SignificanceFunctional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic conditions remains a significant challenge.AimThe modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide a real-time probe three-dimensional (3D) shape estimation to improve the use of fNIRS in everyday conditions.ApproachThe MOBI system utilizes a bendable and lightweight modular circuit-board design to enhance probe conformity to head surfaces and comfort for long-term wearability. Combined with automatic module connection recognition, the built-in orientation sensors on each module can be used to estimate optode 3D positions in real time to enable advanced tomographic data analysis and motion tracking.ResultsOptical characterization of the MOBI detector reports a noise equivalence power of 8.9 and 7.3 pW/Hz at 735 and 850 nm, respectively, with a dynamic range of 88 dB. The 3D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared with positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided.ConclusionsTo the best of our knowledge, the MOBI system is the first modular fNIRS system featuring fully flexible circuit boards. The self-organizing module sensor network and automatic 3D optode position acquisition, combined with lightweight modules (18 g/module) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.
We report one of the first studies on direct 3-D printing of heterogeneous optical phantoms with programmable absorption and scattering properties using a multi-color mixing extruder. This method dynamically mixes off-the-shelf gray, white, and translucent filaments to achieve arbitrary target absorption and scattering coefficients. We use a spatial frequency domain imaging system to characterize and validate the printed properties and verify that they follow our hypothesized linear-mixing models. A complex phantom with five inclusions with distinct optical properties was produced and the measured properties compared to their predicted values showed an error between 12%-15%.
Significance: The expansion of functional near-infrared spectroscopy (fNIRS) systems toward broader utilities has led to the emergence of modular fNIRS systems composed of repeating optical source/detector modules. Compared to conventional fNIRS systems, modular fNIRS systems are more compact and flexible, making wearable and long-term monitoring possible. However, the large number of design parameters makes understanding their impact on a probe’s performance a daunting task.
Aim: We aim to create a systematic software platform to facilitate the design, characterization, and comparison of modular fNIRS probes.
Approach: Our software—modular optode configuration analyzer (MOCA)—implements semi-automatic algorithms that assist in tessellating user-specified regions-of-interest, in interconnecting modules of various shapes, and in quantitatively comparing probe performance using metrics, such as spatial channel distributions and average brain sensitivity of the resulting probes. There is also support for limited parameter sweeping capabilities.
Results: Through several examples, we show that users can use MOCA to design and optimize modular fNIRS probes, study trade-offs between several module shapes, improve brain sensitivity in probes via module re-orientation, and enhance probe performance via adjusting module spatial layouts.
Conclusion: Despite its simplicity, our modular probe design platform offers a framework to describe and quantitatively assess probes made by modules, opening a new door for the growing fNIRS user community to approach the challenging problem of module- and probe-parameter selection and fine-tuning.
The mobile health field has given rise to a surge of point-of-care diagnostic attachments for mobile phones. These attachments, however, are limited in adoption in low-resource settings due to initial acquisition and subsequent maintenance cost challenges. Point-of-care devices that require no or minimum attachment can make a great impact to the accessibility of such devices in resource-poor regions. In this abstract, we report a simulation study to demonstrate the feasibility of using an ultra-low-cost color-paper filter and a mobile phone to perform broadband pulse oximetry. We run a series of GPU-based Monte Carlo simulations using a previously segmented 7T MRI scan of a finger 3D model. We sweep the optical properties of the finger tissues between the wavelengh band of 400-800 nm with a 1 nm increment, with intensity based on the measured spectrum of an iPhone 8’s LED. We also measured the transmission spectra from paper filters of various colors, which we used to further alter the light source spectrum. Using a discretized photoplethysmogram (PPG) signal, we simulate a 60 bpm oscillation optical measurements due to an up to 15% volume changes of the finger arterioles. Simulations were repeated for various peripheral blood oxygen levels (SpO2). Finally, we estimate the SpO2 using the simulated PPG signals using the Ratio of Ratios (RR) method. We evaluate the performance of different color paper filters by comparing 1) total optical signal intensity, 2) maximum magnitude of the RR signal variations and 3) the correlation of the computed and assumed SpO2 values. We found that the purple-colored filter produced the highest RR signal variations and the cyan-colored paper resulted in the largest SpO2 changes in the tested range.
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