论文标题
非线性模糊成像：学习多参数组织映射而无需地面真相以进行压缩定量MRI
Nonlinear Equivariant Imaging: Learning Multi-Parametric Tissue Mapping without Ground Truth for Compressive Quantitative MRI
论文作者
论文摘要
从快速，压缩，磁共振指纹（MRF）中进行定量组织图（MRF）的当前最新重建，使用有监督的深度学习，这是需要高保真地面真相组织图训练数据的缺点。本文提出了一种非线性模糊成像（NLEI），这是一种自制的学习方法，旨在消除对深MRF图像重建的地面真理的需求。 NLEI将最近的模糊成像框架扩展到非线性反问题，例如MRF。仅使用快速压缩采样的MRF扫描进行培训。 NLEI使用时空先验学习组织映射：从MRF数据的不变性到一组几何图像转换，而空间先验获得，而时间先验是从预先训练的神经网络近似的非线性BLOCH响应模型中获得的。经过回顾性的两个采集设置，我们观察到，尽管在训练过程中没有使用地面真相，但NLEI（自学学习）（自学学习）与监督学习的表现紧密相关。
Current state-of-the-art reconstruction for quantitative tissue maps from fast, compressive, Magnetic Resonance Fingerprinting (MRF), use supervised deep learning, with the drawback of requiring high-fidelity ground truth tissue map training data which is limited. This paper proposes NonLinear Equivariant Imaging (NLEI), a self-supervised learning approach to eliminate the need for ground truth for deep MRF image reconstruction. NLEI extends the recent Equivariant Imaging framework to nonlinear inverse problems such as MRF. Only fast, compressed-sampled MRF scans are used for training. NLEI learns tissue mapping using spatiotemporal priors: spatial priors are obtained from the invariance of MRF data to a group of geometric image transformations, while temporal priors are obtained from a nonlinear Bloch response model approximated by a pre-trained neural network. Tested retrospectively on two acquisition settings, we observe that NLEI (self-supervised learning) closely approaches the performance of supervised learning, despite not using ground truth during training.
