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(a) Scanned image of an USAF 1951 Resolution test chart. (b) The 1951 USAF resolution test chart imaged by the OEM system.
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Optical endomicroscopy (OEM) is an emerging technology platform with preclinical and clinical imaging applications. Pulmonary OEM via fibre bundles has the potential to provide in vivo, in situ molecular signatures of disease such as infection and inflammation. However, enhancing the quality of data acquired by this technique for better visualizati...
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Imaging technologies, such as coherent fibred bundle optical microscopy (FBOM), operate with irregularly-spaced sparse sub-samples from their field of view. In this paper, we address the problem of data deconvolution for applications where the observed irregularly distributed samples are considered as a result of a convolution operator acting on or...
Citations
... The cladding around each of the thousands of individual fiber cores hinders light transmission, and the gaps between cores limit resolution, primarily determined by the core diameter [18]. Additionally, crosstalk between adjacent cores blurs images, further diminishing quality [19][20][21]. ...
Fiber-bundle-based endoscopy, with its ultrathin probe and micrometer-level resolution, has become a widely adopted imaging modality for in vivo imaging. However, the fiber bundles introduce a significant honeycomb effect, primarily due to the multi-core structure and crosstalk of adjacent fiber cores, which superposes the honeycomb pattern image on the original image. To tackle this issue, we propose an iterative-free spatial pixel shifting (SPS) algorithm, designed to suppress the honeycomb effect and enhance real-time imaging performance. The process involves the creation of three additional sub-images by shifting the original image by one pixel at 0, 45, and 90 degree angles. These four sub-images are then used to compute differential maps in the x and y directions. By performing spiral integration on these differential maps, we reconstruct a honeycomb-free image with improved details. Our simulations and experimental results, conducted on a self-built fiber bundle-based endoscopy system, demonstrate the effectiveness of the SPS algorithm. SPS significantly improves the image quality of reflective objects and unlabeled transparent scattered objects, laying a solid foundation for biomedical endoscopic applications.
... Stoeve et al (2018) proposed a HOGbased (Histogram of Oriented Gradients) method to detect motion artefacts in CLE images automatically. Then there are hand-crafted methods (Eldaly et al 2018) to restore degraded CLE images. The issue of realistic noise in CLE images is currently unaddressed in any method. ...
Objective:
As an emerging diagnosis technology for gastrointestinal diseases, confocal laser endomicroscopy (CLE) is limited by the physical structure of the fiber bundle, leading to the inevitable production of various forms of noise during the imaging process. However, existing denoising methods based on hand-crafted features inefficiently deal with realistic noise in pCLE images. To alleviate this challenge, we proposed context-aware kernel estimation and multi-scale dynamic fusion modules to remove realistic noise in pCLE images, including multiplicative and additive white noise.
Approach:
Specifically, a realistic noise statistics model with random noise specific to pCLE data is constructed and further used to develop a self-supervised denoised model without the participation of clean images. Secondly, context-aware kernel estimation, which improves the representation of features by similar learnable region weights, addresses the problem of the non-uniform distribution of noises in pCLE images and proposes a lightweight denoised model (CLENet). Thirdly, we have developed a multi-scale dynamic fusion module that decouples and recalibrates features, providing a precise and contextually enriched representation of features. Finally, we integrated two developed modules into a U-shaped backbone to build an efficient denoising network named U-CLENet.
Main results:
Both proposed methods achieve comparable or better performance with low computational complexity on two gastrointestinal disease pCLE image datasets using the same training benchmark.
Significance:
The proposed approaches improve the visual quality of unclear pCLE images for various stages of tumor development, helping to reduce the rate of misdiagnosis in clinical decision-making and achieve computer graphics-assisted diagnosis.
... To obtain a quantitative measurement of phase, it is necessary to eliminate the phase delay introduced by the fiber bundle. Previous studies have utilized a linear forward model, such as the transmission matrix [34][35][36][37][38], to describe the relationship between the original image and the image degraded by the fiber. The linear model is not designed to address individual variations caused by fiber curvature or vibration, but rather to provide a reliable solution for fiber characterization. ...
The flexible endoscope is a minimally invasive tool in clinical settings, but most of them only provide qualitative amplitude information and require puncture biopsy or exogenous staining for diagnosis. These limitations restrict their use for rapid investigation. Here, we developed a fiber-bundle-based gradient light interference endoscopic microscopy (FGLIM) in reflection geometry for quantitative phase imaging of unlabeled thick samples. FGLIM provides the phase information by wavefront modulation using chessboard grating and phase-shifting. FGLIM system features high lateral and axial resolutions of 1.2 and 8.3 {\mu}m, respectively, with a probe diameter of 2.5 mm. By testing pathologic slices and thick opaque mammalian tissue ex vivo, FGLIM identifies normal and tumor glandular structures, secreta, and tomographic skin layers. With the potential for direct morphological and phase measurement, high resolution, and thin fiber tip, the label-free FGLIM could be an attractive tool for various clinical applications.
... (11) below). Finally, the obtained constrained problems are solved with using the alternating direction method of multipliers (ADMM) [2,30,19]. By a careful choice of the new variables, the initial problems are converted into a sequence of much simpler problems, which can be solved iteratively. ...
... Finally, the parameter μ > 0 is updated within the algorithm to keep the primal and dual residual norms within a factor of ρ = 10 of one another [12]. The stopping criterion we use is [2,19] ...
... By setting the offset c to c = 0, and number of exponential components I to I = 1, in the updates of a, r, y, and z in Equations (14), (19), (23), and (25), respectively, we can obtain Algorithm 3 for the SEXP model. ...
... 6 However, distortions, such as blur, noise, and decreased contrast, are common in confocal endoscopy imaging. Blur is the most common type of distortion in confocal endoscopy, which is caused by defocus, probe fiber core cross coupling, 7 and motion caused by the difference in movement speed between the investigated anatomical structures and the physician. 8 Owing to the small field-of-view of the confocal endoscopy, 9 to obtain a comprehensive view, a typical endoscopy examination produces thousands of images, most of which are not useful for diagnostic purposes 5 owing to image degradation and information loss caused by the above distortions. ...
Significance:
Confocal endoscopy images often suffer distortions, resulting in image quality degradation and information loss, increasing the difficulty of diagnosis and even leading to misdiagnosis. It is important to assess image quality and filter images with low diagnostic value before diagnosis.
Aim:
We propose a no-reference image quality assessment (IQA) method for confocal endoscopy images based on Weber's law and local descriptors. The proposed method can detect the severity of image degradation by capturing the perceptual structure of an image.
Approach:
We created a new dataset of 642 confocal endoscopy images to validate the performance of the proposed method. We then conducted extensive experiments to compare the accuracy and speed of the proposed method with other state-of-the-art IQA methods.
Results:
Experimental results demonstrate that the proposed method achieved an SROCC of 0.85 and outperformed other IQA methods.
Conclusions:
Given its high consistency in subjective quality assessment, the proposed method can screen high-quality images in practical applications and contribute to diagnosis.
... Most of state-of-the-art algorithms considered to solve linear inverse image restoration problems (irrespective of the noise model) are either optimization or simulation-based methods. Optimization-based approaches primarily rely on log-concave Bayesian models, such as [8][9][10][11][12][13][14][15], and have been proposed to perform maximum-a-posteriori (MAP) estimation. For example, PIDAL, which stands for Poisson image deblurring using augmented Lagrangian [8], and SALSA, which stands for split augmented Lagrangian shrinkage algorithm [10], are Poisson and Gaussian image restoration algorithms based on a totalvariation loss ot sparsity-promoting prior, which solves the restoration problem using an alternating direction method of multipliers (ADMM). ...
In this paper, we address the problem of activity estimation in Passive Gamma Emission Tomography (PGET) of spent nuclear fuel. Two different noise models are considered and compared; namely the isotropic Gaussian and the Poisson noise models. The problem is formulated within a Bayesian framework as a linear inverse problem and prior distributions are assigned to the unknown model parameters. In particular, a Bernoulli-truncated Gaussian prior model is considered to promote sparse pin configurations. A Markov chain Monte Carlo (MCMC) method based on a split and augmented Gibbs sampler is then used to sample the posterior distribution of the unknown parameters. The proposed algorithm is first validated by simulations conducted using synthetic data, generated using the nominal models. We then consider more realistic data simulated using a bespoke simulator whose forward model is non-linear and not available analytically. In that case, the linear models used are mis-specified and we analyse their robustness for activity estimation. The results demonstrate superior performance of the proposed approach in estimating the pin activities in different assembly patterns in addition to being able to quantify their uncertainty measures, in comparison with existing methods.
... Typical methods, such as filtering methods [9][10][11] or interpolating methods [12,13], do not result in an improvement in spatial resolution. Recently, several computational iterative reconstruction methods have been proposed for removing this pattern, such as Bayesian approximation algorithm [14], l 1 norm minimization in the wavelet domain [15], hierarchical Bayesian model [16,17], iterative shrinkage thresholding algorithm [18], and so on. However, these methods are considered computationally expensive and only reconstruct the limited information from one low-resolution (LR) input image. ...
A fiber bundle allows easy access to a wide range of human tissue. It has smaller diameters, which limits its effective field of view (FOV), and consists of a large number of cores surrounded by a cladding layer, which reduces its spatial resolution. In this paper, we develop an algorithm that processes successively captured raw fiber bundle images in an online fashion. Our algorithm tackles the tasks of super-resolution (SR) and video mosaicing jointly. The natural movement of the fiber tip in successive frames produces offsets that are random in the pixel domain to apply multi-frame SR imaging. Meanwhile, the associated FOV can be extended by mosaicing reconstructed SR images with obtained shifted information. Our approach has low computational complexity that allows for processing in real-time. The performance of resolution improvement in real-time and video mosaicing is demonstrated on the resolution target and biological samples.
... In this manner, CFBs facilitate in vivo microscopy in regions of the body [1][2][3][4][5] including the respiratory system [6][7][8] or GI tract [9], where it would otherwise be challenging to observe biological processes in action. When using a CFB, image resolution can be increased by maximizing the packing density of the light-guiding cores until core crosstalk due to evanescent field coupling introduces blurring and reduces image contrast beyond an acceptable level [10][11][12]. ...
Coherent fiber bundles are used widely for imaging. Commonly, disordered arrays of randomly sized fiber cores avoid proximity between like-cores, which would otherwise result in increased core crosstalk and a negative impact on imaging. Recently, stack-and-draw fiber manufacture techniques have been used to produce fibers with a controlled core layout to minimize core crosstalk. However, one must take manufacturing considerations into account during stack-and-draw fiber design in order to avoid impractical or unachievable fabrication. This comes with a set of practical compromises, such as using only a small number of different core sizes. Through characterization of core crosstalk patterns, this Letter aims to aid the understanding of crosstalk limitations imposed by such compromises in the core layout made for ease of fabrication.
... Image resolution is increased by dense core packaging up to the limit of core crosstalk [27], a power coupling between the individual cores, which introduces blurring and reduces image contrast. Approaches to overcome this limitation include high levels of glass doping, structured packaging of varied core sizes, and signal processing [28,29]. Recently, a low index contrast imaging fibre bundle was reported using commercially available preforms typically used in the telecommunication industry which achieved low core crosstalk by relying on a square array of cores [30]. ...
This paper demonstrates how research at the intersection of physics, engineering, biology and medicine can be presented in an interactive and educational way to a non-scientific audience. Interdisciplinary research with a focus on prevalent diseases provides a relatable context that can be used to engage with the public. Respiratory diseases are significant contributors to avoidable morbidity and mortality and have a growing social and economic impact. With the aim of improving lung disease understanding, new techniques in fibre-based optical endomicroscopy have been recently developed. Here, we present a novel engagement activity that resembles a bench-to-bedside pathway. The activity comprises an inexpensive educational tool (<$70) adapted from a clinical optical endomicroscopy system and tutorials that cover state-of-the-art research. The activity was co-created by high school science teachers and researchers in a collaborative way that can be implemented into any engagement development process.
... In this work, we investigate the performance of an unsupervised approach for bacterial detection in datasets of optical endomicroscopy (OEM) lung images [15][16][17][18][19][20][21]. The main contributions of this work are threefold. ...
... The core idea is to convert this unconstrained minimization problem into another constrained one by the application of a variable splitting operation (see Eq. (3) below). Finally, the obtained constrained problem is solved with using ADMM [19,22,23]. By a careful choice of the new variables, the initial problem is converted into a sequence of much simpler problems, which can be solved iteratively. ...
... ≤ , which is the sum of the primal and dual residuals, where = √ P × L × 10 −6 [19,22]. ...
