  This is a preprint. The latest version has been published here:
https://pubs.rsna.org/doi/10.1148/ryai.230275
  Purpose: Sparse-view computed tomography (CT) is an effective way to reduce
dose by lowering the total number of views acquired, albeit at the expense of
image quality, which, in turn, can impact the ability to detect diseases. We
explore deep learning-based artifact reduction in sparse-view cranial CT scans
and its impact on automated hemorrhage detection. Methods: We trained a U-Net
for artefact reduction on simulated sparse-view cranial CT scans from 3000
patients obtained from a public dataset and reconstructed with varying levels
of sub-sampling. Additionally, we trained a convolutional neural network on
fully sampled CT data from 17,545 patients for automated hemorrhage detection.
We evaluated the classification performance using the area under the receiver
operator characteristic curves (AUC-ROCs) with corresponding 95% confidence
intervals (CIs) and the DeLong test, along with confusion matrices. The
performance of the U-Net was compared to an analytical approach based on total
variation (TV). Results: The U-Net performed superior compared to unprocessed
and TV-processed images with respect to image quality and automated hemorrhage
diagnosis. With U-Net post-processing, the number of views can be reduced from
4096 (AUC-ROC: 0.974; 95% CI: 0.972-0.976) views to 512 views (0.973;
0.971-0.975) with minimal decrease in hemorrhage detection (P<.001) and to 256
views (0.967; 0.964-0.969) with a slight performance decrease (P<.001).
Conclusion: The results suggest that U-Net based artifact reduction
substantially enhances automated hemorrhage detection in sparse-view cranial
CTs. Our findings highlight that appropriate post-processing is crucial for
optimal image quality and diagnostic accuracy while minimizing radiation dose.
