In vivo endoscopic autofluorescence microspectro-imaging of bronchi and alveoli

Fibered confocal fluorescence microscopy (FCFM) is a new technique that can be used during a bronchoscopy to analyze the nature of the human bronchial and alveolar mucosa fluorescence microstructure. An endoscopic fibered confocal fluorescence microscopy system with spectroscopic analysis capability was developed allowing real-time, simultaneous images and emission spectra acquisition at 488 nm excitation using a flexible miniprobe that could be introduced into small airways. This flexible 1.4 mm miniprobe can be introduced into the working channel of a flexible endoscope and gently advanced through the bronchial tree to the alveoli. FCFM in conjunction with bronchoscopy is able to image the in vivo autofluorescence structure of the bronchial mucosae but also the alveolar respiratory network outside of the usual field of view. Microscopic and spectral analysis showed that the signal mainly originates from the elastin component of the bronchial subepithelial layer. In non smokers, the system images the elastin backbone of the aveoli. In active smokers, a strong autofluorescence signal appears from alveolar macrophages. The FCFM technique appears promising for in vivo exploration of the bronchial and alveolar extracellular matrix

Novel Approaches for Detection Fluorescent-Labeled by Cellvizio Lab System on Hippocampal CA1 Region

Neurosteroids have been identified in the 1981. Dehydroepiandrosterone sulphate (DHEAS) is one of the vital neurosteroids that de novo synthesized in the nervous system from cholesterol precursor (Baulieu & Robel, 1998). The aim of the study is to develop a method for fluorescence labelling. Alexa Fluor 488 dye with DHEAS antibody can binds the DHEAS antibody in the rat brain monitored by Cellvizio Lab System. DHEAS antibody (IgG isotype antibodies) was fluorescently conjugated by an amine-reactive compound, Alexa Fluor 5-SDP ester 488 dye. The resultant Alexa Fluor 488-conjugated antibodies were collected and analyzed by UV-Vis spectrophotometer instrument. The absorbance of the protein-dye conjugate at 280 nm and 494 nm were measured. Then, the degree of labeling (DOL) was calculated to achieve the desired results. Fluorescence labelling were carried out into the CA1 region of hippocampus Sprague-Dawley rat. We reported that the conjugation was successful. Optimal labeling depending on degree of labeling (DOL) needs some necessity to achieve and effective binding to the target neurosteroid, DHEAS. Cellvizio Lab system connected with Fiber Fluorescence Microscopy (FFM) probe is presented as a new approach in real-time imaging of DHEAS. In conclusion, we have developed a new method of DHEAS-Alexa Fluor fluorescence labelling to visualize and evaluate the changes of DHEAS fluorescence level in the rat hippocampus. This novel approach as a diagnostic tool and can be used to better understand the mechanisms and functions of DHEAS and other neurosteroids in future research

Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction

PURPOSE: Probe-based confocal laser endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assembled into a single fibre bundle. Video registration techniques can be used to estimate high-resolution (HR) images by exploiting the temporal information contained in a sequence of low-resolution (LR) images. However, the alignment of LR frames, required for the fusion, is computationally demanding and prone to artefacts. METHODS: In this work, we propose a novel synthetic data generation approach to train exemplar-based Deep Neural Networks (DNNs). HR pCLE images with enhanced quality are recovered by the models trained on pairs of estimated HR images (generated by the video registration algorithm) and realistic synthetic LR images. Performance of three different state-of-the-art DNNs techniques were analysed on a Smart Atlas database of 8806 images from 238 pCLE video sequences. The results were validated through an extensive image quality assessment that takes into account different quality scores, including a Mean Opinion Score (MOS). RESULTS: Results indicate that the proposed solution produces an effective improvement in the quality of the obtained reconstructed image. CONCLUSION: The proposed training strategy and associated DNNs allows us to perform convincing super-resolution of pCLE images

Effective deep learning training for single-image super-resolution in endomicroscopy exploiting video-registration-based reconstruction

Purpose: Probe-based Confocal Laser Endomicroscopy (pCLE) is a recent imaging modality that allows performing in vivo optical biopsies. The design of pCLE hardware, and its reliance on an optical fibre bundle, fundamentally limits the image quality with a few tens of thousands fibres, each acting as the equivalent of a single-pixel detector, assembled into a single fibre bundle. Video-registration techniques can be used to estimate high-resolution (HR) images by exploiting the temporal information contained in a sequence of low-resolution (LR) images. However, the alignment of LR frames, required for the fusion, is computationally demanding and prone to artefacts. Methods: In this work, we propose a novel synthetic data generation approach to train exemplar-based Deep Neural Networks (DNNs). HR pCLE images with enhanced quality are recovered by the models trained on pairs of estimated HR images (generated by the video-registration algorithm) and realistic synthetic LR images. Performance of three different state-of-the-art DNNs techniques were analysed on a Smart Atlas database of 8806 images from 238 pCLE video sequences. The results were validated through an extensive Image Quality Assessment (IQA) that takes into account different quality scores, including a Mean Opinion Score (MOS). Results: Results indicate that the proposed solution produces an effective improvement in the quality of the obtained reconstructed image. Conclusion: The proposed training strategy and associated DNNs allows us to perform convincing super-resolution of pCLE images

Motion-Aware Mosaicing for Confocal Laser Endomicroscopy

International audienceProbe-based Confocal Laser Endomicroscopy (pCLE) provides physicians with real-time access to histological information during standard endoscopy procedures, through high-resolution cellular imaging of internal tissues. Earlier work on mosaicing has enhanced the potential of this imaging modality by meeting the need to get a complete representation of the imaged region. However, with approaches, the dynamic information, which may be of clinical interest, is lost. In this study, we propose a new mosaic construction algorithm for pCLE sequences based on a min-cut optimization and gradient-domain composition. Its main advantage is that the motion of some structures within the tissue such as blood cells in capillaries, is taken into account. This allows physicians to get both a sharper static representation and a dynamic representation of the imaged tissue. Results on 16 sequences acquired in vivo on six different organs demonstrate the clinical relevance of our approach

Line-scanning fiber bundle endomicroscopy with a virtual detector slit

Coherent fiber bundles can be used to relay the image plane from the distal tip of an endomicroscope to an external confocal microscopy system. The frame rate is therefore determined by the speed of the microscope’s laser scanning system which, at 10-20 Hz, may be undesirably low for in vivo clinical applications. Line-scanning allows an increase in the frame rate by an order of magnitude in exchange for some loss of optical sectioning, but the width of the detector slit cannot easily be adapted to suit different imaging conditions. The rolling shutter of a CMOS camera can be used as a virtual detector slit for a bench-top line-scanning confocal microscope, and here we extend this idea to endomicroscopy. By synchronizing the camera rolling shutter with a scanning laser line we achieve confocal imaging with an electronically variable detector slit. This architecture allows us to acquire every other frame with the detector slit offset by a known distance, and we show that subtracting this second image leads to improved optical sectioning