The recent development of hybrid imaging scanners that integrate fluorescence molecular tomography (FMT) and x-ray computed tomography (XCT) allows the utilization of x-ray information as image priors for improving optical tomography reconstruction. To fully capitalize on this capacity, we consider a framework for the automatic and fast detection of different anatomic structures in murine XCT images. To accurately differentiate between different structures such as bone, lung, and heart, a combination of image processing steps including thresholding, seed growing, and signal detection are found to offer optimal segmentation performance. The algorithm and its utilization in an inverse FMT scheme that uses priors is demonstrated on mouse images.
A novel hybrid imaging system for simultaneous X-ray and Fluorescence Tomography
is presented, capitalizing on 360°-projection free-space fluorescence tomography. The system is
implemented within a commercial micro-CT scanner allowing reconstructions with a resolution of
95μm. Acquired data sets are intrinsically coregistered in the same coordinate system and can be
used to correctly localize reconstructed fluorescence distributions with morphological features.
More importantly, the micro-CT data, automatically segmented into different organ and tissue
segments can be used to guide the fluorescence reconstruction algorithm and reduce the ill coditioning
of the inverse problem. We showcase the use of the system and the improvements in
image quality for lesions in brain and lung.