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

• URL: http://arxiv.org/abs/2506.23466v1
• Date: Mon, 30 Jun 2025 02:16:38 GMT
• Title: FD-DiT: Frequency Domain-Directed Diffusion Transformer for Low-Dose CT Reconstruction
• Authors: Qiqing Liu, Guoquan Wei, Zekun Zhou, Yiyang Wen, Liu Shi, Qiegen Liu,
• Abstract summary: Low-dose computed tomography (LDCT) reduces radiation exposure but suffers from image artifacts and loss of detail due to quantum and electronic noise.<n>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.<n>A hybrid denoising network is then utilized to optimize the overall data reconstruction process.<n> 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.
• Score: 3.980622332603746
• License: http://arxiv.org/licenses/nonexclusive-distrib/1.0/
• Abstract: 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.

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