Medical　image　fusion　technology　can　improve　the　precision　of　clinical　diagnosis　by　fusing　medical　information　from　different　modalities.　However,　the　quality　of　fusion　is　restricted　due　to　the　particular　imaging　mechanism.　This　paper　proposes　a　quality-enhanced　medical　image　fusion　algorithm　based　on　a　generative　adversarial　network　for　the　lossless　fusion　of　MRI　and　PET　images.　It　consists　of　a　lightweight　image　enhancement　depth　network　to　make　the　quality　of　the　fused　image　suit　human　vision　perceptual　system　better　and　a　generative　adversarial　network　to　enhance　texture　details　and　edge　information　further.　Our　model　is　unsupervised　and　does　not　require　paired　fused　images　for　training.　The　test　results　show　that　our　algorithm　performs　better　in　both　subjective　visual　effects　and　objective　evaluation　metrics.　©　2022　IEEE.