Deep learning based sinogram interpolation applied to X-ray CT measurements of polymer additive manufacturing parts

Abstract

Large acquisition times are required to achieve high-quality XCT scans, because of the need for high exposure times and a large number of X-ray projections. Reducing the number of projections results in an increase in noise, artefacts in the reconstruction domain and measurement errors. To enhance those low-quality XCT scans, while keeping acquisition times low, we investigated the possibilities of deep learning sinogram interpolation, by using a conditional generative adversarial network (cGAN) to artificially increase the number of X-ray projections before reconstruction. The method is evaluated on simulated XCT scans from real objects using the ASTRA toolbox. First, the hyperparameters of the loss functions were altered to determine the optimal combination. Second, we experimented with different acquisition schemes and finally the number of X-ray projections is gradually reduced to quantify the minimum required X-ray projections and acquisition time.