强大的多线圈MRI重建通过自我监督的无声化
Asad Aali1,2, Marius Arvinte3, Sidharth Kumar2
1Department of Radiology, Stanford University, Stanford, California.
Magnetic resonance in medicine
|June 2, 2025
概括
自主监督的denoising通过增强数据质量来改善基于深度学习 (DL) 的磁共振成像 (MRI) 重建. 这种预处理步骤导致更有效的DL网络,减少对无噪声训练数据的需求.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 信号处理 信号处理
背景情况:
- 基于深度学习 (DL) 的重建方法为磁共振成像 (MRI) 提供了高质量的图像.
- 训练这些DL方法通常需要大型,无噪声的数据集,这是不切实际的获取.
- 在MRI中使用的K空间数据本质上是杂的,通常是多线圈的.
研究的目的:
- 为了评估自我监督的denoising作为DL-basedMRI重建的预处理步骤的影响.
- 为了评估无声化如何影响DL方法,训练在杂的K空间数据上.
- 确定除尘能否提高重建质量和效率.
主要方法:
- 杆化通用化斯坦的无偏风险估计 (GSURE) 用于自我监督的拒绝.
- 评估了两个DL重建方法:扩散概率模型 (DPM) 和基于模型的深度学习 (MoDL).
- 在加速的多线圈MRI重建中使用T2加权的大脑和脂肪抑制的质子密度膝盖扫描在各种信号噪声比 (SNR) 上进行了测试.
主要成果:
- 自主监督的消毒显著提高了MRI重建的质量和效率.
- 训练DL网络与无效数据导致较低的正常化根平均平方误差 (NRMSE).
- 否认数据导致不同SNR的结构相似度指数 (SSIM) 和峰值信号噪声比率 (PSNR) 更高.
结论:
- 剥离是有效的DL-based MRI重建的关键预处理技术.
- 通过消除噪音来提高输入数据质量,可以培养更高效的DL网络.
- 这种方法可以消除为培训而购买无噪声参考MRI扫描的必要性.
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