一种深度学习方法来估计数字乳腺图解合成中的X射线散射:从幻影模型到临床应用
Marta C Pinto1, Franziska Mauter1,2, Koen Michielsen1
1Dept. of Medical Imaging, Radboud University Medical Center, Nijmegen, The Netherlands.
Medical physics
|July 3, 2023
概括
这项研究引入了一种快速而准确的深度学习 (DL) 方法,用于预测数字乳腺图解合成 (DBT) 投影中的散射辐射. 这种方法显著提高了图像质量和定量准确性,以更好地检测乳腺癌.
科学领域:
- 医学成像物理 医学成像物理
- 计算成像技术的成像
- 医疗保健中的人工智能
背景情况:
- 数字乳房断层合成 (DBT) 提供了比数字造乳术更好的准确性,但与散射辐射作斗争,影响图像质量和定量准确性.
- 深度学习 (DL) 显示了DBT中分散校正的希望,可能与蒙特卡洛 (MC) 模拟性能相匹配.
研究的目的:
- 开发一个DL模型来预测DBT投影中的散射辐射信号.
- 为了达到临床上可接受的预测时间,只使用随时可用的临床数据 (例如,乳腺厚度,获得角度).
主要方法:
- 训练并验证了一种DL模型,使用MC模拟的均和人形乳房幽灵的散射和初级地图.
- 通过使用定量指标 (MRD,MARD) 和散射对初级比率 (SPR) 对MC基本真相进行DL散射预测的比较.
- 在临床数据集上对线性衰减精度和视觉质量进行评估的分散校正的DBT重建.
主要成果:
- DL模型在预测散射辐射方面取得了很高的准确性,均MRD为同质幻影的0.05%,人类形态幻影的0.21%.
- SPR值在公布范围的±15%以内,视觉评估显示DL和MC散射估计之间的密切一致.
- 散射校正提高了线性衰减的准确性,在幻影中将错误从-16%降低到-2.3%,在临床案例中降低到-4.4%.
- DL模型训练需要40分钟,单个投影预测时间小于0.01秒.
结论:
- 开发的基于DL的方法为DBT预测提供了快速而准确的散射信号估计.
- 这种技术有可能提高乳腺成像中的定量应用.
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