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.

翻译：颅内出血是一种严重的健康问题，需要快速且通常是密集的医疗干预。为了诊断，通常需要进行颅部计算机断层扫描（CCT）。然而，由辐射引起的健康风险增加是一个值得关注的问题。降低这一潜在风险的最重要策略是保持尽可能低的辐射剂量，并与诊断任务相符。稀疏视图CT可以通过减少采集的总视图数来有效降低剂量，尽管这会以牺牲图像质量为代价。在这项工作中，我们使用U-Net架构来减少稀疏视图CCT中的伪影，从稀疏视图重建中预测全采样重建。我们通过一个在完全采样CCT上训练以检测和分类不同出血亚型的出血分类卷积神经网络，评估预测CCT中的出血可检测性。我们的结果表明，通过U-Net，稀疏视图CCT中的出血自动分类和检测准确性可以得到显著提高。这证明了在低剂量CT数据上进行快速自动出血检测的可行性，有助于放射科医生的常规临床实践。