卷积网络用于加速多次射击扩散的MRI.
Or Alus1, Maria El Homsi2, Jennifer S Golia Pernicka2
1Department of Medical Physics, Memorial Sloan Kettering Cancer Center, New York, NY, USA.
Magnetic resonance imaging
|October 11, 2023
概括
这项研究引入了一个卷积神经网络,以消除扩散权重MRI (DWI) 图像,加速多次射击DWI获取. 这种方法显著减少了直肠癌成像扫描的扫描时间,同时保持了图像质量.
科学领域:
- 医疗成像医学成像
- 放射学 放射学是一门学科.
- 人工智能在医学中的应用
背景情况:
- 多射击回声平面成像提高了空间分辨率,并减少了扩散权重MRI (DWI) 中的几何扭曲.
- 然而,多次射击DWI需要更长的扫描时间,因为需要多次重复才能达到足够的信号噪声比,特别是在身体成像中.
- 加快DWI获取对于临床应用至关重要.
研究的目的:
- 开发和验证一个卷积神经网络 (CNN) 用于在多镜头DWI中拒绝高b值图像.
- 通过减少所需的重复次数,加速获得多次射击DWI.
- 为了评估CNN否认对直肠癌患者图像质量和几何文物减少的影响.
主要方法:
- 一个CNN模特被训练在单次射击的DWI数据上进行拒绝.
- 经过训练的CNN被应用来通过减少重复来加速2次射击DWI获取.
- 专家放射科医生使用定性得分来评估图像质量,并进行统计分析 (Wilcoxon签名等级测试) 来评估CNN报道的效果.
主要成果:
- 与临床标准相比,CNN提出的拒绝方法成功地将2次射击DWI的获取加快了4倍.
- 专家放射科医生的定性评估表明,加速和非加速采集之间的图像质量可比或改善.
- 在减少几何文物和增强空间分辨率方面,CNN的否认证明了有效性.
结论:
- 基于CNN的Denoising能够显著加速多拍摄DWI采集,而不会影响图像质量.
- 这种技术可以增加拍摄次数,从而减少了大量的几何文物,并提高了空间分辨率.
- 这些发现表明,CNN的否认可以提高多次射击DWI的临床实用性,用于身体成像,包括直肠癌诊断.
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