Manzano-Patron Jose-Pedro, Moeller Steen, Andersson Jesper L R, Ugurbil Kamil, Yacoub Essa, Sotiropoulos Stamatios N
Sir Peter Mansfield Imaging Centre, School of Medicine, University of Nottingham, UK.
Center for Magnetic Resonance Research, University of Minnesota, USA.
bioRxiv. 2023 Nov 2:2023.07.24.550348. doi: 10.1101/2023.07.24.550348.
Development of diffusion MRI (dMRI) denoising approaches has experienced considerable growth over the last years. As noise can inherently reduce accuracy and precision in measurements, its effects have been well characterised both in terms of uncertainty increase in dMRI-derived features and in terms of biases caused by the noise floor, the smallest measurable signal given the noise level. However, gaps in our knowledge still exist in objectively characterising dMRI denoising approaches in terms of both of these effects and assessing their efficacy. In this work, we reconsider what a denoising method should and should not do and we accordingly define criteria to characterise the performance. We propose a comprehensive set of evaluations, including i) benefits in improving signal quality and reducing noise variance, ii) gains in reducing biases and the noise floor and improving, iii) preservation of spatial resolution, iv) agreement of denoised data against a gold standard, v) gains in downstream parameter estimation (precision and accuracy), vi) efficacy in enabling noise-prone applications, such as ultra-high-resolution imaging. We further provide newly acquired complex datasets (magnitude and phase) with multiple repeats that sample different SNR regimes to highlight performance differences under different scenarios. Without loss of generality, we subsequently apply a number of exemplar patch-based denoising algorithms to these datasets, including Non-Local Means, Marchenko-Pastur PCA (MPPCA) in the magnitude and complex domain and NORDIC, and compare them with respect to the above criteria and against a gold standard complex average of multiple repeats. We demonstrate that all tested denoising approaches reduce noise-related variance, but not always biases from the elevated noise floor. They all induce a spatial resolution penalty, but its extent can vary depending on the method and the implementation. Some denoising approaches agree with the gold standard more than others and we demonstrate challenges in even defining such a standard. Overall, we show that dMRI denoising performed in the complex domain is advantageous to magnitude domain denoising with respect to all the above criteria.
在过去几年中，扩散磁共振成像（dMRI）去噪方法有了显著发展。由于噪声会从本质上降低测量的准确性和精度，其影响在dMRI衍生特征的不确定性增加以及由噪声基底（给定噪声水平下可测量的最小信号）引起的偏差方面都得到了很好的表征。然而，在根据这两种影响客观地表征dMRI去噪方法并评估其有效性方面，我们的知识仍存在差距。在这项工作中，我们重新审视了去噪方法应该做什么和不应该做什么，并相应地定义了表征性能的标准。我们提出了一套全面的评估方法，包括：i）在提高信号质量和降低噪声方差方面的益处；ii）在减少偏差和噪声基底以及改善方面的收获；iii）空间分辨率的保持；iv）去噪后的数据与金标准的一致性；v）在下游参数估计（精度和准确性）方面的收获；vi）在实现易受噪声影响的应用（如超高分辨率成像）方面的有效性。我们还提供了新获取的具有多个重复的复杂数据集（幅度和相位），这些数据集对不同的信噪比区域进行采样，以突出不同场景下的性能差异。在不失一般性的情况下，我们随后将一些基于示例块的去噪算法应用于这些数据集，包括非局部均值、幅度和复域中的马尔琴科 - 帕斯图尔主成分分析（MPPCA）以及NORDIC，并根据上述标准以及与多个重复的金标准复平均值进行比较。我们证明，所有测试的去噪方法都能降低与噪声相关的方差，但并非总能减少来自升高的噪声基底的偏差。它们都会导致空间分辨率的损失，但其程度会因方法和实现方式而异。一些去噪方法与金标准的一致性比其他方法更好，并且我们证明了定义这样一个标准甚至也存在挑战。总体而言，我们表明，就上述所有标准而言，在复域中进行的dMRI去噪比幅度域去噪更具优势。
