SENSOR FUSION IN IMAGE-RECONSTRUCTION

Various medical imaging modalities offer different but often complementary information. For example, x-ray computed tomography (CT) and magnetic resonance (MR) images provide structural information with high spatial resolution; while positron emission tomography (PET) and single-photon emission computed tomography (SPECT) give functional information with less desirable image quality. Integration of images from multiple modalities opens new avenues for developing innovative image reconstruction algorithms that can provide improved image quality. A Bayesian method for ECT image reconstruction has been developed to incorporate a priori information derived from the spatially-correlated CT and MR images. These anatomic maps, showing boundaries between regions that exhibit distinctly different characteristics, can be incorporated in the Bayesian method, thus improving the spatial resolution and noise properties. The correlated structural information can be used also as templates for deriving correction factors for the effect of photon attenuation, thus improving the quantitative accuracy and noise properties. Results from computer simulation studies show significant improvements in image quality.