SDF: A self-supervised denoiser framework for X-Ray computed tomography

We introduce the self-supervised denoiser framework (SDF), a training procedure for learned image denoisers in the context of X-ray computed tomography (CT). Computing a computed tomography CT image amounts to recovering a model parameter from indirect observations of the said model. A popular approach to solve this problem is to train a neural network in a supervised way to enhance an initial approximation of the model, learning the observation-to-image mapping, thus requiring image-observation pairs. In this work, we introduce a method that relies only on observations for pretraining and a few images for fine-tuning image denoisers for sparse-view and low-dose X-ray Computed Tomography (CT).We propose to train an image denoiser in the sinogram space by defining the learning task of an image denoiser as the prediction of one sinogram subset from another. Our approach does not require ground-truth image data, leverages the abundant data modality in CT, the sinogram, and can drastically enhance the quality of images reconstructed from a fraction of the measurements. We demonstrate that SDF produces better image quality in terms of peak signal-to-noise ratio than other analytical and self-supervised frameworks in 2D fan-beam and 3D cone-beam CT settings. Moreover, we show that the enhancement provided by SDF carries over when fine-tuning the image denoiser on a few examples, making it a suitable pretraining technique when there is little high-quality image data. Our results are established on experimental datasets, making SDF a strong candidate for being the building block of foundational, image-enhancement models in CT.