Brion Véronique, Poupon Cyril, Riff Olivier, Aja-Fernández Santiago, Tristán-Vega Antonio, Mangin Jean-François, Le Bihan Denis, Poupon Fabrice
CEA I2BM NeuroSpin, Gif-sur-Yvette, France.
Med Image Comput Comput Assist Interv. 2011;14(Pt 2):226-33. doi: 10.1007/978-3-642-23629-7_28.
Parallel MRI leads to magnitude data corrupted by noise described in most cases as following a Rician or a non central chi distribution. And yet, very few correction methods perform a non central chi noise removal. However, this correction step, adapted to the correct noise model, is of very much importance, especially when working with Diffusion Weighted MR data yielding a low SNR. We propose an extended Linear Minimum Mean Square Error estimator (LMMSE), which is adapted to deal with non central chi distributions. We demonstrate on simulated and real data that the extended LMMSE outperforms the original LMMSE on images corrupted by a non central chi noise.
并行磁共振成像会导致幅度数据被噪声破坏，在大多数情况下，这种噪声遵循莱斯分布或非中心卡方分布。然而，很少有校正方法能去除非中心卡方噪声。不过，这一适应正确噪声模型的校正步骤非常重要，尤其是在处理扩散加权磁共振数据且信噪比很低的情况下。我们提出了一种扩展的线性最小均方误差估计器（LMMSE），它适用于处理非中心卡方分布。我们在模拟数据和真实数据上证明，在被非中心卡方噪声破坏的图像上，扩展LMMSE的性能优于原始LMMSE。
