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.

翻译：目的：稀疏视角计算断层扫描（CT）是通过降低采集总视角数来有效减少辐射剂量的方法，但会牺牲图像质量，进而影响疾病检测能力。本研究探索基于深度学习的稀疏颅脑CT扫描伪影减少技术及其对自动出血检测的影响。方法：使用公共数据集中3000名患者的模拟稀疏颅脑CT扫描数据训练U-Net进行伪影减少，这些数据通过不同水平的子采样重建。同时，在17545名患者的全采样CT数据上训练卷积神经网络用于自动出血检测。通过受试者工作特征曲线下面积（AUC-ROC）及对应的95%置信区间（CIs）、DeLong检验和混淆矩阵评估分类性能，并将U-Net性能与基于全变分（TV）的分析方法进行比较。结果：在图像质量和自动出血诊断方面，U-Net处理后的图像显著优于未处理及TV处理图像。采用U-Net后处理，视角数可从4096（AUC-ROC：0.974；95% CI：0.972-0.976）降至512（0.973；0.971-0.975），出血检测性能几乎无下降（P<.001），降至256（0.967；0.964-0.969）时仅出现轻微性能下降（P<.001）。结论：结果表明，基于U-Net的伪影减少显著提升稀疏颅脑CT中的自动出血检测能力。本研究强调，适当的后处理对于在最小化辐射剂量的同时获得最优图像质量和诊断准确性至关重要。