Noise2Aliasing: Unsupervised Deep Learning for View Aliasing and Noise Reduction in 4DCBCT

Abstract

Respiratory Correlated Cone Beam Computed Tomography (4DCBCT) is a technique used to address respiratory motion artifacts that affect reconstruction quality, especially for the thorax and upper-abdomen. 4DCBCT sorts the acquired projection images in multiple respiratory correlated bins. This technique results in the emergence of aliasing artifacts caused by the low number of projection images per bin, which severely impacts the image quality and limits downstream use. Previous attempts to address this problem relied on traditional algorithms, while only recently deep learning techniques are being employed. In this work, we propose Noise2Aliasing, which reduces both view-aliasing and statistical noise present in 4DCBCT scans. Using a fundamental property of the FDK reconstruction algorithm, and prior results from the literature, we prove mathematically the ability of the method to work and specify the underlying assumptions. We apply the method to a public dataset and to an in-house dataset and show that it matches the performance of a supervised approach and outperforms it when measurement noise is present in the data.