Consistency of the predictions with respect to the physical forward model is
pivotal for reliably solving inverse problems. This consistency is mostly
un-controlled in the current end-to-end deep learning methodologies proposed
for the Magnetic Resonance Fingerprinting (MRF) problem. To address this, we
propose ProxNet, a learned proximal gradient descent framework that directly
incorporates the forward acquisition and Bloch dynamic models within a
recurrent learning mechanism. The ProxNet adopts a compact neural proximal
model for de-aliasing and quantitative inference, that can be flexibly trained
on scarce MRF training datasets. Our numerical experiments show that the
ProxNet can achieve a superior quantitative inference accuracy, much smaller
storage requirement, and a comparable runtime to the recent deep learning MRF
baselines, while being much faster than the dictionary matching schemes. Code
has been released at this https URL

针对磁共振指纹 (MRF) 问题的端到端深度学习方法中预测与物理正向模型的一致性大多未受控制，本文提出了一种名为 ProxNet 的学习的近端梯度下降框架，它直接将前向采集和布洛赫动力学模型纳入到循环学习机制中，使用紧凑的神经近端模型用于去混淆和定量推断，可以灵活地训练稀缺的 MRF 训练数据集。数值实验表明，ProxNet 可以达到卓越的定量推断精度，更小的存储需求，与最新的深度学习 MRF 基线相当的运行时间，同时比字典匹配方案快得多。

神经接近梯度迭代下的压缩 MR 指纹重建

Compressive MR Fingerprinting reconstruction with Neural Proximal  Gradient iterations

Magnetic Resonance Fingerprinting (MRF) is a new approach to quantitative
magnetic resonance imaging that allows simultaneous measurement of multiple
tissue properties in a single, time-efficient acquisition. Standard MRF
reconstructs parametric maps using dictionary matching and lacks scalability
due to computational inefficiency. We propose to perform MRF map reconstruction
using a recurrent neural network, which exploits the time-dependent information
of the MRF signal evolution. We evaluate our method on multiparametric
synthetic signals and compare it to existing MRF map reconstruction approaches,
including those based on neural networks. Our method achieves state-of-the-art
estimates of T1 and T2 values. In addition, the reconstruction time is
significantly reduced compared to dictionary-matching based approaches.

本文提出使用循环神经网络进行磁共振指纹重建方法，比传统字典匹配法具有更高的可扩展性和更短的重建时间，能够同时测量多种组织属性的定量成像方法。

利用循环神经网络的磁共振指纹技术

Magnetic Resonance Fingerprinting using Recurrent Neural Networks

Inspired by the recently proposed Magnetic Resonance Fingerprinting (MRF)
technique, we develop a principled compressed sensing framework for
quantitative MRI. The three key components are: a random pulse excitation
sequence following the MRF technique; a random EPI subsampling strategy and an
iterative projection algorithm that imposes consistency with the Bloch
equations. We show that theoretically, as long as the excitation sequence
possesses an appropriate form of persistent excitation, we are able to
accurately recover the proton density, T1, T2 and off-resonance maps
simultaneously from a limited number of samples. These results are further
supported through extensive simulations using a brain phantom.

使用 Magnetic Resonance Fingerprinting (MRF) 技术，结合随机脉冲激发序列、随机 EPI 子采样策略和迭代投影算法，开发出一种压缩感知框架，能够从有限数量样本中同时准确恢复质子密度、T1、T2 和离共振映射。这些结果经过对大量仿真数据的验证。