The ability to distinguish macrophage subtypes noninvasively could have diagnostic potential in cancer, atherosclerosis, and diabetes, where polarized M1 and M2 macrophages play critical and often opposing roles. Current methods to distinguish macrophage subtypes rely on tissue biopsy. Optical imaging techniques based on light scattering are of interest as they can be translated into biopsy-free strategies. Because mitochondria are relatively strong subcellular light scattering centers, and M2 macrophages are known to have enhanced mitochondrial biogenesis compared to M1, we hypothesized that M1 and M2 macrophages may have different angular light scattering profiles. To test this, we developed an in vitro angle-resolved forward light scattering measurement system. We found that M1 and M2 macrophage monolayers scatter relatively unequal amounts of light in the forward direction between 1.6 deg and 3.2 deg with M2 forward scattering significantly more light than M1 at increasing angles. The ratio of forward scattering can be used to identify the polarization state of macrophage populations in culture.
The objective of this study was to assess the ability of combined photothermal wave (PTW) imaging and optical coherence tomography (OCT) to detect, and further characterize the distribution of macrophages (having taken up plasmonic gold nanorose as a contrast agent) and lipid deposits in atherosclerotic plaques. Aortas with atherosclerotic plaques were harvested from nine male New Zealand white rabbits divided into nanorose- and saline-injected groups and were imaged by dual-wavelength (800 and 1210 nm) multifrequency (0.1, 1 and 4 Hz) PTW imaging in combination with OCT. Amplitude PTW images suggest that lateral and depth distribution of nanorose-loaded macrophages (confirmed by two-photon luminescence microscopy and RAM-11 macrophage stain) and lipid deposits can be identified at selected modulation frequencies. Radiometric temperature increase and modulation amplitude of superficial nanoroses in response to 4 Hz laser irradiation (800 nm) were significantly higher than native plaque (P<0.001). Amplitude PTW images (4 Hz) were merged into a coregistered OCT image, suggesting that superficial nanorose-loaded macrophages are distributed at shoulders on the upstream side of atherosclerotic plaques (P<0.001) at edges of lipid deposits. Results suggest that combined PTW-OCT imaging can simultaneously reveal plaque structure and composition, permitting characterization of nanorose-loaded macrophages and lipid deposits in atherosclerotic plaques.
Retinal nerve fiber layer (RNFL) thickness, a measure of glaucoma progression, can be measured in images acquired
by spectral domain optical coherence tomography (OCT). The accuracy of RNFL thickness estimation, however, is
affected by the quality of the OCT images. In this paper, a new parameter, signal deviation (SD), which is based on the
standard deviation of the intensities in OCT images, is introduced for objective assessment of OCT image quality. Two
other objective assessment parameters, signal to noise ratio (SNR) and signal strength (SS), are also calculated for each
OCT image. The results of the objective assessment are compared with subjective assessment. In the subjective
assessment, one OCT expert graded the image quality according to a three-level scale (good, fair, and poor). The OCT
B-scan images of the retina from six subjects are evaluated by both objective and subjective assessment. From the
comparison, we demonstrate that the objective assessment successfully differentiates between the acceptable quality
images (good and fair images) and poor quality OCT images as graded by OCT experts. We evaluate the performance
of the objective assessment under different quality assessment parameters and demonstrate that SD is the best at
distinguishing between fair and good quality images. The accuracy of RNFL thickness estimation is improved
significantly after poor quality OCT images are rejected by automated objective assessment using the SD, SNR, and
We introduce a method based on optical reflectivity changes to segment the retinal nerve fiber layer (RNFL) in images recorded using swept source spectral domain optical coherence tomography (OCT). The segmented image is used to determine the RNFL thickness. Simple filtering followed by edge detecting techniques can successfully be applied to segment the RNFL from recorded images and estimate RNFL thickness. The method is computationally more efficient than previously reported approaches. Higher computational efficiency allows faster segmentation and provides the ophthalmologist segmented retinal images that better utilize advantages of spectral domain OCT instrumentation. OCT B-scan and fundus images of the retina are recorded for 5 patients. The segmentation method is applied on B-scan images recorded from all patients. An expert ophthalmologist separately demarcates the RNFL layer in the OCT images from the same patients in each B-scan image. Results from automated image processing software are compared to the boundary demarcated by the expert ophthalmologist. The absolute error between the boundaries demarcated by the expert and the algorithm is expressed in terms of area and is used as an error metric. Ability of the algorithm to accurately segment the RNFL in comparison with an expert ophthalmologist is reported.
The relationship between retinal nerve fiber layer (RNFL) birefringence (&Dgr;n) and neurotubule density (NTD, retinal
ganglion cell (RGC) neurotubules per unit RNFL area) was investigated by correlating measurements of these two
parameters in 1 eye of a healthy cynomolgus monkey. Phase retardation per unit depth (PR/UD, proportional to &Dgr;n) was
measured at 5.6-15<sup>o</sup> intervals around the optic nerve head (ONH) with an enhanced polarization-sensitive optical
coherence tomography (EPS-OCT) instrument. Transverse tissue sections containing 3 RGC nerve fiber bundles from
each peripapillary RNFL octant were imaged with a transmission electron microscope (TEM). Morphological
measurements taken in TEM images were used by a novel algorithm to estimate NTD. Registered PR/UD and NTD data
were then correlated using single- and multi-level models, yielding correlation coefficients in the range 0.49 ⩽ r ⩽ 0.61
(0.06 ⩽ P ⩽ 0.11). It was found that in order for the single-level correlation coefficient (r = 0.61) to be statistically
significant (P ⩽ 0.05) and powerful (Power ⩾ 80%), NTD measurements in at least 16, rather than 8, RNFL sectors were
needed. Interestingly, a single-level correlation coefficient of r = 0.81 (P = 0.01) was calculated between octant-averaged
PR/UD and RGC axoplasmic area (A<sub>x</sub>, axon area less non-cytoskeletal organelle area) mode. A<sub>x</sub> represents a
RGC axon's neurotubule-inhabitable area. Intuitively, a strong relationship should exist between A<sub>x</sub> and neurotubule
number if neurotubules provide the primary structural support for RGC axons and structural requirements are the same
in all RGC axons. If this relationship exists, error resulting from NTD estimation methods or preservation artifacts may
have caused lower observed correlations of PR/UD with NTD than with A<sub>x</sub> mode, and more accurate methods of
measuring in vivo NTD may be required to determine an accurate relationship between RNFL birefringence and NTD.
Characterizing and quantifying noise sources in birefringence imaging with polarization-sensitive optical coherence tomography (PS-OCT) is necessary for the development of efficient noise reduction techniques for real-time clinical PS-OCT imaging. We propose three noise regimes based on the strength of specimen backscattering and dominated by different noise sources. We introduce a model that predicts noise effects in two regimes. The model includes source/detector intensity noise, and couples speckle effects with the longitudinal delays due to instrument and specimen birefringence to create realistic noise on simulated orthogonal interference fringe amplitudes and on their relative phases. Experimental examples of the three regimes are presented and in two of them, qualitative agreement between the model and experimental data is demonstrated.