Multimode fiber as a tool to reduce cross talk in Fourier-domain full-field optical coherence tomography

Optical-coherence-tomography-based deep-learning scatterer-density estimator using physically accurate noise model

We demonstrate a deep-learning-based scatterer density estimator (SDE) that processes local speckle patterns of optical coherence tomography (OCT) images and estimates the scatterer density behind each speckle pattern. The SDE is trained using large quantities of numerically simulated OCT images and their associated scatterer densities. The numerical simulation uses a noise model that incorporates the spatial properties of three types of noise, i.e., shot noise, relative-intensity noise, and non-optical noise. The SDE's performance was evaluated numerically and experimentally using two types of scattering phantom and in vitro tumor spheroids. The results confirmed that the SDE estimates scatterer densities accurately. The estimation accuracy improved significantly when compared with our previous deep-learning-based SDE, which was trained using numerical speckle patterns generated from a noise model that did not account for the spatial properties of noise.

Diffuse-illumination holographic optical coherence tomography

Holographic optical coherence tomography (OCT) is a powerful imaging technique, but its ability to reveal low-reflectivity features is limited. In this study, we performed holographic OCT by incoherently averaging volumes with changing diffuse illumination of numerical aperture (NA) equal to the detection NA. While the reduction of speckle from singly scattered light is only modest, we discovered that speckle from multiply scattered light can be arbitrarily reduced, resulting in substantial improvements in image quality. This technique also offers the advantage of suppressing noises arising from spatial coherence, and can be implemented with a partially spatially incoherent light source for further mitigation of multiple scattering. Finally, we show that although holographic reconstruction capabilities are increasingly lost with decreasing spatial coherence, they can be retained over an axial range sufficient to standard OCT applications.

Continuous-wave parallel interferometric near-infrared spectroscopy (CW NIRS) with a fast two-dimensional camera

Interferometric near-infrared spectroscopy (iNIRS) is an optical method that noninvasively measures the optical and dynamic properties of the human brain in vivo. However, the original iNIRS technique uses single-mode fibers for light collection, which reduces the detected light throughput. The reduced light throughput is compensated by the relatively long measurement or integration times (∼1 sec), which preclude monitoring of rapid blood flow changes that could be linked to neural activation. Here, we propose parallel interferometric near-infrared spectroscopy (πNIRS) to overcome this limitation. In πNIRS we use multi-mode fibers for light collection and a high-speed, two-dimensional camera for light detection. Each camera pixel acts effectively as a single iNIRS channel. So, the processed signals from each pixel are spatially averaged to reduce the overall integration time. Moreover, interferometric detection provides us with the unique capability of accessing complex information (amplitude and phase) about the light remitted from the sample, which with more than 8000 parallel channels, enabled us to sense the cerebral blood flow with only a 10 msec integration time (∼100x faster than conventional iNIRS). In this report, we have described the theoretical foundations and possible ways to implement πNIRS. Then, we developed a prototype continuous wave (CW) πNIRS system and validated it in liquid phantoms. We used our CW πNIRS to monitor the pulsatile blood flow in a human forearm in vivo. Finally, we demonstrated that CW πNIRS could monitor activation of the prefrontal cortex by recording the change in blood flow in the forehead of the subject while he was reading an unknown text.

Phase-Sensitive Measurements of Depth-Dependent Signal Transduction in the Inner Plexiform Layer

Non-invasive spatially resolved functional imaging in the human retina has recently attracted considerable attention. Particularly functional imaging of bipolar and ganglion cells could aid in studying neuronal activity in humans, including an investigation of processes of the central nervous system. Recently, we imaged the activity of the inner neuronal layers by measuring nanometer-size changes of the cells within the inner plexiform layer (IPL) using phase-sensitive optical coherence tomography (OCT). In the IPL, there are connections between the neuronal cells that are dedicated to the processing of different aspects of the visual information, such as edges in the image or temporal changes. Still, so far, it was not possible to assign functional changes to single cells or cell classes in living humans, which is essential for studying the vision process. One characteristic of signal processing in the IPL is that different aspects of the visual impression are only processed in specific sub-layers (strata). Here, we present an investigation of these functional signals for three different sub-layers in the IPL with the aim to separate different properties of the visual signal processing. Whereas the inner depth-layer, closest to the ganglion cells, exhibits an increase in the optical path length, the outer depth-layer, closest to the bipolar cell layer, exhibits a decrease in the optical path length. Additionally, we found that the central depth is sensitive to temporal changes, showing a maximum response at a stimulation frequency of around 12.5 Hz. The results demonstrate that the signals from different cell types can be distinguished by phase-sensitive OCT.

Computational refocusing of Jones matrix polarization-sensitive optical coherence tomography and investigation of defocus-induced polarization artifacts

Here we demonstrate a long-depth-of-focus imaging method using polarization sensitive optical coherence tomography (PS-OCT). This method involves a combination of Fresnel-diffraction-model-based phase sensitive computational refocusing and Jones-matrix based PS-OCT (JM-OCT). JM-OCT measures four complex OCT images corresponding to four polarization channels. These OCT images are computationally refocused as preserving the mutual phase consistency. This method is validated using a static phantom, postmortem zebrafish, and ex vivo porcine muscle samples. All the samples demonstrated successful computationally-refocused birefringence and degree-of-polarization-uniformity (DOPU) images. We found that defocusing induces polarization artifacts, i.e., incorrectly high birefringence values and low DOPU values, which are substantially mitigated by computational refocusing.

Light-adapted flicker optoretinograms captured with a spatio-temporal optical coherence-tomography (STOC-T) system

For many years electroretinography (ERG) has been used for obtaining information about the retinal physiological function. More recently, a new technique called optoretinography (ORG) has been developed. In one form of this technique, the physiological response of retinal photoreceptors to visible light, resulting in a nanometric photoreceptor optical path length change, is measured by phase-sensitive optical coherence tomography (OCT). To date, a limited number of studies with phase-based ORG measured the retinal response to a flickering light stimulation. In this work, we use a spatio-temporal optical coherence tomography (STOC-T) system to capture optoretinograms with a flickering stimulus over a 1.7 × 0.85 mm² area of a light-adapted retina located between the fovea and the optic nerve. We show that we can detect statistically-significant differences in the photoreceptor optical path length (OPL) modulation amplitudes in response to different flicker frequencies and with better signal to noise ratios (SNRs) than for a dark-adapted eye. We also demonstrate the ability to spatially map such response to a patterned stimulus with light stripes flickering at different frequencies, highlighting the prospect of characterizing the spatially-resolved temporal-frequency response of the retina with ORG.