A portable, spatially resolved, diffuse reflectance lensless imaging technique based on the charge-coupled device or complementary metal-oxide semiconductor sensor directly coupled to the fiber optic bundle is proposed for visualization of subsurface structures such as superficial microvasculature in the epithelium. We discuss an experimental method for emulating a lensless imaging setup via raster scanning a single fiber-optic cable over a microfluidic phantom containing periodic hemoglobin absorption contrast. To evaluate the ability of the technique to recover information about the subsurface linear structures, scattering layers formed of the Sylgard® 184 Silicone Elastomer and titanium dioxide were placed atop the microfluidic phantom. Thickness of the layers ranged from 0.2 to 0.7 mm, and the values of the reduced scattering coefficient (μs′) were between 0.85 and 4.25 mm−1. The results demonstrate that fiber-optic, lensless platform can be used for two-dimensional imaging of absorbing inclusions in diffuse reflectance mode. In these experiments, it was shown that diffuse reflectance imaging can provide sufficient spatial sampling of the phantom for differentiation of 30 μm structural features of the embedded absorbing pattern inside the scattering media.
A portable, spatially resolved diffuse reflectance (SRDR) lensless imaging technique based on the charge coupled device (CCD), or complementary metal-oxide semiconductor (CMOS) sensor directly coupled with fiber optic bundle can be proposed for visualization of subsurface structures such as intrapapillary capillary loops (IPCLs). In this article, we discuss an experimental method for emulating a lensless imaging setup via raster scanning a single fiberoptic cable (where image is relayed onto the sensor surface through a fiber-optic cable equivalent to coupling a fiber optic conduit directly onto the sensor surface without any lenses) over a microfluidic phantom containing periodic hemoglobin absorption contrast. For mimicking scattering properties of turbid media, a diffusive layer formed of polydimethylsiloxane (PDMS) and titanium dioxide (TiO2) was placed atop of the microfluidic phantom. Thickness of the layers ranged from 0.2-0.7mm, and the μs` value of the layers were in the range of 0.85 mm-1 – 4.25mm-1. The results demonstrate that a fiber-optic bundle/plate coupled lensless imaging setup has a high potential to recover intensity modulations from the subsurface patterns. Decreasing of the interrogation volumes leads to enhanced spatial resolution of diffuse reflectance imaging, and hence, can potentially overcome the scattering caused blurring.
We performed the independent component analysis of the hyperspectral functional near-infrared data acquired on humans during exercise and rest. We found that the hyperspectral functional data acquired on the human brain requires only two physiologically meaningful components to cover more than 50% o the temporal variance in hundreds of wavelengths. The analysis of the spectra of independent components showed that these components could be interpreted as results of changes in the cerebral blood volume and blood flow. Also, we found significant contributions of water and cytochrome c oxydase into changes associated with the independent components. Another remarkable effect of ICA was its good performance in terms of the filtering of the data noise.
We analyze statistically independent temporal components in the broadband spectrum of near-infrared data acquired on
the human head during breath holding. Breath holding is used to cause strong cerebral hemodynamic changes. The
signals from 698 wavelengths of 25-mm long channel and 1289 wavelengths from the 10-mm channel were analyzed
using an independent component analysis algorithm. We show that in spite of a very large number of channels only two
components with their characteristic optical spectra remain stable across multi-subject analysis. We demonstrate a
comparison of fNIRS independent components with simultaneously acquired functional MRI data.
KEYWORDS: Near infrared spectroscopy, Independent component analysis, Functional magnetic resonance imaging, Signal processing, Magnetic resonance imaging, Chromophores, Principal component analysis, Data acquisition, Blood, Carbon dioxide
Although near infrared spectroscopy (NIRS) is now widely used both in emerging clinical techniques and in
cognitive neuroscience, the development of the apparatuses and signal processing methods for these applications
is still a hot research topic. The main unresolved problem in functional NIRS is the separation of functional
signals from the contaminations by systemic and local physiological fluctuations. This problem was approached by
using various signal processing methods, including blind signal separation techniques. In particular, principal
component analysis (PCA) and independent component analysis (ICA) were applied to the data acquired
at the same wavelength and at multiple sites on the human or animal heads during functional activation.
These signal processing procedures resulted in a number of principal or independent components that could be
attributed to functional activity but their physiological meaning remained unknown. On the other hand, the best
physiological specificity is provided by broadband NIRS. Also, a comparison with functional magnetic resonance
imaging (fMRI) allows determining the spatial origin of fNIRS signals. In this study we applied PCA and ICA
to broadband NIRS data to distill the components correlating with the breath hold activation paradigm and
compared them with the simultaneously acquired fMRI signals. Breath holding was used because it generates
blood carbon dioxide (CO2) which increases the blood-oxygen-level-dependent (BOLD) signal as CO2 acts as
a cerebral vasodilator. Vasodilation causes increased cerebral blood flow which washes deoxyhaemoglobin out
of the cerebral capillary bed thus increasing both the cerebral blood volume and oxygenation. Although the
original signals were quite diverse, we found very few different components which corresponded to fMRI signals
at different locations in the brain and to different physiological chromophores.
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