In this paper a minimax methodology is presented for combining information from two imaging modalities having different intrinsic spatial resolutions. The focus application is emission computed tomography (ECT), a low-resolution modality for reconstruction of radionuclide tracer density, when supplemented by high-resolution anatomical boundary information extracted from a magnetic resonance image (MRI) of the same imaging volume.
The MRI boundary within the two-dimensional (2-D) slice of interest is parameterized by a closed planar curve. The Cramer–Rao (CR) lower bound is used to analyze estimation errors for different boundary shapes. Under a spatially inhomogeneous Gibbs field model for the tracer density a representation for the minimax MRI-enhanced tracer density estimator is obtained. It is shown that the estimator is asymptotically equivalent to a penalized maximum likelihood (PML) estimator with resolution selective Gibbs penalty.
Quantitative comparisons are presented using the iterative space alternating generalized expectation maximization (SAGE-EM) algorithm to implement the PML estimator with and without minimax weight averaging.