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一种用于全身动态PET成像的深度学习方法,采用双时间窗口协议

Wenxiang Ding^1,2,3, Hanzhong Wang^1,2,4, Xiaoya Qiao^1,2

¹School of Biomedical Engineering, Shanghai Jiao Tong University, Shanghai, 200240, China.

European journal of nuclear medicine and molecular imaging

|December 17, 2024

概括

一个新的深度学习算法准确地从缩短的双时间窗口协议中重建全动态定子发射断层扫描 (PET) 扫描. 这一进步大大减少了扫描时间,克服了临床PET采用的关键障碍.

科学领域:

医疗成像医学成像
人工智能在医学中的应用
核医学就是核医学.

背景情况:

长时间的扫描时间是动态 pozitron发射断层扫描 (PET) 的临床使用的一个主要限制.
开发方法来缩短PET扫描时间对于改善患者吞吐量和可访问性至关重要.

研究的目的:

开发和评估一种深度学习算法,用于从降低采集协议中预测动态PET图像.
为了显著减少PET扫描时间,同时保持诊断图像质量.

主要方法:

开发了一种带有注意力机制的双向序列对序列模型 (Bi-AT-Seq2Seq),以预测动态PET.
从65分钟的全身[18F]FDG PET/CT扫描中模拟的早期停止和双时间窗口协议,对70名患有肺或乳腺结节的患者进行了PET/CT扫描.
对动力参数使用平均绝对误差 (MAE),偏差,峰值信号噪声比 (PSNR) 和结构相似性 (SSIM) 以及对应相关系数 (CCC) 来评估性能.

主要成果:

在所有评估的指标中,Bi-AT-Seq2Seq模型显著优于单向和非注意模型.
双时间窗口协议 (10分钟早期+5分钟后期扫描) 与15分钟早期停止协议相比,预测指标提高了高达37.31%.
使用恢复的全时活动曲线估计的瘤动态参数与缩短曲线相比显示出更高的一致性.

结论:

开发的深度学习算法准确地从缩短的15分钟双时间窗口协议中生成完整的65分钟动态PET获取.
这种方法提供了一种可行的解决方案,可以减少PET扫描时间,提高临床适用性.

关键词:

深度学习是一种深度学习.双时间窗口协议.动态PET是一种动态PET.动态建模动态建模短期收购短期收购

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相关概念视频

Positron Emission Tomography

Positron Emission Tomography

Positron emission tomography (PET) is a medical imaging technique involving radiopharmaceuticals — substances that emit short-lived radiation. Although the first PET scanner was introduced in 1961, it took 15 more years before radiopharmaceuticals were combined with the technique and revolutionized its potential.
One of the main requirements of a PET scan is a positron-emitting radioisotope, which is produced in a cyclotron and then attached to a substance used by the part of the body...