具有结构图像共变率的VAE应用于压缩传感MRI
M A G Duff1, I J A Simpson2, M J Ehrhardt1
1Department of Mathematical Sciences, University of Bath, Bath, BA2 7AY, United Kingdom.
Physics in medicine and biology
|July 5, 2023
概括
生成模型为医学成像中的反向问题提供了强大的数据驱动的先验. 这种无监督的方法为MRI重建提供了灵活而有竞争力的规范化,适应不同噪音和采样模式.
科学领域:
- 医疗成像医学成像
- 计算科学 计算科学
- 机器学习 机器学习
背景情况:
- 医学成像中的反向问题需要有效的规范化.
- 学习规范化提供数据驱动的先验,但通常需要配对的训练数据.
- 无监督的方法允许灵活地改变前进的问题参数.
研究的目的:
- 调查生成模型作为逆问题的先验.
- 开发一种灵活,数据驱动的学习规范化方法.
- 保持对可变规范化方法的控制和洞察力.
主要方法:
- 使用变量自动编码器进行图像生成和不确定性估计.
- 结合共变矩阵来建模图像依赖的不确定性.
- 从快速MRI数据集中对子样本MRI数据进行生成调节剂的评估.
主要成果:
- 拟议的生成调节器与最先进的方法具有竞争力.
- 该方法在不同的采样模式和噪音水平上展示了一致的性能.
- 无监督学习方法允许适应未来问题的变化.
结论:
- 学习生成规范化为MRI中的反向问题提供了一种可行和灵活的方法.
- 无监督的生成先验可以有效地惩罚学习图像多重组之外的重建.
- 该方法对改善MRI重建质量和稳定性显示出希望.
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