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The time of an X-ray Computed Tomography (XCT) measurement is directly affected by the number of acquired X-ray projections. In general, a large number of X-ray projections, hence a large acquisition time is required to obtain a qualitative reconstruction and successive feature analysis. To expand the applicability of XCT towards low-end parts we propose a novel sinogram interpolation method that incorporates the object rotation. The method combines a forward projection model of the XCT system and a deep learning regression model to generate intermediate X-ray projections for XCT scans with a reduced number of projections. Thereby, the accompanying reconstruction artefacts can be reduced while preserving a lower acquisition time. The method is validated on simulated projections of the 3D Shepp-Logan phantom and simulated projections of real 3D printed components using the ASTRA-toolbox. Within the reconstructions, less streaking artefacts are observed and an increased contrast between different features is obtained without introducing rotational artefacts.

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.