Motion Correction Image Reconstruction using NeuralCT Improves with Spatially Aware Object Segmentation

NeuralCT [1] has been recently proposed as an implicit neural representation-based image reconstruction that can produce time-resolved images from CT sinograms and reduce motion artifacts, even when undergoing complex motions. NeuralCT does not require the prior motion model or estimation of object motion. Instead, it utilizes a network to implicitly represent the time-varying object boundary by singed distance function and optimizes the network via differentiable rendering. In this work, we modify the NeuralCT framework to reconstruct scenes that have multiple moving objects with distinct attenuation levels. We show that the performance of NeuralCT reconstruction depends on the quality of the initialization of the network (in this case, object segmentation in motion corrupted FBP image). We show how spatially aware object segmentation can improve motion-corrected reconstruction in moving objects with multiple attenuation levels despite high angular motion and complex topological changes.