UnWave-Net：用于康普顿断层成像图像重建的展开小波网络
UnWave-Net: Unrolled Wavelet Network for Compton Tomography Image Reconstruction
AI总结：
本文针对康普顿散射断层成像重建难题，提出UnWave-Net展开小波网络，通过非局部小波正则化捕捉长程依赖并提升计算效率，在静态圆形几何CST上取得最优SSIM和PSNR性能。
摘要：
计算机断层成像（CT）是一种广泛应用的医学成像技术，用于扫描人体内部结构，通常涉及准直和机械旋转。康普顿散射断层成像（CST）通过利用康普顿物理而非准直来从多个方向获取信息，为传统CT提供了一种有吸引力的替代方案。虽然CST带来了新的成像机会，并具有高灵敏度、紧凑性和完全固定系统等若干优势，但由于CST建模的数学挑战，图像重建仍然是一个悬而未决的问题。相比之下，深度展开网络尽管计算密集，但已在CT图像重建中展现出潜力。在本研究中，我们考察了展开网络用于CST图像重建的效率。为解决训练所需的重要计算成本问题，我们提出了UnWave-Net，一种新颖的基于展开小波的重建网络。该架构包含一个基于小波的非局部正则化项，能够捕捉图像内的长程依赖关系，并强调小波变换的多尺度分量。我们使用一种在数据采集过程中保持完全静态的圆形几何CST来评估我们的方法，其中UnWave-Net在缺乏特定重建公式的情况下促进了图像重建。我们的方法优于现有方法，在SSIM和PSNR方面达到了最先进的性能，并且与传统展开网络相比提供了更高的计算效率。
英文摘要：
Computed tomography (CT) is a widely used medical imaging technique to scan internal structures of a body, typically involving collimation and mechanical rotation. Compton scatter tomography (CST) presents an interesting alternative to conventional CT by leveraging Compton physics instead of collimation to gather information from multiple directions. While CST introduces new imaging opportunities with several advantages such as high sensitivity, compactness, and entirely fixed systems, image reconstruction remains an open problem due to the mathematical challenges of CST modeling. In contrast, deep unrolling networks have demonstrated potential in CT image reconstruction, despite their computationally intensive nature. In this study, we investigate the efficiency of unrolling networks for CST image reconstruction. To address the important computational cost required for training, we propose UnWave-Net, a novel unrolled wavelet-based reconstruction network. This architecture includes a non-local regularization term based on wavelets, which captures long-range dependencies within images and emphasizes the multi-scale components of the wavelet transform. We evaluate our approach using a CST of circular geometry which stays completely static during data acquisition, where UnWave-Net facilitates image reconstruction in the absence of a specific reconstruction formula. Our method outperforms existing approaches and achieves state-of-the-art performance in terms of SSIM and PSNR, and offers an improved computational efficiency compared to traditional unrolling networks.
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