Multi-stage Deep Learning Artifact Reduction for Computed Tomography

In Computed Tomography (CT), an image of the interior structure of an object is computed from a set of acquired projection images. The quality of these reconstructed images is essential for accurate analysis, but this quality can be degraded by a variety of imaging artifacts. To improve reconstruction quality, the acquired projection images are often processed by a pipeline consisting of multiple artifact-removal steps applied in various image domains (e.g., outlier removal on projection images and denoising of reconstruction images). These artifact-removal methods exploit the fact that certain artifacts are easier to remove in a certain domain compared with other domains. Recently, deep learning methods have shown promising results for artifact removal for CT images. However, most existing deep learning methods for CT are applied as a post-processing method after reconstruction. Therefore, artifacts that are relatively difficult to remove in the reconstruction domain may not be effectively removed by these methods. As an alternative, we propose a multi-stage deep learning method for artifact removal, in which neural networks are applied to several domains, similar to a classical CT processing pipeline. We show that the neural networks can be effectively trained in succession, resulting in easy-to-use and computationally efficient training. Experiments on both simulated and real-world experimental datasets show that our method is effective in reducing artifacts and superior to deep learning-based post-processing.

Noise2Inverse: Self-supervised deep convolutional denoising for linear inverse problems in imaging

Recovering a high-quality image from noisy indirect measurement is an important problem with many applications. For such inverse problems, supervised deep convolutional neural network (CNN)-based denoising methods have shown strong results, but their success critically depends on the availability of a high-quality training dataset of similar measurements. For image denoising, methods are available that enable training without a separate training dataset by assuming that the noise in two different pixels is uncorrelated. However, this assumption does not hold for inverse problems, resulting in artifacts in the output of existing methods. Here, we propose Noise2Inverse, a deep CNN-based denoising method for linear inverse problems in imaging that does not require any additional clean or noisy data. Training a CNN-based denoiser is enabled by exploiting the noise model to compute multiple statistically independent reconstructions. We develop a theoretical framework which shows that such training indeed obtains a denoising CNN, assuming the measured noise is element-wise independent and zero-mean. On simulated CT datasets, Noise2Inverse demonstrates a substantial improvement in peak signal-to-noise ratio (> 2dB) and structural similarity index (> 30%) compared to image denoising methods and conventional reconstruction methods, such as Total-Variation Minimization. We also demonstrate that the method is able to significantly reduce noise in challenging real-world experimental datasets.