<title>Multimodality fusion of physiological images using regularization theory and deformable models</title>

Abstract

A common medical diagnostic problem is the determination of physiological function. Modern medical imaging modalities provide the physician with a wealth of differing yet complementary information. Modalities traditionally offer either mappings of anatomical structure (MRI and CT) or function (SPECT, PET), but seldom both.MRI and CT are somewhat dissimilar but complementary imaging technologies. While CT provides excellent information regarding internal bony structures, MRI has proven to be superior in the production of high contrast images of soft tissue. The integration of these two modalities will ameliorate solutions to problems which require highly accurate mappings of anatomical features. PET imagery presents an accurate view of physiological function but little anatomical information. The ability to integrate quantitative information from these complementary modalities will result in improved medical analysis and subsequent improvements in patient care.We consider the problem of multimodality data fusion as an extension of regularization theory. Directionally controlled continuity stabilizers are utilized in the reconstruction process.Employing multiple modalities in the visual reconstruction process improves reconstruction error, improves the detection of discontinuities and reduces the false detection of discontinuities in all of the data sets. Consequently, the diagnostic utility of the reconstructed imagery is significantly improved. The fusion method presented in this paper can deal with surface discontinuities of an arbitrary order. This marks an improvement from earlier Markov Random Field fusion techniques which were limited to the construction of surfaces with first order discontinuities. The fusion methodology presented can handle with data sets belonging to different visual cues.