This PDF file contains the front matter associated with SPIE Proceedings Volume 12841, including the Title Page, Copyright information, Table of Contents, and Conference Committee information.

Prenatal alcohol exposure is a well-known cause of preventable birth defects, resulting in a decrease in cerebrovascular blood flow, which may cause major neurodevelopmental and morphological alterations in the developing fetus. In this study, we assess the acute and persistent effects of alcohol exposure on fetal brain vasculature in utero. We also analyzed the effects of different alcohol dosages (1.5 g/kg, 3.0 g/kg, and 4.5 g/kg) on fetal brain vasculature. To assess the effects of persistent alcohol exposure, we administered the same dose of alcohol at multiple gestational days (GD12.5, 13.5, and 14.5). To assess the acute effects, we administered the alcohol only at GD14.5. We utilize correlation mapping optical coherence angiography to image changes in fetal brain vasculature caused by exposure to ethanol at each dosage. Results show significant vasoconstriction of the main blood vessel imaged, which is located at the terminal anterior and middle cerebral arteries, irrigating the dorsolateral surface of the embryonic brain, for all three administered dosages. The difference in the primary blood vessel diameter before and after the final ethanol exposure at GD14.5 shows that the greater the dose, the smaller the change in blood vessel diameter. This contrasts with the acute dosing effects, which show a greater change in the primary blood vessel diameter when administering the highest single ethanol dosage. Overall, the multiple dosing and acute effects of alcohol consumption demonstrated a decrease in blood flow in the fetal brain.

Congenital heart defects represent one in three congenital defects and are present in an estimated 1.8% of newborns worldwide. Mouse models provide an irreplaceable resource for studying mammalian development and early cardiogenesis. Early dynamics of circulation during heart development are understood to influence heart formation, but quantitative assessment of spatially and temporally resolved blood flow in the highly dynamic embryonic hearts remains challenging. Optical coherence tomography (OCT) uniquely provides the high speed, spatial resolution, and imaging depth necessary to study biomechanics early in heart development. Building off advancements in Doppler OCT and quantitative OCT angiography, we present dynamic, volumetric (4D) speed analysis of blood flow in the embryonic cardiovascular system. Our new flow tracking method is based on time-at-pixel measurements, blood cell size statistics, and the periodicity of the cardiac cycle. We characterize the effects of detection thresholds to account for variation in signal intensity, such as due to lower light penetration over tissue depth throughout the heart or when comparing between embryos. We incorporate segmentation methods using speckle variance between cycles to expand the analysis from manually defined blood vessels to dynamic regions of blood flow. With these advancements, we quantify blood flow speed within the embryonic mouse heart as it beats. The presented method will allow biomechanical studies of early blood flow in regulating mammalian heart development.

Hemodynamic models connect cerebral blood flow and oxygen metabolism with deoxy-hemoglobin and oxyhemoglobin measured by near-infrared spectroscopy (NIRS) to analyze cerebral hemodynamics. These models elucidate the relationship between physiological processes and NIRS signals, capturing changes in cerebral blood volume, flow, and oxygen metabolism. In our study, we explore microvasculature compartments and apply these models to NIRS data during pig cardiac arrest and cardiopulmonary resuscitation. Our goals were to validate the model and to understand the behavior of cerebral microvasculature and metabolism during cardiac arrest and resuscitation. By employing the inverse of the hemodynamic model, we measure a range of significant physiological parameters.

Speckle contrast optical spectroscopy (SCOS) is an emerging camera-based technique that can measure human cerebral blood flow (CBF) noninvasively with high signal-to-noise ratio (SNR). A noise correction procedure has previously been developed to improve SCOS measurement accuracy, which requires precise characterization of camera properties. Here, we provide guidance on choosing and characterizing a camera for SCOS, considering factors such as linearity, read noise, and gain. We then validate a noise-corrected SCOS measurement of flow changes in a liquid phantom against diffuse correlation spectroscopy (DCS).

The Monro-Kellie doctrine states that the sum of the contents of the intracranial cavity is constant, and consequently dynamics of blood and cerebrospinal fluid (CSF) volumes should be in an anti-correlation relationship. This phenomenon helped to explain many abnormalities in intracranial hypotension and CSF depletion. We aimed to validate the same phenomenon in mice during a blood pressure (BP) lowering test. Eight 2–3-month-old C57/Bl6N (Charles River) female mice were used in this study. We used both nicardipine hydrochloride and sodium nitroprusside (SNP) infusion into the femoral vein to lower the BP. A multi-wavelength NIRS (685, 830, and 980 nm) measuring hemoglobin and water concentrations, sampled at 800 Hz, was used. The fiber probes for the light source and detector were inserted into the ear canals and positioned towards the brain, giving a distance of approximately 1 cm. Following the Monro-Kellie doctrine, the blood volume, i.e., total hemoglobin (HbT), and CSF volume should be in an anti-correlation relationship. Our experiments showed that concentration changes of total hemoglobin (HbT) and water, are in anti-correlation with correlation coefficients of -0.991 ± 0.007.

Obtaining parameters that characterize cerebral fluid interactions in the human brain is of high interest particularly as regards studies of the brain clearance and in relation to neurodegeneration diseases (NDD). Furthermore, disturbances in sleep affecting brain clearance have been linked to NDDs like Alzheimer’s disease (AD). At present, polysomnography (PSG) is the methodological gold standard in sleep research being used in sleep labs. However, it does not provide direct information on cerebral fluid dynamics which may be an important parameter linked to brain clearance activity during sleep. We have developed functional near-infrared spectroscopy (fNIRS) based method for assessment of human cerebral fluid dynamics during sleep. It is optimized as a wearable sleep monitoring device enabling overnight sleep recordings at home without disturbing natural sleep. In this paper, we study spectral entropy (SE) of cerebral fluid dynamics during sleep study. Developed fNIRS technique measures, in addition to cerebral hemodynamics, cortical water concentration changes reflecting dynamics of the cerebrospinal fluid (CSF) volume in macroscale. Our preliminary results of overnight fNIRS sleep measurements from 10 adult subjects show that SE values fluctuate in cycle during the whole night sleep. It may indicate the transition among sleep stages.

Signal quality is crucial in any signal analysis. Typically, the reason for bad signal quality is inappropriate sensor placement which is also highly dependent on the measurement location. It is usually quite easy to get a good optical signal from finger, but not from the brain. This study aims to provide a real-time signal quality assessment method to help clinical personnel in placement of the fNIRS sensors on head to ensure good signal quality. Signal was segmented for each 10 seconds and a band-pass filter at 0.5-3 Hz was applied to isolate signal in cardiac band. Each segmented signal was subject to visual quality assessment to get bad, fair, and good labels. We used maximum to mean power ratio to generate signal quality index (SQI) score. Other methods included were skewness and kurtosis of the heart rate variability (HRV). Results showed that power ratio provides better consistency and separation among three different labels. Both skewness and kurtosis failed to separate fair and good segments. Using two threshold values, indices from power ration can be transformed into red (bad), yellow (fair), and green (good) alarm to help healthcare practitioners, who have no expertise to assess signal quality, to fix sensor placement to get good or acceptable signals.

Fundamental understanding of complex effects of wave propagation in turbid media requires accurate computational techniques to take into account the effects of multiple scattering of light. We present an open-source Monte Carlo algorithm designed for energy-efficient processors, surpassing existing solutions in both accuracy and performance. Our implementation optimizes photon transport simulations using Apple’s low-power, high-performance M-family chips. Additionally, we explore integrating Machine Learning (ML) techniques to efficiently create a forward solver for the Radiative Transport Equation, identifying top-performing ML models. Our open-source software package integrates ML, optimizing photon transport simulations and facilitating customization for specific applications. Extensive validation against common solvers in biomedical imaging demonstrates comparable accuracy with significantly reduced computational time and energy consumption. This approach maintains accuracy while drastically reducing computational time and energy consumption, offering a promising emerging concept/solution for simulating light propagation in turbid media.