通过优化网络分辨率，实现移动端快速精准的锥束CT散射校正。
Ultrafast Deep Learning-Based Scatter Estimation in Cone-Beam Computed Tomography
- 采用多分辨率网络设计，降低输入尺寸与参数量
- FLOPs减少78倍，推理时间缩短16倍，内存减12倍，误差仍低
- 适合移动CBCT、边缘设备等资源受限场景
目的：散射伪影严重降低锥束计算机断层扫描（CBCT）图像质量。尽管基于深度学习的方法在从CBCT测量中估计散射方面展现潜力，但因其网络内存占用大，在移动CBCT系统或边缘设备上的部署仍受限制。本研究通过在不同分辨率下应用网络并选择最优方案，解决了该问题。方法：首先在六种分辨率下比较四种插值方法，评估CBCT散射信号下采样-上采样过程中的重建误差；随后，对一种最新最先进的方法在五种图像分辨率下进行训练与评估，分析浮点运算量（FLOPs）、推理时间及GPU内存需求的降低效果。结果：输入尺寸和网络参数的缩减使FLOPs相比基线方法降低78倍，同时保持相当的性能：平均绝对百分比误差（MAPE）由4.42%降至3.85%，均方误差（MSE）由2.01×10⁻²降至1.34×10⁻²。推理时间与GPU内存使用分别减少16倍和12倍。在大型模拟数据集以及水和Sedentex CT体模的真实CBCT扫描上进行的散射校正重建实验，进一步验证了方法的鲁棒性。结论：本研究强调了下采样在深度学习散射估计中的未被重视作用。所提方法显著降低计算与内存开销，使其适用于移动CBCT和边缘设备等资源受限环境。
原文摘要 · Abstract (English)
Purpose: Scatter artifacts drastically degrade the image quality of cone-beam computed tomography (CBCT) scans. Although deep learning-based methods show promise in estimating scatter from CBCT measurements, their deployment in mobile CBCT systems or edge devices is still limited due to the large memory footprint of the networks. This study addresses the issue by applying networks at varying resolutions and suggesting an optimal one, based on speed and accuracy. Methods: First, the reconstruction error in down-up sampling of CBCT scatter signal was examined at six resolutions by comparing four interpolation methods. Next, a recent state-of-the-art method was trained across five image resolutions and evaluated for the reductions in floating-point operations (FLOPs), inference times, and GPU memory requirements. Results: Reducing the input size and network parameters achieved a 78-fold reduction in FLOPs compared to the baseline method, while maintaining comarable performance in terms of mean-absolute-percentage-error (MAPE) and mean-square-error (MSE). Specifically, the MAPE decreased to 3.85% compared to 4.42%, and the MSE decreased to 1.34 \times 10^{-2} compared to 2.01 \times 10^{-2}. Inference time and GPU memory usage were reduced by factors of 16 and 12, respectively. Further experiments comparing scatter-corrected reconstructions on a large, simulated dataset and real CBCT scans from water and Sedentex CT phantoms clearly demonstrated the robustness of our method. Conclusion: This study highlights the underappreciated role of downsampling in deep learning-based scatter estimation. The substantial reduction in FLOPs and GPU memory requirements achieved by our method enables scatter correction in resource-constrained environments, such as mobile CBCT and edge devices.
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