标题： FD-DiT：频域引导的扩散变压器用于低剂量CT重建 显示英文标题

标题： FD-DiT: Frequency Domain-Directed Diffusion Transformer for Low-Dose CT Reconstruction

摘要： 低剂量计算机断层扫描（LDCT）减少了辐射暴露，但由于量子和电子噪声导致图像伪影和细节丢失，可能影响诊断准确性。 将Transformer与扩散模型结合已被证明是图像生成的一种有前途的方法。 然而，现有方法在保留细粒度图像细节方面存在局限性。 为了解决这个问题，提出了频域引导的扩散Transformer（FD-DiT）用于LDCT重建。 FD-DiT的核心是一种扩散策略，逐步引入噪声，直到分布统计上与LDCT数据一致，然后进行去噪处理。 此外，我们采用频域解耦技术，将噪声主要集中在高频域，从而有助于有效捕捉关键解剖结构和细小细节。 然后使用混合去噪网络优化整体数据重建过程。 为了增强识别高频噪声的能力，我们引入滑动稀疏局部注意力，利用浅层信息的稀疏性和局部性，并通过跳跃连接传播它们以改善特征表示。 最后，我们提出了一种可学习的动态融合策略以实现最佳组件集成。 实验结果表明，在相同剂量水平下，由FD-DiT重建的LDCT图像在噪声和伪影抑制方面优于最先进方法。 显示英文摘要

摘要： Low-dose computed tomography (LDCT) reduces radiation exposure but suffers from image artifacts and loss of detail due to quantum and electronic noise, potentially impacting diagnostic accuracy. Transformer combined with diffusion models has been a promising approach for image generation. Nevertheless, existing methods exhibit limitations in preserving finegrained image details. To address this issue, frequency domain-directed diffusion transformer (FD-DiT) is proposed for LDCT reconstruction. FD-DiT centers on a diffusion strategy that progressively introduces noise until the distribution statistically aligns with that of LDCT data, followed by denoising processing. Furthermore, we employ a frequency decoupling technique to concentrate noise primarily in high-frequency domain, thereby facilitating effective capture of essential anatomical structures and fine details. A hybrid denoising network is then utilized to optimize the overall data reconstruction process. To enhance the capability in recognizing high-frequency noise, we incorporate sliding sparse local attention to leverage the sparsity and locality of shallow-layer information, propagating them via skip connections for improving feature representation. Finally, we propose a learnable dynamic fusion strategy for optimal component integration. Experimental results demonstrate that at identical dose levels, LDCT images reconstructed by FD-DiT exhibit superior noise and artifact suppression compared to state-of-the-art methods.