论文标题
REGAS：单个3D CBCT采集的多相CBCT重建视图的呼吸门控综合
REGAS: REspiratory-GAted Synthesis of Views for Multi-Phase CBCT Reconstruction from a single 3D CBCT Acquisition
论文作者
论文摘要
在呼吸运动下，重建肺部圆锥束计算机断层扫描（CBCT）是一个长期的挑战。这项工作更进一步，以解决一个具有挑战性的设置，以重建仅从单个} 3D CBCT采集中的多相肺图像。为此，我们介绍了对观点或Regas的估算门控综合。 Regas提出了一种自我监督的方法，以合成不足采样的层析成像视图并减轻重建图像中的混叠伪像。此方法允许对相间变形矢量场（DVF）进行更好的估计，这些估计用于从无需合成的直接观察来提高重建质量。为了解决高分辨率4D数据上深神经网络的庞大记忆成本，Regas引入了一种新颖的射线路径变换（RPT），该射线路径变换（RPT）允许分布式，可区分的远期投影。 Regas不需要其他测量，例如先前的扫描，空气流量或呼吸速度。我们的广泛实验表明，REGA在定量指标和视觉质量方面的表现明显优于可比的方法。
It is a long-standing challenge to reconstruct Cone Beam Computed Tomography (CBCT) of the lung under respiratory motion. This work takes a step further to address a challenging setting in reconstructing a multi-phase}4D lung image from just a single}3D CBCT acquisition. To this end, we introduce REpiratory-GAted Synthesis of views, or REGAS. REGAS proposes a self-supervised method to synthesize the undersampled tomographic views and mitigate aliasing artifacts in reconstructed images. This method allows a much better estimation of between-phase Deformation Vector Fields (DVFs), which are used to enhance reconstruction quality from direct observations without synthesis. To address the large memory cost of deep neural networks on high resolution 4D data, REGAS introduces a novel Ray Path Transformation (RPT) that allows for distributed, differentiable forward projections. REGAS require no additional measurements like prior scans, air-flow volume, or breathing velocity. Our extensive experiments show that REGAS significantly outperforms comparable methods in quantitative metrics and visual quality.
