Developing Data Quality Assessment Measures for High-Density Diffuse Optical Tomography

High-density diffuse optical tomography (HD-DOT) is a relatively new neuroimaging technique that detects the changes in the hemoglobin concentrations following neuronal activity through the measurement of near-infrared light intensities. Thus, it has the potential to be a surrogate for functional MRI (fMRI) as a more naturalistic, portable, and cost-effective neuroimaging system. Similar to other neuroimaging modalities, head motion is the most common source of artifact in HD-DOT data that results in spurious effects in the functional brain images. Unlike other neuroimaging modalities, data quality assessment methods are still underdeveloped for HD-DOT. Therefore, developing robust motion detection and motion removal methods in its data processing pipeline is a crucial step for making HD-DOT a reliable neuroimaging modality.

In this graduate seminar, I will talk about the HD-DOT data processing pipeline and the data quality assessment measures that I have been working on in the context of HD-DOT’s clinical applications. These methods enable direct comparisons of HD-DOT images with those of fMRI and equip this modality to be used as a surrogate for fMRI when fMRI is contraindicated or not feasible.