Abstract
Computed Tomography (CT) is pivotal in industrial quality control and medical diagnostics. Sparse-view CT, offering reduced ionizing radiation, faces challenges due to its under-sampled nature, leading to ill-posed reconstruction problems. Recent advancements in Implicit Neural Representations (INRs) have shown promise in addressing sparse-view CT reconstruction. Recognizing that CT often involves scanning similar subjects, we propose a novel approach to improve reconstruction quality through joint reconstruction of multiple objects using INRs. This approach can potentially leverage both the strengths of INRs and the statistical regularities across multiple objects. While current INR joint reconstruction techniques primarily focus on accelerating convergence via meta-initialization, they are not specifically tailored to enhance reconstruction quality. To address this gap, we introduce a novel INR-based Bayesian framework integrating latent variables to capture the inter-object relationships. These variables serve as a dynamic reference throughout the optimization, thereby enhancing individual reconstruction fidelity. Our extensive experiments, which assess various key factors such as reconstruction quality, resistance to overfitting, and generalizability, demonstrate significant improvements over baselines in common numerical metrics. This underscores a notable advancement in CT reconstruction methods.
Abstract (translated)
计算断层成像（CT）在工业品质控制和医学诊断中具有关键作用。稀疏视野CT由于其欠采样特性，面临挑战，导致欠拟合重建问题。随着隐式神经表示（INRs）的最近进展，显示了在解决稀疏视野CT重建方面取得进展的前景。认识到CT通常涉及对类似被试的扫描，我们提出了一种通过使用INRs共同重构多个对象来提高重建质量的新方法。这种方法可以利用INRs的优点和多个对象之间的统计 regularities。尽管当前的INR联合重建技术主要通过元初始化加速收敛，但它们并未专门针对提高重建质量进行优化。为了填补这一空白，我们引入了一个基于INRs的新颖贝叶斯框架，将潜在变量集成在一起，以捕捉对象之间的交互关系。这些变量在优化过程中充当动态参考，从而提高每个重建对象的准确性。我们对各种关键因素（如重建质量、过拟合抵抗性和泛化能力）的广泛实验证明，在常见数值指标上，基准线以上显著改善。这表明在CT重建方法上取得了显著的进展。
URL
https://arxiv.org/abs/2405.02509
