基于微局部分析改进深度学习重建，提升不完整数据下的断层成像质量
Revisiting $Ψ$DONet: microlocally inspired filters for incomplete-data tomographic reconstructions
- 利用微局部理论设计针对伪影特征的特殊滤波器
- 参数量大幅降低，仍保持甚至提升有限角度重建质量
- 适用于稀疏角度断层扫描，为实际应用提供可行方案
本文重新审视一种基于可微分迭代展开的监督学习方法ΨDONet，从微局部理论角度深化其分析，并将其拓展至稀疏角度断层扫描场景。通过设计受断层成像不完整数据中条纹伪影奇异结构启发的专用滤波器，优化了原ΨDONet的实现。该改进显著减少了可学习参数数量，同时在有限角度数据重建上保持或小幅提升图像质量，并为稀疏角度数据重建提供了初步验证。结果表明，该方法在降低模型复杂度的同时有效抑制伪影，具有良好的实用性。
原文摘要 · Abstract (English)
In this paper, we revisit a supervised learning approach based on unrolling, known as $Ψ$DONet, by providing a deeper microlocal interpretation for its theoretical analysis, and extending its study to the case of sparse-angle tomography. Furthermore, we refine the implementation of the original $Ψ$DONet considering special filters whose structure is specifically inspired by the streak artifact singularities characterizing tomographic reconstructions from incomplete data. This allows to considerably lower the number of (learnable) parameters while preserving (or even slightly improving) the same quality for the reconstructions from limited-angle data and providing a proof-of-concept for the case of sparse-angle tomographic data.
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