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基于深度学习的合成对比增强腹部CT的临床可行性在接受非增强CT扫描的患者中

Seungchul Han^1,2,3, Jong-Min Kim⁴, Junghoan Park^1,2

¹Department of Radiology, Seoul National University Hospital, 101 Daehak-ro, Jongno-gu, Seoul, 03080, Republic of Korea.

Scientific reports

|July 31, 2024

概括

基于深度学习的合成对比增强CT (DL-SynCCT) 在接受非增强CT (NECT) 的患者中提高了病变检测灵敏度. 这种人工智能工具增强了诊断信心,并帮助放射科医生识别异常,为标准NECT扫描提供了宝贵的补充.

科学领域:

放射学放射学是一门学科.
人工智能的人工智能
医疗成像医学成像

背景情况:

非增强型计算机断层扫描 (NECT) 对特定的患者群体至关重要.
在NECT中存在检测微妙病变的局限性.
合成对比增强提供了一个潜在的解决方案.

研究的目的:

开发和评估基于深度学习的合成对比增强计算机断层扫描 (DL-SynCCT) 的临床可行性.
评估DL-SynCCT在被指定接受NECT的患者中的有效性.
为了提高病变检测和诊断信心.

主要方法:

开发了DL-SynCCT,使用虚拟非对比CT (VNC) 数据进行弱监督学习.
在2202个双能CT CECT/VNC图像对上受过培训和内部验证.
在3个研究所和多个供应商的398名NECT患者的外部数据集上进行了临床验证.

主要成果:

与CECT相比,DL-SynCCT实现了较高的图像质量 (PSNR: 43.25 ± 0.41,SSIM: 0.92 ± 0.01).
当DL-SynCCT添加到NECT时,放射学家对于病变检测的敏感性显著增加 (72.0%76.4%,P < 0.001).
诊断信心水平与DL-SynCCT (3.03.6,P < 0.001) 显著更高.

结论:

弱监督的DL-SynCCT对于接受NECT的患者来说在临床上是可行的.
DL-SynCCT显著提高了检测异常发现的灵敏度.
这种人工智能驱动的方法在补充NECT时可以提高诊断性能.

关键词:

这里是 Abdomen Abdomen 的意思.深度学习是一种深度学习.诊断诊断诊断诊断是一个断层扫描 (Tomography) 是一个专业的技术.

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