高加速的CESTMRI使用频率抵消依赖的k空间采样和深度学习重建
Chuyu Liu1, Zhongsen Li1, Zhensen Chen2,3
1Center for Biomedical Imaging Research, Department of Biomedical Engineering, Tsinghua University, Beijing, China.
Magnetic resonance in medicine
|April 16, 2024
概括
一种新方法将频率抵消依赖 (FOD) 采样与部分可分离网络 (PSN) 结合起来,以实现更快的化学交换和转移 (CEST) MRI. 这种技术改善了图像重建,并可能有助于脑瘤诊断.
科学领域:
- 磁共振成像技术 磁共振成像技术
- 医学物理 医学物理
- 生物医学工程 生物医学工程
背景情况:
- 化学交换和转移 (CEST) MRI 是代谢成像的一个有价值的技术.
- 加快CEST获取对于临床应用至关重要,特别是在神经瘤学中.
- 目前的CEST方法在速度和重建准确性方面存在局限性.
研究的目的:
- 开发一种高度加速的CEST Z频谱采集方法.
- 实施一种新的k空间采样模式和基于深度学习的重建.
- 评估拟议方法在人类大脑数据集中的性能.
主要方法:
- 设计了一个定制的k空间采样模式,其频率偏移依赖 (FOD) 概率.
- 一个卷积神经网络 (CNN) 增强了一个部分可分离 (PS) 函数 (PSN) 用于重建.
- 对人类大脑数据集进行了回顾性和前性实验,包括健康受试者和脑瘤患者.
主要成果:
- 在量化指标 (nMSE,SSIM) 中,FOD采样与PSN (FOD+PSN) 结合的显著优于传统方法.
- 在加速因子 (4-14) 中,FOD+PSN 持续改进了四个对比图 (MTRasym,MTRrex,amide,NOE).
- 展望结果表明,对于脑瘤患者来说,加速CESTMRI的可行性.
结论:
- 结合FOD采样和PSN重建,可以实现极快的CESTMRI采集.
- 这种先进的技术显示了在脑瘤患者中促进CEST代谢MRI的潜力.
- 开发的方法为更快,更准确的代谢成像提供了一个有希望的方法.
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