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Nikola Krstajic

Publications and source records attributed to Nikola Krstajic.

2 recordsLinked to original sources

High-speed dual color fluorescence lifetime endomicroscopy for highly-multiplexed pulmonary diagnostic applications and detection of labeled bacteria

We present a dual color laser scanning endomicroscope capable of fluorescence lifetime endomicroscopy at one frame per second (FPS). The scanning system uses a coherent imaging fiber with 30,000 cores. High-speed lifetime imaging is achieved by distributing the signal over an array of 1024 parallel single-photon avalanche photo--diode detectors (SPADs), minimizing detection dead time maximizing the number of photons detected per excitation pulse without photon pile--up to achieve the high frame rate. Dual color fluorescence imaging is achieved by temporally shifting the dual excitation lasers, with respect to each other, to separate the two spectrally distinct fluorescent decays. Combining the temporal encoding, to provide spectral separation, with lifetime measurements we show a one FPS, multi-channel endomicroscopy platform for clinical applications and diagnosis. We demonstrate the potential of the system by imaging smartprobe-labeled bacteria in ex vivo samples of human lung using lifetime to differentiate bacterial fluorescence from the strong background lung auto--fluorescence which was used to provide structural information.

physics.med-ph↗

Deconvolution and Restoration of Optical Endomicroscopy Images

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.

cs.CV↗