使用条件生成对抗网络减少稀疏投影CT中的图像工件.
Keisuke Usui1,2, Sae Kamiyama3, Akihiro Arita3
1Department of Radiological Technology, Faculty of Health Science, Juntendo University, 2-1-1, Hongo, Bunkyo-ku, Tokyo, 113-8421, Japan. k-usui@juntendo.ac.jp.
Scientific reports
|February 16, 2024
概括
条件生成对抗网络 (CGAN) 通过减少工件来提高稀疏视图计算机断层扫描 (CT) 图像质量. 与自动编码器和U-Net模型相比,这种方法提供了更好的CT值恢复和图像相似性.
科学领域:
- 医疗成像医学成像
- 医疗保健中的人工智能
- 放射学 放射学是一门学科.
背景情况:
- 在计算机断层扫描 (CT) 中减少投影数据降低了辐射剂量,但引入了图像工件.
- 稀疏视图CT (SVCT) 在由于文物而维持诊断图像质量方面存在挑战.
研究的目的:
- 量化评估条件生成对抗网络 (CGAN) 在稀疏视图CT中恢复图像质量的有效性.
- 为了比较CGAN与自动编码器 (AE) 和U-Net模型在稀疏视图CT图像恢复中的性能.
主要方法:
- 模拟的稀疏投影CT图像被用于训练AE,U-Net和CGAN模型.
- 模型被训练在文物-原始图像对上,90%的患者病例用于训练,10%用于评估.
- 使用平均误差 (ME),平均绝对误差 (MAE),结构图像相似性 (SSIM) 和峰值信号噪声比 (PSNR) 来评估图像质量.
主要成果:
- 所有模型都显示图像质量有所改善,但AE和U-Net导致分辨率降低和模糊,肺/空气区域存在重大错误.
- 与AE和U-Net相比,CGAN证明了精确的CT值恢复和优越的SSIM和PSNR.
- 在CGAN结果中,发现了瘤/脊柱区域的轻微变形和一些幻觉器件.
结论:
- CGAN模型为稀疏视图CT中高保真图像质量恢复提供了一个有希望的方法.
- 在低剂量CT应用中,CGAN在减轻文物和保持图像保真方面优于AE和U-Net.
- 进一步的研究可能会解决在CGAN中观察到的轻微工件,以便在SVCT中完全抑制工件.
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