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深度学习增强的超快全身光学扫描在疑似恶性瘤患者的临床性能

Na Qi¹, Boyang Pan², Qingyuan Meng¹

¹Department of Nuclear Medicine, Shanghai East Hospital, Tongji University School of Medicine, Shanghai, 200120, China.

BMC medical imaging

|September 9, 2024

概括

深度学习图像增强显著提高了超快的全身光学 (WBS) 质量,实现了与标准扫描相美的诊断性能. 模拟模型看起来很有希望,但可能无法完全复制真实世界的数据细微差别.

科学领域:

医疗成像医学成像
人工智能的人工智能
核医学就是核医学.

背景情况:

核医学中的加速成像技术旨在减少患者扫描时间.
深度学习 (DL) 提供了提高快速光学图像质量的潜力.
使用真实和模拟数据评估DL性能对于临床翻译至关重要.

研究的目的:

评估两种DL方法的临床性能,以提高2D快速全身光学 (WBS) 图像质量.
为了比较在真实临床数据和模拟数据集上训练的DL模型.
评估加速WBS获取速度对诊断疗效的影响.

主要方法:

对83名怀疑骨转移的患者进行了回顾性分析,这些患者在1x,2x和3x速度下接受了WBS.
开发两个DL模型:一个使用真实快速扫描数据 (2x-real,3x-real),另一个使用模拟数据 (2x-simu,3x-simu).
使用利克特尺度,LPIPS和FID评估图像质量;通过准确度,灵敏度,特异性和AUC评估诊断疗效.

主要成果:

通过DL增强的图像 (2x-real,2x-simu,3x-real,3x-simu) 与标准快速扫描 (2x,3x) 相比,显示质量明显提高.
超快速的2x速度图像 (真实和模拟) 实现了与标准的1x速度图像相比的诊断效果.
模拟数据模型 (2x-simu) 在主观质量方面略高于真实数据模型 (2x-real),尽管两者都是有效的.
加速3倍速度扫描,即使使用DL增强,与1x和2x速度相比,诊断性能也存在局限性.

结论:

使用DL增强的Ultrafast 2x WBS提供了与标准采集速度可比的诊断价值.
无论是在真实数据还是模拟数据上训练的DL模型,都能有效地提高加速WBS中的图像质量.
虽然模拟提供了一种可行的方法,但重要的是要注意,模拟算法可能无法完美反映真实临床数据的特征.

关键词:

深度学习是一种深度学习.诊断疗效诊断疗效诊断疗效诊断疗效斯佩克特 (Spectre) 是一个运动场.全身光束扫描全身光束扫描

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