Abstract
Fluorescence diffuse optical tomography is an emerging technology for molecular imaging with recent technological advances in biomarkers and photonics. The introduction of noncontact imaging methods enables very large-scale data acquisition that is orders of magnitude larger than that from earlier systems. In this study, the effects of sampling strategy on image quality were investigated using an imaging phantom mimicking small animals and further analyzed using singular value analysis (SVA). The sampling strategy was represented in terms of a number of key acquisition parameters, namely the numbers of sources, detectors, and imaging angles. A number of metrics were defined to quantitatively evaluate image quality. The effects of acquisition parameters on image quality were subsequently studied by varying each of the parameters within a reasonable range while maintaining the other parameters constant, a method analogue to partial derivative in mathematical analysis. It was found that image quality improves at a much slower rate if the acquisition parameters are above certain critical values (approximately 5 sources, approximately 15 detectors, and approximately 20 angles for our system). These critical values remain virtually the same even if other acquisition parameters are doubled. It was also found that increasing different acquisition parameters improves image quality with different efficiencies in terms of the number of measurements: for a system characterized by a smaller threshold in SVA (less than 10(-5) in our study), the number of sources is the most efficient, followed by the number of detectors and subsequently the number of imaging angles. However, for systems characterized by a larger threshold, the numbers of sources and angles are equally more efficient than the number of detectors.