在没有完全采样数据的情况下,使用自我校准的消极剂进行MRI恢复
Muhammad Shafique1,2, Sizhuo Liu1, Philip Schniter3
1Biomedical Engineering, Ohio State University, Columbus, OH, 43210, USA.
Magma (New York, N.Y.)
|October 16, 2024
概括
我们开发了ReSiDe,这是一种自主监督的磁共振成像 (MRI) 重建方法,可以从不足样本的数据中恢复图像,而不需要完全样本的训练数据集. 在静态和动态MRI应用中,ReSiDe的性能优于现有的方法.
科学领域:
- 医疗成像医学成像
- 机器学习 机器学习
- 图像重建 图像的重建
背景情况:
- 获取用于磁共振成像 (MRI) 应用的完全采样训练数据通常是困难的.
- 现有的方法可能需要广泛的,完全采样的数据集来训练图像重建模型.
研究的目的:
- 介绍ReSiDe,一种用于MRI的新型自主监督图像重建方法.
- 为了使图像仅从不足样本的数据中恢复,克服训练数据的限制.
主要方法:
- ReSiDe的灵感来源于插即用 (PnP) 方法,但在重建过程中反复训练denoiser.
- 开发和比较了两个变体,ReSiDe-S (扫描特定) 和ReSiDe-M (多组).
- 对T1/T2加权的大脑MRI,MRXCAT幻影和心脏输液数据进行了评估.
主要成果:
- 与其他自我监督/无监督方法相比,ReSiDe-S和ReSiDe-M显示出更高的性能.
- 优异性被测量为静态成像的峰值信号噪声比和结构相似性指数.
- 专家评分证实了动态心脏输液成像中的有效性.
结论:
- ReSiDe是一种经过验证的自主监督图像重建技术,用于静态和动态MRI.
- 这种方法显著有利于MRI应用程序,这些应用程序对完全采样的培训数据的访问有限.
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