Automated quantification of lung structures from optical coherence tomography images

Massively parallel simulator of optical coherence tomography of inhomogeneous turbid media

BACKGROUND AND OBJECTIVE

An accurate and practical simulator for Optical Coherence Tomography (OCT) could be an important tool to study the underlying physical phenomena in OCT such as multiple light scattering. Recently, many researchers have investigated simulation of OCT of turbid media, e.g., tissue, using Monte Carlo methods. The main drawback of these earlier simulators is the long computational time required to produce accurate results. We developed a massively parallel simulator of OCT of inhomogeneous turbid media that obtains both Class I diffusive reflectivity, due to ballistic and quasi-ballistic scattered photons, and Class II diffusive reflectivity due to multiply scattered photons.

METHODS

This Monte Carlo-based simulator is implemented on graphic processing units (GPUs), using the Compute Unified Device Architecture (CUDA) platform and programming model, to exploit the parallel nature of propagation of photons in tissue. It models an arbitrary shaped sample medium as a tetrahedron-based mesh and uses an advanced importance sampling scheme.

RESULTS

This new simulator speeds up simulations of OCT of inhomogeneous turbid media by about two orders of magnitude. To demonstrate this result, we have compared the computation times of our new parallel simulator and its serial counterpart using two samples of inhomogeneous turbid media. We have shown that our parallel implementation reduced simulation time of OCT of the first sample medium from 407 min to 92 min by using a single GPU card, to 12 min by using 8 GPU cards and to 7 min by using 16 GPU cards. For the second sample medium, the OCT simulation time was reduced from 209 h to 35.6 h by using a single GPU card, and to 4.65 h by using 8 GPU cards, and to only 2 h by using 16 GPU cards. Therefore our new parallel simulator is considerably more practical to use than its central processing unit (CPU)-based counterpart.

CONCLUSIONS

Our new parallel OCT simulator could be a practical tool to study the different physical phenomena underlying OCT, or to design OCT systems with improved performance.

Automated segmentation and quantification of airway mucus with endobronchial optical coherence tomography

We propose a novel suite of algorithms for automatically segmenting the airway lumen and mucus in endobronchial optical coherence tomography (OCT) data sets, as well as a novel approach for quantifying the contents of the mucus. Mucus and lumen were segmented using a robust, multi-stage algorithm that requires only minimal input regarding sheath geometry. The algorithm performance was highly accurate in a wide range of airway and noise conditions. Mucus was classified using mean backscattering intensity and grey level co-occurrence matrix (GLCM) statistics. We evaluated our techniques in vivo in asthmatic and non-asthmatic volunteers.

Clinical potential for imaging in patients with asthma and other lung disorders

The ability of lung imaging to phenotype patients, determine prognosis, and predict response to treatment is expanding in clinical and translational research. The purpose of this perspective is to describe current imaging modalities that might be useful clinical tools in patients with asthma and other lung disorders and to explore some of the new developments in imaging modalities of the lung. These imaging modalities include chest radiography, computed tomography, lung magnetic resonance imaging, electrical impedance tomography, bronchoscopy, and others.

Optical coherence tomography in respiratory science and medicine: from airways to alveoli

Optical coherence tomography is a rapidly maturing optical imaging technology, enabling study of the in vivo structure of lung tissue at a scale of tens of micrometers. It has been used to assess the layered structure of airway walls, quantify both airway lumen caliber and compliance, and image individual alveoli. This article provides an overview of the technology and reviews its capability to provide new insights into respiratory disease.

Speckle statistics in OCT images: Monte Carlo simulations and experimental studies

The speckle pattern of an optical coherence tomography (OCT) image carries potentially useful sample information that may assist in tissue characterization. Recent biomedical results in vivo indicate that the distribution of signal intensities within an OCT tissue image is well described by a log-normal-like (Gamma) function. To fully understand and exploit this finding, an OCT Monte Carlo model that accounts for speckle effects was developed. The resultant Monte Carlo speckle statistics predictions agree well with experimental OCT results from a series of control phantoms with variable scattering properties; the Gamma distribution provides a good fit to the theoretical and experimental results. The ability to quantify subresolution tissue features via OCT speckle analysis may prove useful in diagnostic photomedicine.