用自监督深度学习降噪原始探测器数据，提升超快电子束CT图像质量。
Self-supervised denoising of raw tomography detector data for improved image reconstruction
- 设计两种自监督深度学习方法，直接对原始探测数据去噪。
- 显著提升探测数据信噪比，重建图像质量稳定改善。
- 适合从事医学成像、工业检测中快速扫描图像优化的研究者。
超快电子束X射线计算机断层扫描因测量时间极短导致数据噪声大，引发重建伪影并限制整体图像质量。为解决该问题，本文研究并对比了两种基于自监督深度学习的原始探测器数据去噪方法，与非学习型去噪方法进行比较。结果表明，深度学习方法能有效提升探测数据的信噪比，并带来一致的重建图像质量改进，优于传统非学习方法。
原文摘要 · Abstract (English)
Ultrafast electron beam X-ray computed tomography produces noisy data due to short measurement times, causing reconstruction artifacts and limiting overall image quality. To counteract these issues, two self-supervised deep learning methods for denoising of raw detector data were investigated and compared against a non-learning based denoising method. We found that the application of the deep-learning-based methods was able to enhance signal-to-noise ratios in the detector data and also led to consistent improvements of the reconstructed images, outperforming the non-learning based method.
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