发表机构
State University of New York at Buffalo（纽约州立大学布法罗分校）
机构由 AI 辅助整理，请以论文原文为准。AI 中文总结
提出轻量级DAFNet，通过双路径编码器与自适应融合模块实现T1到T2 MRI合成，在降低计算成本的同时达到26.43 dB PSNR，平衡了保真度与效率。
AI 中文摘要
使用深度学习的跨模态MRI合成可以简化临床工作流程、补偿缺失的图像对比度并减少检查时间；然而，现有方法通常需要大量的计算资源，限制了其实际可及性。为解决这一局限，我们提出并研究了一个轻量级但有效的框架，称为双路径自适应融合网络（DAFNet），用于T1到T2图像合成。DAFNet基于cGAN（条件生成对抗网络）结构，其生成器采用双路径编码器，自适应地结合深度可分离卷积和扩张卷积，以捕获细粒度细节和更广泛的上下文特征。我们引入了一种新颖的双路径自适应融合模块（DAFM），通过通道级互补加权动态组合这些特征流，实现高效且自适应的特征集成。该融合机制进一步应用于最终的跳跃连接以增强重建保真度，而解码器采用标准的转置卷积架构。结合传统的条件GAN判别器，所提出的模型在保持低计算复杂度的同时提高了感知图像保真度。实验结果表明，与基线U-Net和传统cGAN模型相比，DAFNet实现了更优越的性能，峰值信噪比（PSNR）达到26.43 dB，同时显著减小了模型规模。这一结果表明DAFNet在合成保真度和计算效率之间提供了有效的平衡，使其成为在标准计算硬件上部署以及更广泛的临床和研究应用中MRI对比度合成的有前景的解决方案。
英文摘要
Cross-modality MRI synthesis using deep learning can streamline clinical workflows, compensate for missing image contrasts, and reduce examination time; however, existing methods often require substantial computational resources, limiting their practical accessibility. To address this limitation, we propose and investigate a lightweight yet effective framework, termed the Dual-path Adaptive Fusion Network (DAFNet), for T1-to-T2 images synthesis. DAFNet is based on cGAN (conditional generative adversarial networks) structure, and the generator employs a dual-path encoder that adaptively combines depth-wise separable convolutions and dilated convolutions to capture both fine-grained details and broader contextual features. A novel Dual-path Adaptive Fusion Module (DAFM) is introduced to dynamically combine these feature streams through channel-wise complementary weighting, enabling efficient and adaptive feature integration. This fusion mechanism is further applied to the final skip connection to enhance reconstruction fidelity, while the decoder adopts a standard transposed convolution architecture. Coupled with a conventional conditional GAN discriminator, the proposed model maintains low computational complexity while improving perceptual image fidelity. Experimental results demonstrate that DAFNet achieves superior performance compared with baseline U-Net and conventional cGAN models, reaching a peak signal-to-noise ratio (PSNR) of 26.43 dB while significantly reducing model size. This result indicates that DAFNet provides an effective balance between synthesis fidelity and computational efficiency, making it a promising solution for deployment on standard computing hardware and for broader clinical and research applications in MRI contrast synthesis.
Comments26 pages, 7 figures
