<![CDATA[Medical image fusion (MIF) is a key technique in medical imaging, which combines complementary information from different imaging modalities, thereby improving the accuracy of diagnosis, particularly for lesion detection and treatment planning. Deep learning has significantly advanced this area, with the development of generative models and transformers leading to improvements in fidelity and accuracy, although the study of the influence of attention mechanisms on these models remains limited to a single type or a single architectural placement. This paper offers an analytical examination of the architectures of Pix2Pix with three attention mechanisms (spatial attention, channel attention (Squeeze-and-Excitation), and self-attention), where they are tested in three different placement strategies (encoder-only, decoder-only, and encoder-decoder), using the BraTS2020 dataset, with training supervised by a pseudo-ground-truth derived from arithmetic averaging. We fused six MRI modality pairs (FLAIR-T1, FLAIR-T1ce, FLAIR-T2, T1-T1ce, T1-T2, T1ce-T2), evaluating them using different metrics, including SSIM, PSNR, NMI, Entropy, and QAB/F. Results show that, in all cases, attention integration can significantly improve the quality of fusion over baseline methods, including cGAN and standard Pix2Pix. Spatial attention with encoder-decoder placement shows the best results, with SSIM values up to 0.91 and PSNR superior to 25 dB for the heterogeneous modality pair FLAIR-T1. Similarly, channel and self-attention demonstrate their effectiveness, especially with encoder-decoder placements. Based on these findings, attention-based fusion systems can be practically designed in a way that enhances MMIF, and the importance of designing attention in accordance with the nature of the modality is emphasized for optimal fusion performance. Our study demonstrates its effectiveness and may serve as a foundation for future research.]]>
