable optical imaging modality for biomedical diagnosis oral cancer. A fiber-based endoscope combined with an intensified charge-coupled device (ICCD) allows to collect and split the fluorescence emission into multiple bands, from which the fluorescence lifetime decay in each spectral channel can be calculated separately. However, for accurate calculations, it is necessary to gather multiple gates increasing the imaging time. Since this time is critical for real-time in vivo applications. This study presents a novel approach to using Rapid Lifetime Determination (RLD) methods to considerably shorten this time period. Moreover, the use of a dual-modality system, incorporating Optical Coherence Tomography (OCT) and FLIM, which simultaneously characterizes 3-D tissue morphology and biochemical composition of tissue, leads to the development of robust computational tools for image processing, integration, and visualization of these imaging techniques. OCTFLIM systems provide 3D structural and 2D biochemical tissue information, which the software tools developed in this work properly integrate to assist the image processing, characterization, and visualization of OCT-FLIM images of atherosclerotic plaques. Additionally, plaque characterization is performed by visual assessment and requires a trained expert for interpretation of the large data sets. Here, we present two novel computational methods for automated intravascular (IV) OCT plaque characterization. The first method is based on the modeling of each A-line of an IV-OCT data set as a linear combination of a number of depth profiles. After estimating these depth profiles by means of an alternating least square optimization strategy, they are automatically classified to predefined tissue types based on their morphological characteristics. The second method is intended to automatically identify macrophage/foam cell clusters in atherosclerotic plaques. Vulnerable plaques are characterized by presenting a necrotic core below a thin fibrous cap, and extensive infiltration of macrophages/foam cells. Thus, the degree of macrophage accumulation is an indicator in determining plaque progression and probability of rupture. In this work, two texture features are introduced, the normalized standard deviation ratio (NSDRatio) and the entropy ratio (ENTRatio), to effectively classify areas in the plaque with macrophage/foam cell infiltration. Since this methodology has low complexity and computational cost, it could be implemented for in vivo real time identification of macrophage/foam cell presence