New techniques for data fusion in multimodal FMT-CT imaging.

We examine approaches to the incorporation of anatomic structural information into the inverse problem of fluorescence molecular tomography (FMT). Using an appropriate relationship between anatomic and reconstruction image resolution, we build an inverse problem parameterized along the anatomical segmentation. These values serve as the basis for two new regularization techniques. The first regularizes individual voxels in proportion to the importance of the underlying segments in reducing the residual error. The second is based on a well known statistical interpretation of Tikhonov-type regularization in which the statistical prior is defined implicitly as the solution to a PDE whose structure is based on the anatomical segmentation. Results are shown using both techniques for a simulated experiment within the chest cavity of a mouse