Abstract
Muon Scattering Tomography (MST) is a promising non-invasive inspection technique, yet the practical application of short-time MST is hindered by poor image quality due to limited muon flux. To address this limitation, we propose a U-Net-based framework trained on Point of Closest Approach (PoCA) images reconstructed with simulation MST data to enhance image quality. When applied to experimental MST data, the framework significantly improves image quality, increasing the Structural Similarity Index Measure (SSIM) from 0.7232 to 0.9699 and decreasing the Learned Perceptual Image Patch Similarity (LPIPS) from 0.3604 to 0.0270. These results demonstrate that our method can effectively enhance low-statistics MST images, thereby paving the way for the practical deployment of short-time MST.
Abstract (translated)
缪子散射断层成像（MST）是一种有前景的非侵入性检测技术，然而由于缪子通量有限导致图像质量不佳，短时间内的实际应用受到了限制。为了解决这一局限，我们提出了一种基于U-Net框架的方法，该方法使用模拟MST数据重建的最接近点成像（PoCA）进行训练，以提升图像质量。当应用于实验性的MST数据时，此框架显著改善了图像的质量，将结构相似性指数测量（SSIM）从0.7232提高到0.9699，并且将学习感知图像块相似度（LPIPS）从0.3604降低至0.0270。这些结果表明我们的方法能够有效提升低统计量的MST图像的质量，从而为短时间MST的实际应用铺平了道路。
URL
https://arxiv.org/abs/2602.07060
