Henriques Rafael Neto, Ianuş Andrada, Novello Lisa, Jovicich Jorge, Jespersen Sune N, Shemesh Noam
Champalimaud Research, Champalimaud Foundation, Lisbon, Portugal.
Center for Mind/Brain Sciences - CIMeC, University of Trento, Rovereto, Italy.
Imaging Neurosci (Camb). 2023 Dec 18;1. doi: 10.1162/imag_a_00049. eCollection 2023.
Marčenko-Pastur PCA (MPPCA) denoising is emerging as an effective means for noise suppression in MR imaging (MRI) acquisitions with redundant dimensions. However, MPPCA performance can be severely compromised by spatially correlated noise-an issue typically affecting most modern MRI acquisitions-almost to the point of returning the original images with little or no noise removal. In this study, we explore different threshold criteria for principal component analysis (PCA) component classification that enable efficient and robust denoising of MRI data even when noise exhibits high spatial correlations, especially in cases where data are acquired with Partial Fourier and when only magnitude data are available. We show that efficient denoising can be achieved by incorporating a-priori information about the noise variance into PCA denoising thresholding. Based on this, two denoising strategies developed here are: 1) General PCA (GPCA) denoising that uses a-priori noise variance estimates without assuming specific noise distributions; and 2) Threshold PCA (TPCA) denoising which removes noise components with a threshold computed from a-priori estimated noise variance to determine the upper bound of the Marčenko-Pastur (MP) distribution. These strategies were tested in simulations with known ground truth and applied for denoising diffusion MRI data acquired using pre-clinical (16.4T) and clinical (3T) MRI scanners. In synthetic phantoms, MPPCA denoising failed to denoise spatially correlated data, while GPCA and TPCA better classified components as dominated by signal/noise. In cases where the noise variance was not accurately estimated (as can be the case in many practical scenarios), TPCA still provides excellent denoising performance. Our experiments in pre-clinical diffusion data with highly corrupted by spatial correlated noise revealed that both GPCA and TPCA robustly denoised the data while MPPCA denoising failed. In diffusion MRI data acquired on a clinical scanner in healthy subjects, MPPCA weakly removed noised, while TPCA was found to have the best performance, likely due to misestimations of the noise variance. Thus, our work shows that these novel denoising approaches can strongly benefit future pre-clinical and clinical MRI applications.
马尔琴科 - 帕斯特尔主成分分析（MPPCA）去噪正成为一种在具有冗余维度的磁共振成像（MRI）采集中抑制噪声的有效手段。然而，MPPCA的性能可能会因空间相关噪声而严重受损，这一问题通常影响大多数现代MRI采集，几乎到了返回原始图像且几乎没有去除噪声的程度。在本研究中，我们探索了用于主成分分析（PCA）成分分类的不同阈值标准，即使噪声表现出高空间相关性，尤其是在使用部分傅里叶采集数据且仅提供幅度数据的情况下，也能实现对MRI数据的高效且稳健的去噪。我们表明，通过将关于噪声方差的先验信息纳入PCA去噪阈值处理，可以实现高效去噪。基于此，这里开发的两种去噪策略是：1）通用PCA（GPCA）去噪，它使用先验噪声方差估计，而不假设特定的噪声分布；2）阈值PCA（TPCA）去噪，它使用根据先验估计的噪声方差计算的阈值去除噪声成分，以确定马尔琴科 - 帕斯特尔（MP）分布的上限。这些策略在具有已知真实情况的模拟中进行了测试，并应用于对使用临床前（16.4T）和临床（3T）MRI扫描仪采集的扩散MRI数据进行去噪。在合成体模中，MPPCA去噪无法对空间相关数据进行去噪，而GPCA和TPCA能更好地将成分分类为以信号/噪声为主导。在噪声方差未准确估计的情况下（在许多实际场景中可能如此）TPCA仍然提供了出色的去噪性能。我们在临床前扩散数据中进行的实验，该数据因空间相关噪声而严重受损，结果表明GPCA和TPCA都能稳健地对数据进行去噪，而MPPCA去噪失败。在健康受试者的临床扫描仪上采集的扩散MRI数据中，MPPCA对噪声的去除效果不佳，而TPCA被发现具有最佳性能，这可能是由于噪声方差的错误估计。因此，我们的工作表明，这些新颖的去噪方法可以极大地惠及未来的临床前和临床MRI应用。
