FIND-Net通过频域与空域联合处理，有效减少金属伪影并保留解剖结构。
FIND-Net -- Fourier-Integrated Network with Dictionary Kernels for Metal Artifact Reduction
- 融合傅里叶卷积与可学习高斯滤波，双域协同处理伪影
- 合成数据上比顶尖方法降低3.07% MAE，提升0.90% PSNR
- 适合临床CT图像修复，尤其关注结构保真度的场景
金属植入物引起的金属伪影严重降低计算机断层扫描（CT）图像质量，影响诊断与治疗规划。尽管现有深度学习方法在金属伪影抑制（MAR）方面取得显著进展，但仍难以在抑制伪影的同时保持结构细节。为此，本文提出FIND-Net（基于字典核的傅里叶集成网络），一种结合频域与空域处理的新MAR框架。FIND-Net引入快速傅里叶卷积（FFC）层和可训练高斯滤波，将MAR视为跨空间与频率域的混合任务，增强全局上下文感知与频率选择性，有效抑制伪影并保留解剖结构。合成数据实验表明，FIND-Net相比当前最优方法，平均绝对误差（MAE）降低3.07%，结构相似性（SSIM）提升0.18%，峰值信噪比（PSNR）提高0.90%，且在不同伪影复杂度下均表现稳健。真实临床CT扫描评估进一步验证了其在最小化对正常组织干扰的同时有效抑制金属诱导畸变的能力。结果表明，FIND-Net具备提升MAR性能的潜力，提供更优的结构保真与临床适用性。代码已开源：https://github.com/Farid-Tasharofi/FIND-Net
原文摘要 · Abstract (English)
Metal artifacts, caused by high-density metallic implants in computed tomography (CT) imaging, severely degrade image quality, complicating diagnosis and treatment planning. While existing deep learning algorithms have achieved notable success in Metal Artifact Reduction (MAR), they often struggle to suppress artifacts while preserving structural details. To address this challenge, we propose FIND-Net (Fourier-Integrated Network with Dictionary Kernels), a novel MAR framework that integrates frequency and spatial domain processing to achieve superior artifact suppression and structural preservation. FIND-Net incorporates Fast Fourier Convolution (FFC) layers and trainable Gaussian filtering, treating MAR as a hybrid task operating in both spatial and frequency domains. This approach enhances global contextual understanding and frequency selectivity, effectively reducing artifacts while maintaining anatomical structures. Experiments on synthetic datasets show that FIND-Net achieves statistically significant improvements over state-of-the-art MAR methods, with a 3.07% MAE reduction, 0.18% SSIM increase, and 0.90% PSNR improvement, confirming robustness across varying artifact complexities. Furthermore, evaluations on real-world clinical CT scans confirm FIND-Net's ability to minimize modifications to clean anatomical regions while effectively suppressing metal-induced distortions. These findings highlight FIND-Net's potential for advancing MAR performance, offering superior structural preservation and improved clinical applicability. Code is available at https://github.com/Farid-Tasharofi/FIND-Net
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