4D time-space reconstruction of dynamic events or deforming objects using X-ray computed tomography (CT) is an extremely ill-posed inverse problem. Existing approaches assume that the object remains static for the duration of several tens or hundreds of X-ray projection measurement images (reconstruction of consecutive limited-angle CT scans). However, this is an unrealistic assumption for many in-situ experiments that causes spurious artifacts and inaccurate morphological reconstructions of the object. To solve this problem, we propose to perform a 4D time-space reconstruction using a distributed implicit neural representation (DINR) network that is trained using a novel distributed stochastic training algorithm. Our DINR network learns to reconstruct the object at its output by iterative optimization of its network parameters such that the measured projection images best match the output of the CT forward measurement model. We use a continuous time and space forward measurement model that is a function of the DINR outputs at a sparsely sampled set of continuous valued object coordinates. Unlike existing state-of-the-art neural representation architectures that forward and back propagate through dense voxel grids that sample the object's entire time-space coordinates, we only propagate through the DINR at a small subset of object coordinates in each iteration resulting in an order-of-magnitude reduction in memory and compute for training. DINR leverages distributed computation across several compute nodes and GPUs to produce high-fidelity 4D time-space reconstructions even for extremely large CT data sizes. We use both simulated parallel-beam and experimental cone-beam X-ray CT datasets to demonstrate the superior performance of our approach.

翻译：利用X射线计算机断层扫描（CT）对动态事件或变形物体进行四维时空重建是一个极度病态的逆问题。现有方法假设物体在数十或数百张X射线投影测量图像的采集过程中保持静止（即连续有限角度CT扫描的重建）。然而，这一假设对许多原位实验而言并不现实，会导致伪影和物体形态重建不准确。为解决这一问题，我们提出采用分布式隐式神经表示（DINR）网络进行四维时空重建，该网络通过一种新颖的分布式随机训练算法进行训练。我们的DINR网络通过迭代优化其网络参数，使测得的投影图像与CT正向测量模型的输出最佳匹配，从而学习重建物体。我们采用连续时空正向测量模型，该模型依赖于稀疏采样连续值物体坐标处的DINR输出。与现有最先进的神经表示架构不同（这些架构需要在密集体素网格中进行正向和反向传播，以采样物体的完整时空坐标），我们在每次迭代中仅通过少量物体坐标处的DINR进行传播，从而使训练的内存和计算量降低一个数量级。DINR利用多个计算节点和GPU的分布式计算能力，即使针对极大规模的CT数据，也能生成高保真的四维时空重建结果。我们使用模拟平行束和实验锥束X射线CT数据集验证了本方法的优越性能。