Model-Based Vasculature Extraction From Optical Fluorescence Cryomicrotome Images

The aim of this study was to develop a novel method to reconstruct 3-D coronary vasculature from cryomicrotome images, comprised of two distinct sets of data-fluorescent microsphere beads and coronary vasculature. Fluorescent beads and cast injected into the vasculature were separately imaged with different filter settings to obtain the microsphere and vascular data, respectively. To extract the vascular anatomy, light scattering in the tissue was modelled using a point spread function (PSF). The PSF was parametrized by optical tissue excitation and emission attenuation coefficients, which were estimated by fitting simulated images of microspheres convolved with the PSF model to the experimental microsphere images. These parameters were then applied within a new model-based method for vessel radius estimation. Current state-of-the-art radii estimation methods and the proposed model-based method were applied on vessel phantoms. In this validation study, the full-width half-maximum method of radii estimation, when performed on the raw data without correcting for the optical blurring, resulted in 42.9% error on average for the 170 μm vessel. In comparison, the model-based method resulted in 0.6% error on average for the same phantom. Whole-organ porcine coronary vasculature was automatically reconstructed with the new model-based vascular extraction method.