A Pilot Study: PET/CT Cross-Modal-Based Multi-Head Fusion Attention Generative Adversarial Network (MHFA-GAN) for PET Image Super-Resolution.

Positron emission tomography (PET) is a valuable imaging technique for assessing cellular metabolic activity and is widely used for early disease detection and diagnosis. While high-resolution (HR) PET images offer richer diagnostic information, achieving such quality often requires expensive equipment and time-intensive reconstruction processes. To address these challenges without hardware upgrades, this paper introduces a novel multi-head fusion attention generative adversarial network (MHFA-GAN), designed to enhance the spatial resolution and reconstruction efficiency of PET images through super-resolution (SR) techniques. The MHFA-GAN employs a multi-head mixed convolution mechanism to extract complementary features from PET and computed tomography (CT) images. An enhanced multi-head fusion attention module (EMHFA) is incorporated to adaptively adjust feature weights and align cross-modal information, effectively minimizing the impact of sub-pixel displacement while integrating metabolic and anatomical details. During the reconstruction phase, a global residual block (GRB) is utilized to balance local high-frequency details with global contextual information. Experiments conducted on small animal (SA), phantom, capillary, and