While Doppler OCT is effective at measuring axial velocities, OCT autocorrelation was introduced to measure flow in all directions. However, accuracy is limited as several factors other than particle velocity including diffusion, blood-cell tumbling, and axial velocity gradient affect signal decorrelation.
Here, we show that the use of the ratio of autocorrelation from few-mode optical coherence tomography (FM-OCT) improves the accuracy of transverse flow velocity measurements. To validate our approach, we showed that milk velocity pumped through a circular tube at constant speed was accurately retrieved.
We present here an open-source platform capable of simulating Optical Coherence Tomography (OCT) images to be used by the community. Such a simulator can have a variety of applications such as the training of neural networks. Our first-generation Monte Carlo simulator quantifies detected photons path length. The code achieves reasonable runtime by exploiting a bias scattering scheme. The results are then interpreted to produce an OCT A-line. A Github platform was implemented with necessary documentation (e.g. readme file and instructions guide) to allow a variety of application and geometry as well as community-based improvements over time.
KEYWORDS: Near infrared spectroscopy, Veins, Monte Carlo methods, Brain, In vivo imaging, Tissue optics, Functional magnetic resonance imaging, Tissues, Oxygen, Data modeling
Near-Infrared Spectroscopy (NIRS) measures the functional hemodynamic response occuring at the surface of
the cortex. Large pial veins are located above the surface of the cerebral cortex. Following activation, these
veins exhibit oxygenation changes but their volume likely stays constant. The back-reflection geometry of the
NIRS measurement renders the signal very sensitive to these superficial pial veins. As such, the measured NIRS
signal contains contributions from both the cortical region as well as the pial vasculature. In this work, the
cortical contribution to the NIRS signal was investigated using (1) Monte Carlo simulations over a realistic
geometry constructed from anatomical and vascular MRI and (2) multimodal NIRS-BOLD recordings during
motor stimulation. A good agreement was found between the simulations and the modeling analysis of in vivo
measurements. Our results suggest that the cortical contribution to the deoxyhemoglobin signal change (ΔHbR)
is equal to 16-22% of the cortical contribution to the total hemoglobin signal change (ΔHbT). Similarly, the
cortical contribution of the oxyhemoglobin signal change (ΔHbO) is equal to 73-79% of the cortical contribution
to the ΔHbT signal. These results suggest that ΔHbT is far less sensitive to pial vein contamination and
therefore, it is likely that the ΔHbT signal provides better spatial specificity and should be used instead of
ΔHbO or ΔHbR to map cerebral activity with NIRS. While different stimuli will result in different pial vein
contributions, our finger tapping results do reveal the importance of considering the pial contribution.
NIRS is safe, non-invasive and offers the possibility to record local hemodynamic parameters at the bedside,
avoiding the transportation of neonates and critically ill patients. In this work, we evaluate the accuracy of the
frequency-domain multi-distance (FD-MD) method to retrieve brain optical properties from neonate to adult.
Realistic measurements are simulated using a 3D Monte Carlo modeling of light propagation. Height different
ages were investigated: a term newborn of 38 weeks gestational age, two infants of 6 and 12 months of age,
a toddler of 2 year (yr.) old, two children of 5 and 10 years of age, a teenager of 14 yr. old, and an adult.
Measurements are generated at multiple distances on the right parietal area of head models and fitted to a
homogeneous FD-MD model to estimate the brain optical properties. In the newborn, infants, toddler and 5 yr.
old child models, the error was dominated by the head curvature, while the superficial layer in the 10 yr. old
child, teenager and adult heads. The influence of the CSF is also evaluated. In this case, absorption coefficients
suffer from an additional error. In all cases, measurements at 5 mm provided worse estimation because of the
diffusion approximation.
Biophysical models of hemodynamics provide a tool for quantitative multimodal brain imaging by allowing a deeper
understanding of the interplay between neural activity and blood oxygenation, volume and flow responses to stimuli.
Multicompartment dynamical models that describe the dynamics and interactions of the vascular and metabolic
components of evoked hemodynamic responses have been developed in the literature. In this work, multimodal data
using near-infrared spectroscopy (NIRS) and diffuse correlation flowmetry (DCF) is used to estimate total baseline
hemoglobin concentration (HBT0) in 7 adult subjects. A validation of the model estimate and investigation of the partial
volume effect is done by comparing with time-resolved spectroscopy (TRS) measures of absolute HBT0. Simultaneous
NIRS and DCF measurements during hypercapnia are then performed, but are found to be hardly reproducible. The
results raise questions about the feasibility of an all-optical model-based estimation of individual vascular properties.
KEYWORDS: Tissues, Absorption, Functional magnetic resonance imaging, Magnetic resonance imaging, Brain, Diffusion, Photons, Signal detection, 3D modeling, Monte Carlo methods
Diffuse optical imaging (DOI) is a relatively new functional imaging modality offering the possibility to record changes
in hemoglobin concentrations. It is based on the propagation of near-infrared light through biological tissues. By
measuring the optical absorption of the blood in the cortex, DOI enables the estimation of changes of deoxy-hemoglobin
(HbR) and oxy-hemoglobin (HbO2) concentrations. It thus provides indirect information on neuronal activity.
Drawbacks of optical imaging are its lack of quantification abilities as well as poor spatial resolution. Although not
much can be done concerning the second issue, diffusion being the limiting factor, one can aim at more quantitative data
by the use of extra information. As an example, the determination of baseline concentrations done by fitting a temporal
or frequency curve to recover background concentrations is not expected to be accurate due to the heterogeneity of the
underlying tissues. The vascular architecture, unknown when doing DOI alone, also plays a significant role in the signal
detected. Partial volume effects due to an optode pair overlapping a large vein will lead to confounding data and create
difficulties in analyzing the neuronal activation. Here we show that fusion with MRI, but done outside the scanner, may
help solving some of these issues.
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