从相应的早期扫描PET进行基于深度学习的延迟PET图像合成,用于剂量计吸收估计
Kangsan Kim1, Byung Hyun Byun2, Ilhan Lim2
1Division of Applied RI, Korea Institute of Radiological and Medical Sciences, Seoul 01812, Republic of Korea.
Diagnostics (Basel, Switzerland)
|October 14, 2023
概括
这项研究使用深度学习从早期扫描中创建延迟正子发射断层扫描 (PET) 图像,减少扫描时间. 人工智能模型准确地预测了健康受试者的器官放射性药物吸收.
科学领域:
- 医疗成像医学成像
- 放射性药物 放射性药物 放射性药物
- 人工智能的人工智能
背景情况:
- 放射性药物分发的正电子发射断层扫描 (PET) 成像是耗时的,因为剂量计需要多次扫描.
- 这需要大量的时间和参与者的不便.
研究的目的:
- 开发一种深度学习模型,能够从早期获得的图像中生成延迟的PET图像.
- 为了减少与传统的多时间点PET成像相关的时间和不便.
主要方法:
- 使用基于生成对抗网络 (GAN) 的配对图像对图像翻译模型.
- 18名健康参与者接受了[18F]氧糖注射.
- 该模型从早期扫描中生成延迟的PET图像,并将标准化吸收值 (SUV) 平均值与地面真相数据进行比较.
主要成果:
- 最小方形GAN和感知损失组合显示出最佳的性能.
- 随着早期图像吸收时间接近基准时间,观察到更好的翻译准确度.
- 对肌肉,心脏,肝脏和脏的SUV平均值以合理的准确度估计.
结论:
- 基于深度学习的图像对图像翻译对于从正常受试者的其他功能图像中生成功能性PET图像是有效的.
- 该模型可以预测特定正常器官的器官智能放射性药物活性.
- 这种方法提供了一个潜在的解决方案,以减轻PET成像中剂量计的时间成本.
关键词:
在18F-FDG中.深度学习是一种深度学习.诊断放射性药物 医疗药物 诊断放射性药物功能成像功能成像功能成像生成性的对抗性网络.图像对图像的翻译内部剂量计是指内部的剂量计.定子发射断层扫描 (PET).标准化吸收值是标准化的吸收值.更多相关视频
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