Open AccessOptimization of probe geometry for diffuse optical brain imaging based on measurement density and distribution
Author(s) -
Fenghua Tian,
George Alexandrakis,
Hanli Liu
Publication year - 2009
Publication title -
applied optics
Language(s) - English
Resource type - Journals
ISSN - 0003-6935
DOI - 10.1364/ao.48.002496
Subject(s) - optode , optics , image resolution , diffuse optical imaging , imaging phantom , image quality , physics , point spread function , pixel , iterative reconstruction , noise (video) , resolution (logic) , materials science , tomography , artificial intelligence , computer science , image (mathematics) , fluorescence
Optode geometry plays an important role in achieving both good spatial resolution and spatial uniformity of detection in diffuse-optical-imaging-based brain activation studies. The quality of reconstructed images for six optode geometries were studied and compared using a laboratory tissue phantom model that contained an embedded object at two separate locations. The number of overlapping measurements per pixel (i.e., the measurement density) and their spatial distributions were quantified for all six geometries and were correlated with the quality of the resulting reconstructed images. The latter were expressed by the area ratio (AR) and contrast-to-noise ratio (CNR) between reconstructed and actual objects. Our results revealed clearly that AR and CNR depended on the measurement density asymptotically, having an optimal point for measurement density beyond which more overlapping measurements would not significantly improve the quality of reconstructed images. Optimization of probe geometry based on our method demonstrated that a practical compromise can be attained between DOI spatial resolution and measurement density.