Neural Field Regularization by Denoising for 3D Sparse-View X-Ray Computed Tomography

Abstract In this paper, we present a method that allows the conditioning of Neural Fields using Regularization by Denoising (RED). As opposed to learning a joint convolutional neural network to condition the output of a neural field, the RED framework is memory-efficient. It allows us to decouple the conditioning network and neural field optimization entirely. We focus our work on applications for 3D sparse-view X-ray Computed Tomography (CT) and propose a flexible procedure that does not assume coordinate-friendly partitioning of the forward operator. Indeed, our method applies to any CT geometry, particularly Cone-Beam CT, which is the most common setup in industrial inspection. We quantitatively evaluate our approach and show that our method is either better or on par with the state-of-the-art regarding reconstruction quality while being the most memory-efficient.

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