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基于深度学习的洛伦兹拟合水和转移在MRI中的参考光谱
Sajad Mohammed Ali1, Nirbhay N Yadav2,3, Ronnie Wirestam1
1Department of Medical Radiation Physics, Lund University, Lund, Sweden.
Magnetic resonance in medicine
|June 6, 2023
概括
一种新的深度学习方法,sLoFNet,在MRI中提供了较快,更强大的水和转移参考 (WASSR) Z光谱分析,与传统的最小平方拟合相比,特别是在杂的条件下.
科学领域:
- 磁共振成像是一种磁共振成像技术.
- 医疗成像中的人工智能
- 生物医学信号处理
背景情况:
- 水和转移参考 (WASSR) Z光谱对于化学交换和转移 (CEST) MRI 领域参考至关重要.
- 传统的最小平方 (LS) 洛伦兹拟合用于WASSR分析是缓慢的,易受错误由于体内噪声.
研究的目的:
- 引入基于深度学习的方法,即单一的洛伦兹拟合网络 (sLoFNet),以克服LS适合WASSR Z光谱的局限性.
- 在准确性,强度和速度方面评估sLoFNet与LS配件的性能.
主要方法:
- 开发和优化了一个神经网络架构 (sLoFNet).
- 该网络在模拟和体内配对数据集上进行了训练.
- 使用模拟和体内3T大脑WASSRMRI数据,sLoFNet的性能与LS配件进行了比较.
主要成果:
- sLoFNet和LS配件在体内数据上显示了可比的准确性 (RMS误差,平均绝对误差).
- sLoFNet表现出优越的抗噪强度,只有边际误差增加了高达4.5%的噪声,与LS不同.
- sLoFNet对降低的Z-光谱采样密度具有显著的弹性,并且比LS快70倍.
结论:
- sLoFNet为WASSRMRI Z光谱分析提供了相对于LS配件的显著优势.
- 深度学习方法提供了对噪音的增强稳定性和减少采样,并节省了大量时间.
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