Doppler OCT clutter rejection using variance minimization and offset extrapolation

Near-lifespan longitudinal tracking of brain microvascular morphology, topology, and flow in male mice

In age-related neurodegenerative diseases, pathology often develops slowly across the lifespan. As one example, in diseases such as Alzheimer's, vascular decline is believed to onset decades ahead of symptomology. However, challenges inherent in current microscopic methods make longitudinal tracking of such vascular decline difficult. Here, we describe a suite of methods for measuring brain vascular dynamics and anatomy in mice for over seven months in the same field of view. This approach is enabled by advances in optical coherence tomography (OCT) and image processing algorithms including deep learning. These integrated methods enabled us to simultaneously monitor distinct vascular properties spanning morphology, topology, and function of the microvasculature across all scales: large pial vessels, penetrating cortical vessels, and capillaries. We have demonstrated this technical capability in wild-type and 3xTg male mice. The capability will allow comprehensive and longitudinal study of a broad range of progressive vascular diseases, and normal aging, in key model systems.

Investigating the correlation between early vascular alterations and cognitive impairment in Alzheimer's disease in mice with SD-OCT

Vascular alterations have recently gained some attention with their strong association with Alzheimer's disease (AD). We conducted a label-free in vivo optical coherence tomography (OCT) longitudinal imaging using an AD mouse model. We achieved the tracking of the same individual vessels over time and conducted an in-depth analysis of temporal dynamics in vasculature and vasodynamics using OCT angiography and Doppler-OCT. The AD group showed an exponential decay in both vessel diameter and blood flow change with the critical timepoint before 20 weeks of age, which precedes cognitive decline observed at 40 weeks of age. Interestingly, for the AD group, the diameter change showed the dominance in arterioles over venules, but no such influence was found in blood flow change. Conversely, three mice groups with early vasodilatory intervention did not show any significant change in both vascular integrity and cognitive function compared to the wild-type group. We found early vascular alterations and confirmed their correlation with cognitive impairment in AD.

Validation of red blood cell flux and velocity estimations based on optical coherence tomography intensity fluctuations

We present a validation of red blood cell flux and speed measurements based on the passage of erythrocytes through the OCT’s focal volume. We compare the performance of the so-called RBC-passage OCT technique to co-localized and simultaneously acquired two-photon excitation fluorescence microscopy (TPEF) measurements. Using concurrent multi-modal imaging, we show that fluctuations in the OCT signal display highly similar features to TPEF time traces. Furthermore, we demonstrate an overall difference in RBC flux and speed of 2.5 ± 3.27 RBC/s and 0.12 ± 0.67 mm/s (mean ± S.D.), compared to TPEF. The analysis also revealed that the OCT RBC flux estimation is most accurate between 20 RBC/s to 60 RBC/s, and is severely underestimated at fluxes beyond 80 RBC/s. Lastly, our analysis shows that the RBC speed estimations increase in accuracy as the speed decreases, reaching a difference of 0.16 ± 0.25 mm/s within the 0–0.5 mm/s speed range.

Optical coherence tomography velocimetry based on decorrelation estimation of phasor pair ratios (DEPPAIR)

Quantitative velocity estimations in optical coherence tomography requires the estimation of the axial and lateral flow components. Optical coherence tomography measures the depth resolved complex field reflected from a sample. While the axial velocity component can be determined from the Doppler shift or phase shift between a pair of consecutive measurements at the same location, the estimation of the lateral component for in vivo applications is still challenging. One approach to determine lateral velocity is multiple simultaneous measurements at different angles. In another approach the lateral component can be retrieved through repeated measurements at (nearly) the same location by an analysis of the decorrelation over time. In this paper we follow the latter approach. We describe a model for the complex field changes between consecutive measurements and use it to predict the uncertainties for amplitude-based, phase-based and complex algorithms. The uncertainty of the flow estimations follows from a statistical analysis and is determined by the number of available measurements and the applied analysis method. The model is verified in phantom measurements and the dynamic range of velocity estimations is investigated. We demonstrate that phase-based and complex (phasor) based lateral flow estimation methods are superior to amplitude-based algorithms.