Stand-alone segmentation of blood flow pulsatility measured with diffuse correlation spectroscopy.

We present a stand-alone blood flow index (BFI) pulse segmentation method for diffuse correlation spectroscopy that uses a wavelet-based representation of the BFI signal at the cardiac frequency in place of an exogenous physiological reference. We use this wavelet-based segmentation method to quantify BFI waveform morphology in a cohort of 30 healthy adults. We demonstrate that the waveform morphology features obtained with the wavelet approach strongly agree with those obtained using an exogenous blood pressure reference signal.

Normative cerebral microvascular blood flow waveform morphology assessed with diffuse correlation spectroscopy.

Microvascular cerebral blood flow exhibits pulsatility at the cardiac frequency that carries valuable information about cerebrovascular health. This study used diffuse correlation spectroscopy to quantify normative features of these waveforms in a cohort of thirty healthy adults. We demonstrate they are sensitive to changes in vascular tone, as indicated by pronounced morphological changes with hypercapnia.

Morphometric Analysis of Retinal Ganglion Cell Axons in Normal and Glaucomatous Brown Norway Rats Optic Nerves.

Purpose: A reference atlas of optic nerve (ON) retinal ganglion cell (RGC) axons could facilitate studies of neuro-ophthalmic diseases by detecting subtle RGC axonal changes. Here we construct an RGC axonal atlas for normotensive eyes in Brown Norway rats, widely used in glaucoma research, and also develop/evaluate several novel metrics of axonal damage in hypertensive eyes.

Methods: Light micrographs of entire ON cross-sections from hypertensive and normotensive eyes were processed through a deep learning-based algorithm, AxoNet2.

AxoNet 2.0: A Deep Learning-Based Tool for Morphometric Analysis of Retinal Ganglion Cell Axons.

Purpose: Assessment of glaucomatous damage in animal models is facilitated by rapid and accurate quantification of retinal ganglion cell (RGC) axonal loss and morphologic change. However, manual assessment is extremely time- and labor-intensive. Here, we developed AxoNet 2.