Department of Biomedical Engineering, Vanderbilt University, Nashville, Tennessee.
Institute of Imaging Science, Vanderbilt University Medical Center, Nashville, Tennessee.
Magn Reson Med. 2019 Jun;81(6):3503-3514. doi: 10.1002/mrm.27658. Epub 2019 Feb 5.
Multi-exponential relaxometry is a powerful tool for characterizing tissue, but generally requires high image signal-to-noise ratio (SNR). This work evaluates the use of principal-component-analysis (PCA) denoising to mitigate these SNR demands and improve the precision of relaxometry measures.
PCA denoising was evaluated using both simulated and experimental MRI data. Bi-exponential transverse relaxation signals were simulated for a wide range of acquisition and sample parameters, and experimental data were acquired from three excised and fixed mouse brains. In both cases, standard relaxometry analysis was performed on both original and denoised image data, and resulting estimated signal parameters were compared.
Denoising reduced the root-mean-square-error of parameters estimated from multi-exponential relaxometry by factors of ≈3×, for typical acquisition and sample parameters. Denoised images and subsequent parameter maps showed little or no signs of spatial artifact or loss of resolution.
Experimental studies and simulations demonstrate that PCA denoising of MRI relaxometry data is an effective method of improving parameter precision without sacrificing image resolution. This simple yet important processing step thus paves the way for broader applicability of multi-exponential MRI relaxometry.
多指数弛豫定量分析是一种强大的组织特征描述工具，但通常需要较高的图像信噪比（SNR）。本研究旨在评估主成分分析（PCA）去噪在减轻 SNR 需求和提高弛豫定量测量精度方面的作用。
采用模拟和实验 MRI 数据评估 PCA 去噪。针对广泛的采集和样本参数，模拟了双指数横向弛豫信号，从三个离体固定的小鼠脑中采集了实验数据。在这两种情况下，分别对原始图像数据和去噪图像数据进行标准弛豫定量分析，并比较了得到的估计信号参数。
对于典型的采集和样本参数，去噪将多指数弛豫定量分析估计参数的均方根误差降低了约 3 倍。去噪后的图像及其随后的参数图几乎没有或没有空间伪影或分辨率损失的迹象。
实验研究和模拟表明，MRI 弛豫定量数据分析的 PCA 去噪是一种在不牺牲图像分辨率的情况下提高参数精度的有效方法。因此，这一简单而重要的处理步骤为更广泛地应用多指数 MRI 弛豫定量分析铺平了道路。
