Parallel MR image reconstruction using augmented Lagrangian methods.
Parallel MR image reconstruction using augmented Lagrangian methods.
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DOI：
10.1109/tmi.2010.2093536
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发表时间：
2011-03
影响因子：
10.6
通讯作者：
Fessler JA
中科院分区：
文献类型：
--
作者：
Ramani S;Fessler JA
利用灵敏度编码（SENSE）重建磁共振图像需要进行正则化以抑制噪声和混叠效应。边缘保持和基于稀疏性的正则化准则可以提高图像质量，但它们需要计算密集型的非线性优化。在本文中，我们提出了一种新的方法，利用增强拉格朗日（AL）框架来解决大规模约束优化问题，从欠采样灵敏度编码数据中进行正则化MRI重构。我们首先将正则化的感官重构表述为无约束优化任务，然后使用变量拆分将其转换为一组（等效的）约束问题。然后，我们使用交替最小化方法在人工智能框架中攻击这些约束版本，从而导致可以轻松实现的算法。所提出的方法适用于一般类型的正则化，包括流行的边缘保持（例如，全变分）和稀疏性提升（例如，小波系数的1-范数）准则及其组合。人工合成和人体数据的数值实验表明，人工智能算法比非线性共轭梯度（NCG）和最先进的MFISTA方法等通用优化算法收敛速度更快。
Magnetic resonance image (MRI) reconstruction using SENSitivity Encoding (SENSE) requires regularization to suppress noise and aliasing effects. Edge-preserving and sparsity-based regularization criteria can improve image quality, but they demand computation-intensive nonlinear optimization. In this paper, we present novel methods for regularized MRI reconstruction from undersampled sensitivity encoded data—SENSE-reconstruction—using the augmented Lagrangian (AL) framework for solving large-scale constrained optimization problems. We first formulate regularized SENSE-reconstruction as an unconstrained optimization task and then convert it to a set of (equivalent) constrained problems using variable splitting. We then attack these constrained versions in an AL framework using an alternating minimization method, leading to algorithms that can be implemented easily. The proposed methods are applicable to a general class of regularizers that includes popular edge-preserving (e.g., total-variation) and sparsity-promoting (e.g., ℓ1-norm of wavelet coefficients) criteria and combinations thereof. Numerical experiments with synthetic and in-vivo human data illustrate that the proposed AL algorithms converge faster than both general-purpose optimization algorithms such as nonlinear conjugate gradient (NCG) and state-of-the-art MFISTA method.