Random Matrix Theory-Based Noise Removal in MRI

MRI 中基于随机矩阵理论的噪声消除

• 批准号: 10229483
• 负责人: Els Fieremans
• 金额: $ 67.81万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2019
• 资助国家: 美国
• 起止时间: 2019-09-16 至 2023-07-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10229483
• 关键词: Address; Algorithms; Anatomy; Body Surface; Body Temperature; Brain; Brain Mapping; Brain Neoplasms; Cell Nucleus; Clinical; Data; Deep Brain Stimulation; Deterioration; Development; Diagnosis; Diagnostic; Diagnostic Imaging; Diffusion Magnetic Resonance Imaging; Essential Tremor; Excision; Feedback; Floor; Focused Ultrasound; Functional Magnetic Resonance Imaging; Geometry; Goals; Image; Joints; Language; Length; Lesion; Magnetic Resonance Imaging; Maps; Measurement; Medial; Methods; Modeling; Monitor; Morphologic artifacts; Motion; Noise; Nuclear; Operative Surgical Procedures; Outcome; Parkinson Disease; Patients; Plant Roots; Principal Component Analysis; Protocols documentation; Pyramidal Tracts; RF coil; Residual state; Resolution; Retrospective Studies; Sampling; Scanning; Signal Transduction; Spectrum Analysis; Techniques; Testing; Thalamic Nuclei; Therapeutic; Time; Translating; Ultrasonography; Variant; base; brain tumor resection; clinically relevant; denoising; functional MRI scan; healthy volunteer; image reconstruction; imaging modality; improved; nervous system disorder; neurosurgery; reconstruction; research study; response; soft tissue; theories

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.

项目概要 MRI 是一种广泛使用的成像方式，可提供独特的软组织对比度并提供丰富的信息 解剖和功能信息。然而，MRI 本身速度较慢且信噪比 (SNR) 有限， 导致诊断图像质量参差不齐并限制研究的统计能力。特别 临床相关的 SNR 匮乏的应用是用于手术计划的扩散 MRI (dMRI) 和功能性 (fMRI) （例如，在功能神经外科和脑肿瘤中）。 dMRI 存在扫描时间长、分辨率低、 主体运动；使用 3T MRI 时，BOLD fMRI 反应信号变化仅为 3% 左右。最先进的 基于图像模型或平滑的去噪方法会导致部分体积效应和精细度损失 解剖细节。 我们已经在临床可行的 MRI 协议中找到了未开发的显着降噪储备 使用无模型模型，导致信噪比 (SNR) 增加，莱斯 MRI 本底噪声降低高达 5 倍 基于随机矩阵理论的降噪（去噪）和图像重建技术。它不 依赖于用户特定的输入，并且优于最先进的去噪方法。我们的方法使我们能够 识别并消除主成分分析 (PCA) 表示中的纯热噪声贡献 MRI 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 当来自大量体素且不等价的信号时，主成分变得确定性 采集（例如，q 空间、时域、线圈）被组合起来，这使我们能够识别和删除纯 噪声成分。我们的 MP-PCA 方法的关键是采集冗余，这样 PCA 的大部分 频谱主要是噪声，然后可以识别并消除噪声的影响。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， 以及功能磁共振成像的时域。 本研究的主要目标是： 开发和优化 MP-PCA 去噪框架 多线圈图像重建并评估其在 dMRI 中的准确性和精度（目标 1）；来评价其 基于以下事实得出的提高功能神经外科 dMRI 分辨率的临床实用性 MR 引导超声术中反馈（目标 2）；并评估其降低 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）。 从根本上讲，该项目将建立一个客观框架来量化不同领域的信息内容。 MRI 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，将导致最大可能的信噪比，从而减少扫描时间，并且 提高临床相关 MRI 方案的分辨率、精确度、灵敏度和诊断实用性。