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Data-driven CT-image reconstruction techniques for truncated or sparsely acquired projection data to reduce radiation dose, iodine volume, and patient motion artifacts have been proposed. These approaches have shown good performance and preservation of image quality metrics. To continue these efforts, we investigated whether these techniques affect the performance of a machine-learning algorithm to identify the presence of intracranial hemorrhage (ICH). Ten-thousand head CT scans were collected from the 2019 RSNA Intracranial Hemorrhage Detection and Classification Challenge dataset. Sinograms were simulated and then resampled in both a one-third truncated and onethird sparse manner. GANs were tasked with correcting the incomplete projection data in two ways. Firstly, in the sinogram domain, where the incomplete sinogram was filled by the GAN and then reconstructed. Secondly, in the reconstruction domain, where the incomplete data were first reconstructed and the sparse or truncation artifacts were corrected by the GAN. Eighty-five hundred images were used for artifact correction network training, and 1500 were withheld for network assessment via an already trained machine-learning algorithm tasked with diagnosis of ICH presence. Fully-sampled reconstructions were compared with the sparse and truncated reconstructions for classification accuracy. Difference in classification accuracy between the fully sampled (83.4%), sparse (82.0%), and truncated (82.3%) reconstructions was minimal, demonstrating that the network diagnosis performance is unaffected by 2/3 reduction of projection data. This work indicates that data-driven reconstructions for a sparse or truncated projection dataset can provide high diagnostic performance for ICH detection at a fraction of the typical radiation dose.
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