Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels.
Dictionary-Free MRI PERK: Parameter Estimation via Regression with Kernels.
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DOI：
10.1109/tmi.2018.2817547
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发表时间：
2018-09
影响因子：
10.6
通讯作者：
Fessler JA
中科院分区：
文献类型：
--
作者：
Nataraj G;Nielsen JF;Scott C;Fessler JA
本文介绍了一种快速、通用的基于核函数回归（PERK）的定量磁共振成像（QMRI）无字典参数估计方法。PERK首先使用先验分布和非线性MR信号模型来模拟许多参数测量对。受机器学习的启发，PERK将这些参数-测量值对作为标记的训练点，并使用核函数和凸优化从中学习非线性回归函数。PERK承认一个简单的实现，即MRI测量值的每体素非线性提升，然后是线性最小均方误差回归。我们展示了PERK的T1，T2估计，一个研究得很好的应用程序，它是简单的比较PERK估计对基于字典的网格搜索估计和迭代优化估计。数值模拟以及单层体模和体内实验表明，PERK和其他测试方法在白色和灰质中产生相当的T1，T2估计值，但PERK始终至少快140倍。对于涉及每个体素更多潜在参数的全体积QMRI估计问题，该加速因子可以增加几个数量级。
This paper introduces a fast, general method for dictionary-free parameter estimation in quantitative magnetic resonance imaging (QMRI) via regression with kernels (PERK). PERK first uses prior distributions and the nonlinear MR signal model to simulate many parameter-measurement pairs. Inspired by machine learning, PERK then takes these parameter-measurement pairs as labeled training points and learns from them a nonlinear regression function using kernel functions and convex optimization. PERK admits a simple implementation as per-voxel nonlinear lifting of MRI measurements followed by linear minimum mean-squared error regression. We demonstrate PERK for T1, T2 estimation, a well-studied application where it is simple to compare PERK estimates against dictionary-based grid search estimates and iterative optimization estimates. Numerical simulations as well as single-slice phantom and in vivo experiments demonstrate that PERK and other tested methods produce comparable T1, T2 estimates in white and gray matter, but PERK is consistently at least 140× faster. This acceleration factor may increase by several orders of magnitude for full-volume QMRI estimation problems involving more latent parameters per voxel.