NeuroSpin, CEA/DSV/I2BM, Gif-sur-Yvette, France; IFR 49, Paris, France.
Magn Reson Imaging. 2013 Oct;31(8):1360-71. doi: 10.1016/j.mri.2013.04.002. Epub 2013 May 6.
Parallel magnetic resonance imaging (MRI) yields noisy magnitude data, described in most cases as following a noncentral χ distribution when the signals received by the coils are combined as the sum of their squares. One well-known case of this noncentral χ noise model is the Rician model, but it is only valid in the case of single-channel acquisition. Although the use of parallel MRI is increasingly common, most of the correction methods still perform Rician noise removal, yielding an erroneous result due to an incorrect noise model. Moreover, the existence of noise correlations in phased array systems renders noise nonstationary and further modifies the noise description in parallel MRI. However, the noncentral χ model has been demonstrated to work as a good approximation as long as effective voxelwise parameters are used. A good correction step, adapted to the right noise model, is of paramount importance, especially when working with diffusion-weighted MR data, whose signal-to-noise ratio is low. In this paper, we present a noise removal technique designed to be fast enough for integration into a real-time reconstruction system, thus offering the convenience of obtaining corrected data almost instantaneously during the MRI scan. Our method employs the noncentral χ noise model and uses a simplified method to account for noise correlations; this leads to an efficient and rapid correction. The method consists of an anisotropic extension of the Linear Minimum Mean Square Error estimator (LMMSE) that is a far better edge-preserving method than the traditional LMMSE and addresses noncentral χ distributions along with empirically computed global effective parameters. The results on simulated and real data demonstrate that this anisotropic extended LMMSE outperforms the original LMMSE on images corrupted by noncentral χ noise. Moreover, in comparison with the existing LMMSE technique incorporating the estimation of voxelwise effective parameters, our method yields improved results.
并行磁共振成像（MRI）会产生噪声幅度数据，在大多数情况下，当线圈接收到的信号被组合为其平方和时，这些数据符合非中心 χ 分布。这种非中心 χ 噪声模型的一个著名例子是瑞利模型，但它仅在单通道采集的情况下有效。尽管并行 MRI 的使用越来越普遍，但大多数校正方法仍在执行瑞利噪声消除，由于噪声模型不正确，会产生错误的结果。此外，相控阵系统中的噪声相关性会使噪声非平稳，并进一步修改并行 MRI 中的噪声描述。然而，只要使用有效的体素参数，非中心 χ 模型已被证明是一个很好的近似。一个好的校正步骤，适应正确的噪声模型，是至关重要的，特别是在处理扩散加权磁共振数据时，其信噪比很低。在本文中，我们提出了一种噪声消除技术，旨在足够快地集成到实时重建系统中，从而在 MRI 扫描过程中几乎即时获得校正数据的便利性。我们的方法采用非中心 χ 噪声模型，并使用简化方法来考虑噪声相关性；这导致了一种高效快速的校正。该方法由线性最小均方误差估计器（LMMSE）的各向异性扩展组成，它是一种比传统 LMMSE 更好的边缘保持方法，可以解决非中心 χ 分布以及经验计算的全局有效参数问题。在模拟和真实数据上的结果表明，这种各向异性扩展的 LMMSE 在受非中心 χ 噪声污染的图像上的性能优于原始 LMMSE。此外，与现有的包含体素有效参数估计的 LMMSE 技术相比，我们的方法产生了更好的结果。
