用仿真数据训练网络，有效降低多能谱CT的噪声干扰。
Multispectral CT Denoising via Simulation-Trained Deep Learning: Experimental Results at the ESRF BM18
- 通过角-空间与谱域冗余信息，结合注意力机制去噪。
- 在真实实验数据上显著降噪且保留细节，性能优于传统方法。
- 适合做高精度材料表征的科研人员参考使用。
多能谱计算机断层成像（CT）通过获取能量分辨的投影数据实现材料的高级表征。然而，由于入射X射线通量被分配到多个窄能量窗，每个能量窗的光子计数大幅减少，导致图像噪声显著增加，可能需延长扫描时间或产生严重噪声伪影。为此，本文提出一种专用于欧洲同步辐射装置BM18束线采集的多能谱CT投影数据的深度学习去噪方法。该方法通过专用子网络，在角-空间和谱域中利用非局部相似性及相邻能量带间的相关性，实现鲁棒去噪并保留精细结构。训练仅使用模拟数据，精确复现了BM18装置的物理与噪声特性，验证基于自定义含高原子序数与低原子序数材料的幻影扫描数据。去噪后的投影与重建结果相比经典去噪方法和基线卷积神经网络模型有显著提升。定量评估表明，该方法在宽谱范围内表现优异，对真实实验数据具有强泛化能力，显著降噪同时保持结构保真度。
原文摘要 · Abstract (English)
Multispectral computed tomography (CT) enables advanced material characterization by acquiring energy-resolved projection data. However, since the incoming X-ray flux is be distributed across multiple narrow energy bins, the photon count per bin is greatly reduced compared to standard energy-integrated imaging. This inevitably introduces substantial noise, which can either prolong acquisition times and make scan durations infeasible or degrade image quality with strong noise artifacts. To address this challenge, we present a dedicated neural network-based denoising approach tailored for multispectral CT projections acquired at the BM18 beamline of the ESRF. The method exploits redundancies across angular, spatial, and spectral domains through specialized sub-networks combined via stacked generalization and an attention mechanism. Non-local similarities in the angular-spatial domain are leveraged alongside correlations between adjacent energy bands in the spectral domain, enabling robust noise suppression while preserving fine structural details. Training was performed exclusively on simulated data replicating the physical and noise characteristics of the BM18 setup, with validation conducted on CT scans of custom-designed phantoms containing both high-Z and low-Z materials. The denoised projections and reconstructions demonstrate substantial improvements in image quality compared to classical denoising methods and baseline CNN models. Quantitative evaluations confirm that the proposed method achieves superior performance across a broad spectral range, generalizing effectively to real-world experimental data while significantly reducing noise without compromising structural fidelity.
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