论文标题
使用基于能量的先验的计算机断层扫描重建
Computed Tomography Reconstruction using Generative Energy-Based Priors
论文作者
论文摘要
在过去的几十年中，计算机断层扫描（CT）已将自己确立为医学中最重要的成像技术之一。如今，CT的适用性仅受沉积的辐射剂量的限制，其减少在嘈杂或不完整的测量中表现出来。因此，出现了强大的重建算法的需求。在这项工作中，我们通过最大化参考CT数据的可能性来学习具有全球接收领域的参数正常化程序。由于这种无监督的学习策略，我们训练有素的正常化程序确实代表了更高级别的领域统计数据，我们通过合成CT图像从经验上证明了这一点。此外，通过将其嵌入变异框架中，可以轻松地将其应用于不同的CT重建问题，这与基于前馈学习的方法相比提高了灵活性和解释性。此外，随附的概率观点使专家能够探索完整的后验分布，并可以量化重建方法的不确定性。我们将正规化程序应用于有限角度和少量视图CT重建问题，在该问题中，它的表现要优于传统重建算法的大幅度。
In the past decades, Computed Tomography (CT) has established itself as one of the most important imaging techniques in medicine. Today, the applicability of CT is only limited by the deposited radiation dose, reduction of which manifests in noisy or incomplete measurements. Thus, the need for robust reconstruction algorithms arises. In this work, we learn a parametric regularizer with a global receptive field by maximizing it's likelihood on reference CT data. Due to this unsupervised learning strategy, our trained regularizer truly represents higher-level domain statistics, which we empirically demonstrate by synthesizing CT images. Moreover, this regularizer can easily be applied to different CT reconstruction problems by embedding it in a variational framework, which increases flexibility and interpretability compared to feed-forward learning-based approaches. In addition, the accompanying probabilistic perspective enables experts to explore the full posterior distribution and may quantify uncertainty of the reconstruction approach. We apply the regularizer to limited-angle and few-view CT reconstruction problems, where it outperforms traditional reconstruction algorithms by a large margin.
