Random Matrix Theory-Based Noise Removal in MRI

项目摘要 MRI是一种广泛使用的成像方式，其提供独特的软组织对比度，并提供丰富的 解剖和功能信息。然而，MRI固有地慢并且信噪比（SNR）受限， 从而导致可变的诊断图像质量并限制研究的统计能力。特别 临床相关的SNR缺乏应用是用于手术计划的弥散MRI（dMRI）和功能MRI（fMRI (e.g.,在功能性神经外科和脑肿瘤中）。dMRI的缺点是扫描时间长、分辨率低， 受试者运动;使用3 T MRI时，BOLD fMRI响应信号变化仅为约3%。State-of-the-art 基于图像模型或平滑的去噪方法导致部分体积效应和精细度损失， 解剖细节 我们已经确定了一个未开发的储备显着减少噪音在临床上可行的MRI协议 导致SNR增加和Rician MRI噪声基底降低高达5倍，使用无模型 降噪（去噪）和图像重建技术，基于随机矩阵理论。它不 依赖于用户特定的输入，并优于最先进的去噪方法。我们的方法可以让我们 识别并去除所述主成分分析（PCA）表示中的纯热噪声贡献 核磁共振成像数据矩阵。值得注意的是，虽然噪声随机进入每个体素的信号，但它对信号的贡献是随机的。 当信号来自大量体素且不等价时， 收购（例如，q空间，时域，线圈）相结合，这使我们能够识别和去除纯- 噪声成分我们的MP-PCA方法的关键是采集冗余，使得PCA的大部分 频谱由噪声主导，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， 和功能磁共振成像的时间域。 本研究的主要目标是：在以下水平上开发和优化MP-PCA去噪框架： 多线圈图像重建，并评价其在dMRI中的准确性和精密度（目标1）;评价其在dMRI中的准确性和精密度（目标1）。 基于从以下来源获得的基础事实，在功能神经外科中提高dMRI分辨率的临床实用性 MR引导超声术中反馈（目标2）;并评价其减少fMRI的临床效用 脑肿瘤切除术术前计划中的扫描时间（目标3）。 从根本上说，这个项目将建立一个客观的框架，以量化不同的信息内容， MRI模态，通过分离信号和噪声。它在dMRI和fMRI上的应用， 使用多线圈冗余，将导致最大可能的SNR，从而减少扫描时间， 改善临床相关MRI协议的分辨率、精确度、灵敏度和诊断效用。

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.