ArXiv Preprint
Intracranial hemorrhage poses a serious health problem requiring rapid and
often intensive medical treatment. For diagnosis, a Cranial Computed Tomography
(CCT) scan is usually performed. However, the increased health risk caused by
radiation is a concern. The most important strategy to reduce this potential
risk is to keep the radiation dose as low as possible and consistent with the
diagnostic task. Sparse-view CT can be an effective strategy to reduce dose by
reducing the total number of views acquired, albeit at the expense of image
quality. In this work, we use a U-Net architecture to reduce artifacts from
sparse-view CCTs, predicting fully sampled reconstructions from sparse-view
ones. We evaluate the hemorrhage detectability in the predicted CCTs with a
hemorrhage classification convolutional neural network, trained on fully
sampled CCTs to detect and classify different sub-types of hemorrhages. Our
results suggest that the automated classification and detection accuracy of
hemorrhages in sparse-view CCTs can be improved substantially by the U-Net.
This demonstrates the feasibility of rapid automated hemorrhage detection on
low-dose CT data to assist radiologists in routine clinical practice.
Johannes Thalhammer, Manuel Schultheiss, Tina Dorosti, Tobias Lasser, Franz Pfeiffer, Daniela Pfeiffer, Florian Schaff
2023-03-16
