Flexible-circuit-based 3-D aware modular optical brain imaging system for high-density measurements in natural settings.

Significance: Functional near-infrared spectroscopy (fNIRS) presents an opportunity to study human brains in everyday activities and environments. However, achieving robust measurements under such dynamic condition remains a significant challenge. Aim: The modular optical brain imaging (MOBI) system is designed to enhance optode-to-scalp coupling and provide real-time probe 3-D shape estimation to improve the use of fNIRS in everyday conditions. Approach: The 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 3-D positions in real-time to enable advanced tomographic data analysis and motion tracking. Results: Optical characterization of the MOBI detector reports a noise equivalence power (NEP) of 8.9 and 7.3 pW / H z at 735 nm and 850 nm, respectively, with a dynamic range of 88 dB. The 3-D optode shape acquisition yields an average error of 4.2 mm across 25 optodes in a phantom test compared to positions acquired from a digitizer. Results for initial in vivo validations, including a cuff occlusion and a finger-tapping test, are also provided. Conclusions: To 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 3-D optode position acquisition, combined with lightweight modules (18 g/module) and ergonomic designs, would greatly aid emerging explorations of brain function in naturalistic settings.

Compact breast shape acquisition system for improving diffuse optical tomography image reconstructions.

Diffuse optical tomography (DOT) has been investigated for diagnosing malignant breast lesions, but its accuracy relies on model-based image reconstructions, which in turn depends on the accuracy of breast shape acquisition. In this work, we have developed a dual-camera structured light imaging (SLI) breast shape acquisition system tailored for a mammography-like compression setting. Illumination pattern intensity is dynamically adjusted to account for skin tone differences, while thickness-informed pattern masking reduces artifacts due to specular reflections. This compact system is affixed to a rigid mount that can be installed into existing mammography or parallel-plate DOT systems without the need for camera-projector re-calibration. Our SLI system produces sub-millimeter resolution with a mean surface error of 0.26 mm. This breast shape acquisition system results in more accurate surface recovery, with an average 1.6-fold reduction in surface estimation errors over a reference method via contour extrusion. Such improvement translates to 25% to 50% reduction in mean squared error in the recovered absorption coefficient for a series of simulated tumors 1-2 cm below the skin.

MOCA: a systematic toolbox for designing and assessing modular functional near-infrared brain imaging probes.

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.