Lightweight high-density diffuse optical tomography using sCMOS detection

The widespread adoption of optical neuroimaging has been restricted by the tradeoff between cap wearability and brain coverage [1]. Increased coverage requires more fibers and larger imaging consoles, however these changes drastically reduce the wearability of the imaging cap and the portability of the entire system. The size of the detection fibers, which is driven by signal-to-noise considerations, is the primary obstacle to fabricating more wearable and portable optical neuroimaging arrays. Here we report on a design that leverages the low-noise of scientific CMOS cameras, along with binning and noise reduction algorithms to use fibers with approximately 30x smaller cross-sectional area than current high-density diffuse optical tomography (HD-DOT) systems [2]. We have developed a Super-Pixel sCMOS Diffuse Optical Tomography (SP-DOT) system (Fig. 1a) that uses 200um diameter source and detector fibers, with a lightweight low-profile, wearable design. A super-pixel algorithm leverages pixel binning to provide dynamic range (DNR), Noise Equivalent Power (NEP), and cross- talk (CT) specifications comparable to previous HD-DOT [2]. We have demonstrated retinotopic mapping with a SP-DOT system (Fig. 1). The system has a high DNR (\u3e105), high frame rate (\u3e6Hz) and low NEP (\u3c 9fW/√Hz). Please click Additional Files below to see the full abstract

Mapping brain function at the bedside during acute stroke recovery using High-Density Diffuse Optical Tomography

Within 72 hours of stroke onset, High-Density Diffuse Optical Tomography can detect disruptions in functional connectivity patterns that significantly differ from healthy subjects (p&lt;1E-5) and that correlate with the NIH Stroke Scale (p&lt;3.3E-4).</p

Mapping brain function at the bedside during acute stroke recovery using High-Density Diffuse Optical Tomography

Within 72 hours of stroke onset, High-Density Diffuse Optical Tomography can detect disruptions in functional connectivity patterns that significantly differ from healthy subjects (p&lt;1E-5) and that correlate with the NIH Stroke Scale (p&lt;3.3E-4).</p