Algorithmic depth compensation improves transverse resolution and quantification in functional diffuse optical tomography

摘要

One of the major challenges in diffuse optical tomography (DOT) is attributed to the severe decay of sensitivity along depth. In conventional reconstruction method using regularized inversion, it yields significant depth distortion in the reconstructed image as a cortical activation is always projected into the skull. Recently we developed a depth compensation algorithm (DCA) to minimize the depth localization error in DOT, which introduces a depth-variant weight matrix to counterbalance the severe sensitivity decay of A-matrix. The DCA algorithm has been previously validated in both laboratory phantom experiments and an in vivo human study. In this study, we first present a comprehensive analysis on how DCA alters the depth localization and spatial resolution in DOT. It reveals that DCA greatly improves the transverse resolution in sub-cortical region. Second, we present a quantification approach for DCA. By forming a spatial prior directly from the reconstructed image, this approach greatly improves the quantification accuracy in DOT.