对监督和无监督的深度学习策略进行比较评估,以消除超极化Xe肺MRI
Abdullah S Bdaiwi1, Matthew M Willmering1, Riaz Hussain1
1Division of Pulmonary Medicine, Center for Pulmonary Imaging Research, Cincinnati Children's Hospital Medical Center, Cincinnati, Ohio, USA.
Magnetic resonance in medicine
|August 14, 2025
概括
深度学习无声化通过增强信号对噪声比来显著提高超极化129XeMRI质量. 这一进步有助于更准确的肺功能评估和临床诊断.
科学领域:
- 磁共振成像技术 磁共振成像技术
- 医疗成像医学成像
- 肺部病理学 肺部病理学
背景情况:
- 在超极化129XeMRI中信号噪声比 (SNR) 较低,可能会阻碍研究和诊断的准确量化.
- 开发有效的排泄技术对于最大限度地提高129XeMRI的临床效用至关重要.
研究的目的:
- 评价监督深度学习 (DL) 无声化方法,包括传统的 (Trad),Noise2Noise (N2N) 和无监督的Noise2Void (N2V),用于129Xe MRI.
- 为了比较DL无声化技术与区块匹配3D方法的性能.
主要方法:
- 在952 129XeMRI数据集 (通风,扩散权重,气体交换) 上训练并测试了DL消噪框架.
- 使用SNR,噪声标准偏差和清晰度等指标评估图像质量.
- 评估的功能参数包括通风缺陷百分比 (VDP),明显扩散系数 (ADC) 和气体交换指标.
主要成果:
- 所有的无声化方法都显著降低了噪声,并改善了SNR (p < 0.05).
- DL方法在VDP量化中显示出不同的偏差,其中Block Matching 3D和N2V显示出最小的偏差.
- 排毒降低了ADC平均值和SD,并提高了气体交换指标的精度.
结论:
- 在129XeMRI中SNR低可能会阻碍临床诊断和肺功能评估.
- 监督和无监督的DL消噪方法提高了129Xe的成像质量.
- 这些DL技术有望改善129XeMRI的临床解释和诊断准确性.
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