Ultra-Fast High-Resolution Multi-Parametric MRI for Characterizing Cartilage Extracellular Matrix

用于表征软骨细胞外基质的超快速高分辨率多参数 MRI

• 批准号: 10929242
• 负责人: Fang Liu
• 金额: $ 64.32万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2023
• 资助国家: 美国
• 起止时间: 2023-09-21 至 2024-08-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10929242
• 关键词: Acceleration; Affect; Binding; Biochemical; Biological Markers; Cartilage; Clinical; Clinical Research; Collagen; Collagen Fiber; Communities; Degenerative polyarthritis; Disease; Evolution; Extracellular Matrix; Face; Histologic; Human; Image; Imaging Techniques; Incidence; Individual; Joints; Knee; Knee Osteoarthritis; Knee joint; Magic; Magnetic Resonance; Magnetic Resonance Imaging; Magnetic Resonance Spectroscopy; Maps; Measures; Methods; Patients; Physics; Predisposition; Proteoglycan; Protocols documentation; Relaxation; Research; Resolution; Scanning; Signal Transduction; Slice; Specificity; Statistical Methods; Structure; Techniques; Thick; Thinness; Time; Tissue Model; Tissues; Water; articular cartilage; cartilage degradation; deep learning; human subject; image reconstruction; imaging biomarker; imaging modality; in vivo; learning strategy; magnetic resonance imaging biomarker; mechanical properties; millimeter; non-invasive monitor; novel; reconstruction; research study; specific biomarkers; success; supervised learning

PROJECT SUMMARY Osteoarthritis (OA) is one of the most prevalent diseases affecting human joints, characterized by decreased proteoglycan content and disruption of the collagen fiber network in the cartilage extracellular matrix. Quantitative magnetic resonance (MR) imaging has been used to quantify cartilage composition and microstructure changes due to extracellular matrix degeneration in OA research studies. While many quantitative MR techniques have been explored, existing methods face serious limitations, including lack of specificity to assess individual macromolecular components, sensitive to magic angle effect, susceptible to partial volume effect due to thick image slice. More importantly, quantitative MR techniques typically require a much longer scan time than standard imaging due to the need for repeated scans of the same imaging object at varying imaging parameters. Spatial resolution and imaging volume coverage must be compromised to make a clinically feasible scan in OA research studies. This proposal aims to develop a new imaging technique that can provide robust, sensitive, and specific imaging biomarkers for simultaneously assessing cartilage proteoglycan and collagen components, and meanwhile can be acquired at the submillimeter spatial resolution, thin image slice, and full knee coverage within a 10-min scan time. Among all the quantitative MR techniques, multi-component T2 relaxation imaging has been found to provide sensitive and specific information for cartilage proteoglycan content; cross-relaxation imaging has been found to provide complementary information regarding the collagen fiber network of cartilage. The proposal will develop a simultaneous multi-component T2 relaxation and cross-relaxation imaging technique that can provide sensitive and specific imaging biomarkers to assess proteoglycan and collagen content and their ultra-structures in a unified imaging framework (Aim 1). This imaging protocol will be optimized using rigorous statistical methods and accelerated through a novel deep learning method that leverages self-supervised learning and MR physics-informed tissue modeling. The derived MR imaging biomarkers will be correlated with tissue histological, biochemical, and mechanical properties, which will create a basis for interpretation of the clinical study results (Aim 2). A pilot clinical study using the optimized and accelerated imaging technique will be performed on patients with varying degrees of knee OA, establishing the clinical evidence of the utility, efficiency, and overall clinical value of this newly proposed technique on detecting OA incidence and predicting OA progression (Aim 3). Our proposed new methods will root from developing novel rapid image acquisition, combined with advanced deep learning reconstruction and automatic processing, all of which are pioneered by our research team. Successful completion of the proposal will provide the OA research community with a new set of MR biomarkers to non-invasively monitor disease-related and treatment-related changes in cartilage composition and ultra-structure in human subjects.

项目摘要 骨关节炎（OA）是影响人类关节的最普遍的疾病之一，其特征在于骨关节炎（OA）引起的骨关节炎（OA）减少。 蛋白多糖含量和软骨细胞外基质中胶原纤维网络的破坏。定量 磁共振（MR）成像已被用于量化软骨成分和微结构变化 由于OA研究中的细胞外基质变性。虽然许多定量MR技术 然而，现有的方法面临着严重的局限性，包括缺乏特异性来评估个体 高分子组分，对魔角效应敏感，由于厚而易受部分体积效应影响， 图像切片。更重要的是，定量MR技术通常需要比常规MR技术长得多的扫描时间。 由于需要在不同的成像参数下重复扫描相同的成像对象， 空间分辨率和成像体积覆盖范围必须妥协，以在OA中进行临床可行的扫描 调查研究。该提案旨在开发一种新的成像技术，该技术可以提供鲁棒的、灵敏的、 用于同时评估软骨蛋白聚糖和胶原成分的特异性成像生物标志物，和 同时可以在亚毫米空间分辨率、薄图像切片和全膝关节覆盖范围内采集 扫描时间为10分钟。在所有的定量MR技术中，多分量T2弛豫成像已经被广泛应用于临床。 发现为软骨蛋白多糖含量提供敏感和特异性信息;交叉弛豫成像 已经发现提供了关于软骨胶原纤维网络的补充信息。的 该提案将开发同时多分量T2弛豫和交叉弛豫成像技术， 可以提供敏感和特异的成像生物标志物，以评估蛋白聚糖和胶原蛋白含量及其 统一成像框架中的超微结构（目标1）。该成像方案将使用严格的 统计方法，并通过利用自监督的新型深度学习方法加速 学习和MR物理信息组织建模。将导出的MR成像生物标志物与以下各项相关联： 组织的组织学、生物化学和机械特性，这将为解释 临床研究结果（目标2）。将进行一项使用优化和加速成像技术的试点临床研究， 对不同程度的膝关节OA患者进行了研究，建立了临床证据， 以及这项新提出的技术在检测OA发病率和预测OA方面的总体临床价值 进展（目标3）。我们提出的新方法将源于开发新的快速图像采集， 结合先进的深度学习重建和自动处理，所有这些都是由 我们的研究团队该提案的成功完成将为OA研究界提供一个新的 一组MR生物标志物，用于非侵入性监测软骨中的疾病相关和治疗相关变化 组成和超微结构。