Diffuse reflectance spectroscopy (DRS) can be used to noninvasively measure skin properties. To extract skin properties from DRS spectra, you need a model that relates the reflectance to the tissue properties. Most models are based on the assumption that skin is homogenous. In reality, skin is composed of multiple layers, and the homogeneity assumption can lead to errors. In this study, we analyze the errors caused by the homogeneity assumption. This is accomplished by creating realistic skin spectra using a computational model, then extracting properties from those spectra using a one-layer model. The extracted parameters are then compared to the parameters used to create the modeled spectra. We used a wavelength range of 400 to 750 nm and a source detector separation of 250 μm. Our results show that use of a one-layer skin model causes underestimation of hemoglobin concentration [Hb] and melanin concentration [mel]. Additionally, the magnitude of the error is dependent on epidermal thickness. The one-layer assumption also causes [Hb] and [mel] to be correlated. Oxygen saturation is overestimated when it is below 50% and underestimated when it is above 50%. We also found that the vessel radius factor used to account for pigment packaging is correlated with epidermal thickness.
The sampling depth of light for diffuse reflectance spectroscopy is analyzed both experimentally and computationally. A Monte Carlo (MC) model was used to investigate the effect of optical properties and probe geometry on sampling depth. MC model estimates of sampling depth show an excellent agreement with experimental measurements over a wide range of optical properties and probe geometries. The MC data are used to define a mathematical expression for sampling depth that is expressed in terms of optical properties and probe geometry parameters.
We present a Monte Carlo lookup table (MCLUT)-based inverse model for extracting optical properties from tissue-simulating phantoms. This model is valid for close source-detector separation and highly absorbing tissues. The MCLUT is based entirely on Monte Carlo simulation, which was implemented using a graphics processing unit. We used tissue-simulating phantoms to determine the accuracy of the MCLUT inverse model. Our results show strong agreement between extracted and expected optical properties, with errors rate of 1.74% for extracted reduced scattering values, 0.74% for extracted absorption values, and 2.42% for extracted hemoglobin concentration values.
Ultrasound poroelastography can quantify structural and mechanical properties of tissues such as stiffness,
compressibility, and fluid flow rate. This novel ultrasound technique is being explored to detect tissue changes
associated with lymphatic disease. We have constructed a macroscopic fluorescence imaging system to validate
ultrasonic fluid flow measurements and to provide high resolution imaging of microfluidic phantoms. The optical
imaging system is composed of a white light source, excitation and emission filters, and a camera with a zoom lens. The
field of view can be adjusted from 100 mm x 75 mm to 10 mm x 7.5 mm. The microfluidic device is made of
polydimethylsiloxane (PDMS) and has 9 channels, each 40 μm deep with widths ranging from 30 μm to 200 μm. A
syringe pump was used to propel water containing 15 μm diameter fluorescent microspheres through the microchannels,
with flow rates ranging from 0.5 μl/min to 10 μl/min. Video was captured at a rate of 25 frames/sec. The velocity of the
microspheres in the microchannels was calculated using an algorithm that tracked the movement of the fluorescent
microspheres. The imaging system was able to measure particle velocities ranging from 0.2 mm/sec to 10 mm/sec. The
range of flow velocities of interest in lymph vessels is between 1 mm/sec to 10 mm/sec; therefore our imaging system is
sufficient to measure particle velocity in phantoms modeling lymphatic flow.
In-vivo reflectance confocal microscopy (RCM) shows promise for the early detection of superficial spreading melanoma (SSM). RCM of SSM shows pagetoid melanocytes (PMs) in the epidermis and disarray at the dermal-epidermal junction (DEJ), which are automatically quantified with a computer algorithm that locates depth of the most superficial pigmented surface [DSPS(x,y)] containing PMs in the epidermis and pigmented basal cells near the DEJ. The algorithm uses 200 noninvasive confocal optical sections that image the superficial 200 μm of ten skin sites: five unequivocal SSMs and five nevi. The pattern recognition algorithm automatically identifies PMs in all five SSMs and finds none in the nevi. A large mean gradient ψ (roughness) between laterally adjacent points on DSPS(x,y) identifies DEJ disruption in SSM ψ = 11.7 ± 3.7 [−] for n = 5 SSMs versus a small ψ = 5.5 ± 1.0 [−] for n = 5 nevi (significance, p = 0.0035). Quantitative endpoint metrics for malignant characteristics make digital RCM data an attractive diagnostic asset for pathologists, augmenting studies thus far, which have relied largely on visual assessment.
In vivo reflectance confocal microscopy shows promise for the early detection of malignant melanoma. One diagnostic
trait of malignancy is the presence of pagetoid melanocytes in the epidermis. For automated detection of MM, this
feature must be identified quantitatively through software. Beginning with in vivo, noninvasive confocal images from 10
unequivocal MMs and benign nevi, we developed a pattern recognition algorithm that automatically identified pagetoid
melanocytes in all four MMs and identified none in five benign nevi. One data set was discarded due to artifacts caused
by patient movement. With future work to bring the performance of this pattern recognition technique to the level of the
clinicians on difficult lesions, melanoma diagnosis could be brought to primary care facilities and save many lives by
improving early diagnosis.
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