Random Matrix Theory-Based Noise Removal in MRI

项目总结 MRI是一种广泛使用的成像方式，它提供独特的软组织对比度，并提供 解剖和功能信息。然而，MRI固有的速度慢且信噪比(SNR)有限， 从而导致不同的诊断图像质量并限制研究的统计能力。尤其是 临床上缺乏SNR的应用是用于外科计划的弥散磁共振成像(DMRI)和功能磁共振成像(fMRI (例如，在功能神经外科和脑肿瘤中)。DMRI扫描时间长、分辨率低、 受试者运动：3T磁共振的大胆fMRI反应信号改变仅为3%左右。最先进的 基于图像模型或平滑的去噪方法会导致局部体积效应和细节损失 解剖细节。 我们已经确定了在临床可行的MRI方案中用于显著降低噪声的未开发储备 使用无模型时，SNR增加，RICER MRI噪声下限降低多达5倍 基于随机矩阵理论的降噪(去噪)和图像重建技术。它不会 依赖于用户特定的输入，性能优于最先进的去噪方法。我们的方法允许我们 识别并去除主成分分析(PCA)表示中的纯热噪声贡献 核磁共振数据矩阵。值得注意的是，当噪声随机进入每个体素的信号时，它对 当来自大量体素和不等价的信号时，主成分变得确定 采集(例如，Q空间、时间域、线圈)被组合在一起，这允许我们识别和去除纯粹的- 噪波分量。MP-PCA方法的关键是捕获冗余，使得PCA的大部分 频谱是由噪声主导的，然后可以识别和去除噪声的贡献。虽然我们最初 利用dMRI Q空间中的冗余，我们的初步发现表明它也存在于多线圈阵列中， 在功能磁共振成像的时间域中。 本研究的主要目标是：开发和优化MP-PCA去噪框架 多线圈图像重建及其在dMRI中的准确性和精密度(目标1)；评价其 在功能神经外科中提高dMRI分辨率的临床应用 MR引导超声术中反馈(AIM 2)；并评价其降低功能磁共振成像的临床应用价值 脑肿瘤手术前计划的扫描时间(目标3)。 从根本上说，该项目将建立一个客观框架，以量化不同 磁共振成像模式，通过分离信号和噪声。它在dMRI和fMRI中的应用 使用多线圈冗余，将导致最大可能的SNR，从而减少扫描时间，以及 提高临床相关磁共振成像方案的分辨率、精确度、灵敏度和诊断实用性。

PROJECT SUMMARY MRI is a widely-used imaging modality which offers unique soft-tissue contrast and provides a wealth of anatomical and functional information. However, MRI is inherently slow and signal-to-noise ratio (SNR)-limited, resulting in variable diagnostic image quality and limiting statistical power for research studies. Particularly clinically relevant SNR-starved applications are diffusion MRI (dMRI) and functional (fMRI) for surgical planning (e.g., in functional neurosurgery and in brain tumors). dMRI suffers from long scan times, low resolution and subject motion; BOLD fMRI response signal changes are only about 3% using 3T MRI. State-of-the-art denoising methods, based on image models or smoothing, result in partial-volume effects and loss of fine anatomical detail. We have identified an untapped reserve for significant noise reduction in clinically feasible MRI protocols resulting in SNR increase and Rician MRI noise floor decrease by factors of up to 5-fold, using a model-free noise reduction (denoising) and image reconstruction technique, based on random matrix theory. It does not rely on user-specific input, and outperforms state-of-the-art denoising methods. Our method allows us to identify and remove a pure thermal noise contribution in the principal component analysis (PCA) representation of an MRI data matrix. Remarkably, while noise enters randomly in each voxel's signal, its contribution to the principal components becomes deterministic, when signals from large number of voxels and inequivalent acquisitions (e.g., q-space, time-domain, coils) are combined, which allows us to identify and remove pure- noise components. The key to our MP-PCA method is acquisition redundancy, such that the bulk of the PCA spectrum is dominated by the noise, whose contribution can then be identified and removed. While we initially exploited redundancy in the dMRI q-space, our preliminary findings show it is also present in multi-coil arrays, and in the temporal domain of fMRI. The main goals of this study are: To develop and optimize the MP-PCA denoising framework at the level of multi-coil image reconstruction and to evaluate its accuracy and precision in dMRI (Aim 1); to evaluate its clinical utility for increasing dMRI resolution in functional neurosurgery, based on the ground-truth derived from MR-guided ultrasound intra-operative feedback (Aim 2); and to evaluate its clinical utility for decreasing fMRI scan time in preoperative planning of brain tumor resections (Aim 3). Fundamentally, this project will establish an objective framework to quantify the information content of different MRI modalities, by separating between the signal and the noise. Its applications to dMRI and fMRI, together with using multi-coil redundancy, will lead to maximal possible SNR, thereby reducing scan time, and improving resolution, precision, sensitivity and diagnostic utility of clinically relevant MRI protocols.