在参数映射中用近似贝叶斯深度图像先验的Denoising和不确定性估计
Max Hellström1, Tommy Löfstedt1,2, Anders Garpebring1
1Department of Radiation Sciences, Umeå University, Umeå, Sweden.
Magnetic resonance in medicine
|August 15, 2023
概括
深度图像先验 (DIP) 成功地消除了参数映射,减少了医疗成像中的噪音和不确定性. 这种方法具有适应性,不需要训练数据,尽管计算时间较长,但提供了强大的方法.
科学领域:
- 医疗成像医学成像
- 计算神经科学是一种神经科学.
- 机器学习 机器学习
背景情况:
- 医学成像中的参数映射通常会受到噪音和高不确定性的影响.
- 传统的方法很难有效地否定这些参数图.
研究的目的:
- 通过使用深度图像先验 (DIP) 将参数映射作为否定任务来解决杂的参数地图和高不确定性.
- 为了将DIP的无色化能力扩展到组织参数地图生成.
主要方法:
- 通过将图像生成网络的输出作为组织参数图的参数化来利用深度图像先验 (DIP).
- 采用未经训练的卷积神经网络 (CNN) 进行隐式拒绝,过低级图像特征.
- 综合不确定性估计使用蒙特卡洛 (MC) 脱落用于voxel-wise不确定性量化.
- 开发了一个模块化方法,允许适应各种应用程序,如T1映射,T2映射和明显扩散系数映射.
主要成果:
- 在多个参数映射应用程序中证明了DIP的成功适应.
- 与传统技术相比,实现了显著的噪声降低和参数图的不确定性降低.
- 识别了延长的计算时间和潜在的偏差引入从先前的denoising作为限制.
结论:
- 深度图像先验 (DIP) 有效地拒绝参数映射,并且可以在各种场景中使用,最小调整.
- 由于没有培训数据要求,实现的方便性是一个关键优势.
- 虽然在计算上密集,但MC中断衍生的不确定性信息增强了稳定性,并在校准时提供了有价值的见解.
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