Purpose: To improve the image quality of sparse-view computed tomography (CT) images with a U-Net for lung cancer detection and to determine the best trade-off between number of views, image quality, and diagnostic confidence. Methods: CT images from 41 subjects (34 with lung cancer, seven healthy) were retrospectively selected (01.2016-12.2018) and forward projected onto 2048-view sinograms. Six corresponding sparse-view CT data subsets at varying levels of undersampling were reconstructed from sinograms using filtered backprojection with 16, 32, 64, 128, 256, and 512 views, respectively. A dual-frame U-Net was trained and evaluated for each subsampling level on 8,658 images from 22 diseased subjects. A representative image per scan was selected from 19 subjects (12 diseased, seven healthy) for a single-blinded reader study. The selected slices, for all levels of subsampling, with and without post-processing by the U-Net model, were presented to three readers. Image quality and diagnostic confidence were ranked using pre-defined scales. Subjective nodule segmentation was evaluated utilizing sensitivity (Se) and Dice Similarity Coefficient (DSC) with 95% confidence intervals (CI). Results: The 64-projection sparse-view images resulted in Se = 0.89 and DSC = 0.81 [0.75,0.86] while their counterparts, post-processed with the U-Net, had improved metrics (Se = 0.94, DSC = 0.85 [0.82,0.87]). Fewer views lead to insufficient quality for diagnostic purposes. For increased views, no substantial discrepancies were noted between the sparse-view and post-processed images. Conclusion: Projection views can be reduced from 2048 to 64 while maintaining image quality and the confidence of the radiologists on a satisfactory level.

翻译：目的：通过U-Net提升稀疏视图计算机断层扫描（CT）图像质量以用于肺癌检测，并确定视图数量、图像质量与诊断可信度之间的最佳平衡点。方法：回顾性选取41名受试者（34名肺癌患者，7名健康者）的CT图像（2016年1月至2018年12月），对图像进行前向投影生成2048视图正弦图。分别采用滤波反投影法从包含16、32、64、128、256和512个视图的六种不同欠采样水平的正弦图重建对应的稀疏视图CT数据子集。针对每种欠采样水平，使用来自22名患者的8658张图像训练并评估双帧U-Net。从19名受试者（12名患者，7名健康者）中为单盲阅片研究选取每个扫描的代表性图像。将各欠采样水平下经U-Net模型后处理及未经后处理的选定切片呈现给三名阅片者。采用预定义量表对图像质量和诊断可信度进行评分。利用敏感性及Dice相似系数（含95%置信区间）评估主观结节分割效果。结果：64投影稀疏视图图像的敏感性=0.89、Dice相似系数=0.81[0.75,0.86]；经U-Net处理后，相应图像的指标提升至敏感性=0.94、Dice相似系数=0.85[0.82,0.87]。更少视图会导致图像质量不足以满足诊断需求。随着视图数增加，稀疏视图图像与处理后图像之间未观察到显著差异。结论：在保持图像质量及放射科医师满意度的情况下，可将投影视图从2048减少至64个。