从使用深度学习的MR多任务空间因素中直接合成多对比的大脑MRI图像
Shihan Qiu1,2, Sen Ma1, Lixia Wang1
1Biomedical Imaging Research Institute, Cedars-Sinai Medical Center, Los Angeles, California, USA.
Magnetic resonance in medicine
|May 29, 2023
概括
一种新的深度学习方法从MRI多任务空间因子中合成脑MRI对比加权图像. 这种技术可以在一次扫描中同时获取定量地图和加权图像,从而提高效率.
科学领域:
- 医疗成像医学成像
- 人工智能的人工智能
- 神经科学是一个神经科学.
背景情况:
- 传统的MRI测序需要多次扫描以获得不同的图像对比度.
- MR多任务处理允许同时获取定量图 (T1,T2,T1ρ),但合成传统加权图像是具有挑战性的.
研究的目的:
- 开发一种深度学习 (DL) 方法,从MR多任务空间因素中合成常规对比权重的大脑MRI图像.
- 为了评估基于DL的合成与基于布洛赫方程的合成的性能.
主要方法:
- 使用MR多任务空间因子训练了一个2D U-Net神经网络,以合成T1 MPRAGE,T1梯度回声和T2流体减弱反转恢复图像.
- 18名受试者接受了全脑定量T1-T2-T1ρMRI多任务成像.
- 量化指标 (NRMSE,PSNR,SSIM) 和放射科医生的图像质量评级被用于评估.
主要成果:
- 基于DL的合成图像显示了与真实采集相比较的对比度,并且优于基于Bloch方程的合成.
- 与基于布洛赫方程的方法 (p<0.05) 相比,DL合成实现了明显更好的定量指标 (NRMSE=0.184±0.075,PSNR=28.14±2.51,SSIM=0.918±0.034).
- 放射科医生发现DL合成的图像与真实采集相比没有显著的质量下降,比基于布洛赫方程的合成更好.
结论:
- 一种DL技术有效地从MR多任务空间因子中合成常规加权的MRI图像.
- 该方法允许在单个MRI扫描中同时获取多参数定量图和临床对比权重图像.
- DL方法为脑MRI采集提供了更有效和更强大的解决方案.
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