基于 U-Net 网络的稀疏视角肺部肿瘤 CT 图像质量提升

Improving image quality of sparse-view lung tumor CT images with U-Net.

机构信息

Chair of Biomedical Physics, Department of Physics, School of Natural Sciences, Technical University of Munich, Garching, 85748, Germany.

Munich Institute of Biomedical Engineering, Technical University of Munich, 85748, Garching, Germany.

出版信息

Eur Radiol Exp. 2024 May 3;8(1):54. doi: 10.1186/s41747-024-00450-4.

DOI:10.1186/s41747-024-00450-4

PMID:38698099

原文链接:https://pmc.ncbi.nlm.nih.gov/articles/PMC11065797/

Abstract

BACKGROUND

We aimed to improve the image quality (IQ) of sparse-view computed tomography (CT) images using a U-Net for lung metastasis detection and determine the best tradeoff between number of views, IQ, and diagnostic confidence.

METHODS

CT images from 41 subjects aged 62.8 ± 10.6 years (mean ± standard deviation, 23 men), 34 with lung metastasis, 7 healthy, were retrospectively selected (2016-2018) and forward projected onto 2,048-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. 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, 7 healthy) for a single-blinded multireader study. These slices, for all levels of subsampling, with and without U-Net postprocessing, were presented to three readers. IQ and diagnostic confidence were ranked using predefined scales. Subjective nodule segmentation was evaluated using sensitivity and Dice similarity coefficient (DSC); clustered Wilcoxon signed-rank test was used.

RESULTS

The 64-projection sparse-view images resulted in 0.89 sensitivity and 0.81 DSC, while their counterparts, postprocessed with the U-Net, had improved metrics (0.94 sensitivity and 0.85 DSC) (p = 0.400). Fewer views led to insufficient IQ for diagnosis. For increased views, no substantial discrepancies were noted between sparse-view and postprocessed images.

CONCLUSIONS

Projection views can be reduced from 2,048 to 64 while maintaining IQ and the confidence of the radiologists on a satisfactory level.

RELEVANCE STATEMENT

Our reader study demonstrates the benefit of U-Net postprocessing for regular CT screenings of patients with lung metastasis to increase the IQ and diagnostic confidence while reducing the dose.

KEY POINTS

• Sparse-projection-view streak artifacts reduce the quality and usability of sparse-view CT images. • U-Net-based postprocessing removes sparse-view artifacts while maintaining diagnostically accurate IQ. • Postprocessed sparse-view CTs drastically increase radiologists' confidence in diagnosing lung metastasis.

摘要

背景

我们旨在使用 U-Net 提高肺转移检测稀疏视图计算机断层扫描（CT）图像的图像质量（IQ），并确定视图数量、IQ 和诊断置信度之间的最佳权衡。

方法

回顾性选择了 41 名年龄 62.8±10.6 岁（均值±标准差，23 名男性）的患者的 CT 图像，其中 34 名患有肺转移，7 名健康。这些图像在 2016 年至 2018 年间采集，并向前投影到 2048 个视图正弦图上。使用滤波反投影从正弦图中重建了六个不同欠采样水平的稀疏视图 CT 数据子集，使用 16、32、64、128、256 和 512 个视图进行重建。使用来自 22 名患病患者的 8658 张图像对每个子采样水平的双框架 U-Net 进行了训练和评估。从 19 名患者（12 名患病，7 名健康）中选择了一张代表性的扫描图像进行单次盲多位读者研究。对所有子采样水平的这些切片，包括使用和不使用 U-Net 后处理的切片，由三位读者进行审阅。使用预定义的量表对 IQ 和诊断置信度进行排名。使用敏感性和 Dice 相似系数（DSC）评估主观结节分割；使用聚类 Wilcoxon 符号秩检验。