Abstract
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 visualization and subsequent analysis remains a challenging problem. Cross coupling between fiber cores and sparse sampling by imaging fiber bundles are the main reasons for image degradation, and poor detection performance (i.e., inflammation, bacteria, etc.). In this work, we address the problem of deconvolution and restoration of OEM data. We propose a hierarchical Bayesian model to solve this problem and compare three estimation algorithms to exploit the resulting joint posterior distribution. The first method is based on Markov chain Monte Carlo (MCMC) methods, however, it exhibits a relatively long computational time. The second and third algorithms deal with this issue and are based on a variational Bayes (VB) approach and an alternating direction method of multipliers (ADMM) algorithm respectively. Results on both synthetic and real datasets illustrate the effectiveness of the proposed methods for restoration of OEM images.
Highlights
P NEUMONIA is a major cause of morbidity and mortality in mechanically ventilated patients in intensive care [1]
The main contributions of this work are fourfold: 1) We address the problem of deconvolution and restoration in Optical endomicroscopy (OEM)
2) We develop algorithms dedicated to irregularly sampled images which do not rely on strong assumptions about the spatial structure of the sampling patterns
Summary
P NEUMONIA is a major cause of morbidity and mortality in mechanically ventilated patients in intensive care [1]. Many studies have considered hierarchical Bayesian models to solve the deconvolution and restoration problem [22]–[31] These models offer a flexible and consistent methodology to deal with uncertainty in inference when limited amount of data or information is available. Other unknown parameters can be jointly estimated within the algorithm such as noise variance(s) and regularization parameters As such, they represent an attractive way to tackle ill-posed problems such as the one considered in this work. The second algorithm uses the variational Bayes (VB) methodology [33], [34] to approximate the joint posterior distribution by minimizing the KullbackLeibler (KL) divergence between the true posterior distribution and its approximation [35] It can estimate the hyperparameters associated with the prior distributions, and it is totally unsupervised, as is the MCMC-based method.
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