Abstract
High density diffuse optical tomography (HD-DOT) is a functional neuroimaging method that uses multiple overlapping and multi-distance functional near-infrared spectroscopy measurements in a dense grid array. Herein, we investigate multiple frequency and matrix scaling strategies to improve frequency domain HD-DOT methods and use simulations of point spread functions in anatomical models to assess the improvement attainable over standard methods. We observe a small improvement in image quality metrics by adding multiple modulation frequencies, and a significant improvement after column scaling sensitivity matrices. These methods may advance image quality of HD-DOT beyond current limitations.
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