Methods used in optical tomography have thus far proven to produce images of complex target media (e.g., tissue) having, at best, relatively modest spatial resolution. This presents a challenge in differentiating artifact from true features. Further complicating such efforts is the expectation that the optical properties of tissue for any individual are largely unknown and are likely to be quite variable due to the occurrence of natural vascular rhythms whose amplitudes are sensitive to a host of autonomic stimuli that are easily induced. We recognize, however, that rather than frustrating efforts to validate the accuracy of image features, the time–varying properties of the vasculature can be exploited to aid in such efforts. This is possible owing to the known structure–dependent frequency response of the vasculature and to the fact that hemoglobin is a principal contrast feature of the vasculature at NIR wavelengths. To accomplish this it is necessary to generate a time series of image data. In this report we have tested the hypothesis that through analysis of this data, independent contrast features can be derived that serve to validate, at least qualitatively, the accuracy of imaging data, in effect establishing a self–referencing scheme. A significant finding is the observation that analysis of such data can produce high–contrast images that reveal features that are mainly obscured in individual image frames or in time–averaged image data. Given the central role of hemoglobin in tissue function, this finding suggests that a wealth of new features associated with vascular dynamics can be identified from the analysis of time–series image data.
Keywords: Dynamic optical tomography, imaging, physiology, correlation and spectral analysis.