MDRANet: A multiscale dense residual attention network for magnetic resonance and nuclear medicine image fusion

Abstract

Magnetic resonance and nuclear medicine images are the two categories of multimodal medical images. Magnetic resonance images reveal physiological anatomical information of patients, and nuclear medicine images accurately show tissue lesion information. Through medical image fusion algorithms, these fusion images containing both tissue lesion information and physiological anatomical information are obtained to provide sufficient information for clinical medical technologies. However, most existing fusion algorithms are based on mathematical transform domains, and these fusion results have the weaknesses of blurred edges, color distortion and detail loss. To address these problems, a multiscale dense residual attention network (MDRANet) is proposed and applied to magnetic resonance and nuclear medicine image fusion. MDRANet combines multiscale dense network and multiscale residual attention network to extract and enhance deep features. Moreover, four different loss functions are used to optimize MDRANet and improve the fusion quality. The experimental results show that the fusion results of our proposed algorithm have richer details and better objective metrics compared with the reference algorithms.