论文标题
从投影的形状通过可区分的前向投影仪进行计算机断层扫描
Shape from Projections via Differentiable Forward Projector for Computed Tomography
论文作者
论文摘要
在计算机断层扫描中，通常在体素电网上获得重建。但是，在这项工作中，我们提出了一种基于网格的重建方法。对于断层扫描问题，已经研究了3D网格，以模拟数据采集，但不是为了重建，3D网格意味着从投影中估算形状的反向过程。在本文中，我们为3D网格提出了一个可区分的远期模型，该模型弥合了3D表面和优化的前向模型之间的差距。我们将远期预测视为一个渲染过程，并通过在可区分渲染中扩展最新工作来使其可区分。我们使用拟议的前向模型直接从预测中重建3D形状。单对象问题的实验结果表明，该提出的方法在嘈杂的模拟数据上优于基于传统的体素方法。我们还将提出的方法应用于纳米颗粒的电子断层扫描图像，以证明该方法在实际数据上的适用性。
In computed tomography, the reconstruction is typically obtained on a voxel grid. In this work, however, we propose a mesh-based reconstruction method. For tomographic problems, 3D meshes have mostly been studied to simulate data acquisition, but not for reconstruction, for which a 3D mesh means the inverse process of estimating shapes from projections. In this paper, we propose a differentiable forward model for 3D meshes that bridge the gap between the forward model for 3D surfaces and optimization. We view the forward projection as a rendering process, and make it differentiable by extending recent work in differentiable rendering. We use the proposed forward model to reconstruct 3D shapes directly from projections. Experimental results for single-object problems show that the proposed method outperforms traditional voxel-based methods on noisy simulated data. We also apply the proposed method on electron tomography images of nanoparticles to demonstrate the applicability of the method on real data.
