Deep learning ring artifact correction in photon-counting spectral CT with perceptual loss

Photon-counting spectral CT is a novel technology with a lot of promise. However, one common issue is detector inhomogeneity which results in streak artifacts in the sinogram domain and ring artifacts in the image domain. These rings are very conspicuous and limit the clinical usefulness of the images. We propose a deep learning based image processing technique for ring artifact correction in the sinogram domain. In particular, we train a UNet using a perceptual loss function with VGG16 as feature extractor to remove streak artifacts in the basis sinograms. Our results show that this method can successfully produce ring-corrected virtual monoenergetic images at a range of energy levels.
QC 20230614