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.

项目摘要 MRI是一种广泛使用的成像方式，可提供独特的软组织对比，并提供大量 解剖和功能信息。但是，MRI本质上是缓慢且有信噪比（SNR）的限制， 导致可变的诊断图像质量和限制研究的统计能力。特别 用于手术计划的临床相关SNR饥饿应用是扩散MRI（DMRI）和功能（fMRI） （例如，在功能性神经外科和脑肿瘤中）。 DMRI遭受了长时间的扫描时间，低分辨率和 主题运动；使用3T MRI，大胆的fMRI响应信号变化仅为3％。最先进的 基于图像模型或平滑的去核方法会导致部分体积效应和罚款损失 解剖细节。 我们已经确定了一个未开发的储备，以大幅度降低临床可行的MRI方案的降噪功能 使用无模型 基于随机矩阵理论，降噪（降低）和图像重建技术。它没有 依靠特定于用户的输入，并且优于最先进的denoising方法。我们的方法使我们能够 在主成分分析（PCA）表示中识别并删除纯热噪声贡献 MRI数据矩阵。值得注意的是，当噪声在每个体素信号中随机输入时，其对 当来自大量体素和不相等的信号时，主组件变得确定性 合并了采集（例如Q空间，时间域，线圈），这使我们能够识别并去除纯净的 噪声组件。 MP-PCA方法的关键是采集冗余，因此PCA的大部分 频谱由噪声支配，然后可以识别和去除其贡献。当我们最初 在DMRI Q-Space中被剥削的冗余，我们的初步发现表明，它也存在于多线圈阵列中， 以及fMRI的时间领域。 这项研究的主要目标是：开发和优化MP-PCA Denoisising框架 多线圈图像重建并评估其在DMRI中的准确性和精度（AIM 1）；评估它 基于从源自功能性神经外科的DMRI分辨率的临床实用性，基于源自 MR引导超声内部反馈（AIM 2）；并评估其临床实用程序以减少fMRI 扫描时间在术前计划脑肿瘤切除术（AIM 3）。 从根本上讲，该项目将建立一个客观的框架来量化不同的信息内容 MRI模式，通过在信号和噪声之间分离。它在DMRI和fMRI中的应用 使用多组油的冗余，将导致最大可能的SNR，从而减少扫描时间，并且 改善临床相关MRI方案的分辨率，精度，灵敏度和诊断效用。