A Deep Learning Method for Image based Anti-aliasing in CT Scanners with Single Focal Spot Acquisition

This paper describes a deep learning method that provides an improved image quality for single focal spot CT scan. An experiment with convolutional neural network learning is based on 30 clinical head datasets of flying focal spot images and the corresponding single focal spot images. The generated antialiasing images reduce streak artifacts and noise granularity, and improve the contrast of bony structure, which potentially meet with the high diagnostic criteria in clinical applications. At the same time, the CT system simplicity and stability properties are easier to maintain compared to a system with the flying focal spot system. Further, results for applications such as inner ear and extremities are also of diagnostic quality.