Low performing pixel correction in computed tomography using deep learning

Low performing pixels (LPP)/missing/bad channels in CT detectors, if left uncorrected cause ring and streak artifacts, structured non-uniformities, and make the reconstructed image unusable for diagnostic purposes. Many image processing methods are proposed to correct the ring and streak artifacts in reconstructed images, but it is more appropriate to correct the LPPs in sinogram domain as the errors are localized. Although Generative Adversarial Networks based sinogram inpainting methods have shown promise in interpolating the missing sinogram information, it is often observed that the reconstructed images lack diagnostic value especially in visualizing soft tissues with certain window width and level. In this work, we propose a deep-learning based solution that operates on the sinogram data to remove the distortions cause by LPPs. This method leverages the CT system geometry (including conjugate ray information) to learn the anatomy aware interpolation in the sinogram domain. We demonstrated the efficacy of the proposed method using data acquired on GE RevACT multi-slice CT system with flat-panel detector. We have considered 46 axial head scans out of them 42 sets are used for training and the remaining 4 sets for validation/testing. We have simulated isolated LPPs accounting for 10% of total channels in the central panel of the detector and corrected them using the proposed approach. Detailed statistical analysis has revealed that, approximately 5dB improvement in SNR is observed in both sinogram and reconstruction domain as compared to classical bicubic and Lagrange interpolation methods. Also, with reduction in ring and streak artifacts, the perceptual image quality is improved across all the test images.