| Popis: |
Photon-counting spectral computed tomography is now clinically available. These new detectors come with the promise of higher contrast-to-noise ratio and spatial resolution and improved low-dose imaging. However, one important design consideration is to build detector elements that are sufficiently homogeneous. In practice, there will always be a degree of inhomogeneity in the detector elements, which will materialize as variations in the energy bin thresholds. Without proper detector calibration, this will lead to streak artifacts in the sinograms and corresponding ring artifacts in the reconstructed images, which limit their clinical usefulness. Since detector calibration is a time-consuming process, having access to a fast ring correction technique may greatly improve workflow. In this paper, we propose a deep learning-based post-processing technique for ring artifact correction in photon-counting spectral CT. We train a UNet with a custom loss to correct for ring artifacts in the material basis images. Our proposed loss is made ``task-aware'' by explicitly incorporating the fact that we are working with spectral CT by combining a L1-loss operating on the material basis images with a perceptual loss, using VGG16 as feature extractor, operating on 70 keV virtual monoenergetic images. Our results indicate that using this novel loss greatly improves performance. We demonstrate that our proposed method can successfully produce ring corrected 40, 70, and 100 keV virtual monoenergetic images. |
