用神经隐式表示实现自由呼吸下肝脏多参数精准成像
Implicit neural representation for free-breathing MR fingerprinting (INR-MRF): co-registered 3D whole-liver water T1, water T2, proton density fat fraction, and R2* mapping
- 结合4D与3D隐式神经表征，同步学习运动变形与静态图像
- 在10名健康人身上验证，各项参数与传统屏气扫描偏差极小
- 无需水脂分离后处理，适合临床快速无创肝病评估
目的：开发一种用于自由呼吸下3D全肝定量的MRI技术，测量水T1、水T2、质子密度脂肪分数（PDFF）和R2*。方法：设计了一种八回波扰动梯度回波脉冲序列，采用螺旋读出并交错注入反转恢复和T2磁化准备。提出基于4D与3D隐式神经表征（INR）的神经网络，分别学习运动形变场和静态参考帧MRI子空间图像。训练过程中直接分离水和脂肪信号，无需回顾性水脂分离。使用常规扫描生成的定量图作为参考，在10名健康受试者中验证了该方法生成的T1、T2、R2*和PDFF值。结果：与传统屏气扫描相比，肝脏内T1、T2、R2*和PDFF值的偏差最小，95%一致性界限狭窄。结论：INR-MRF实现了单次自由呼吸扫描下的共注册3D全肝T1、T2、R2*和PDFF成像。
原文摘要 · Abstract (English)
Purpose: To develop an MRI technique for free-breathing 3D whole-liver quantification of water T1, water T2, proton density fat fraction (PDFF), R2*. Methods: An Eight-echo spoiled gradient echo pulse sequence with spiral readout was developed by interleaving inversion recovery and T2 magnetization preparation. We propose a neural network based on a 4D and a 3D implicit neural representation (INR) which simultaneously learns the motion deformation fields and the static reference frame MRI subspace images respectively. Water and fat singular images were separated during network training, with no need of performing retrospective water-fat separation. T1, T2, R2* and proton density fat fraction (PDFF) produced by the proposed method were validated in vivo on 10 healthy subjects, using quantitative maps generated from conventional scans as reference. Results: Our results showed minimal bias and narrow 95% limits of agreement on T1, T2, R2* and PDFF values in the liver compared to conventional breath-holding scans. Conclusions: INR-MRF enabled co-registered 3D whole liver T1, T2, R2* and PDFF mapping in a single free-breathing scan.
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