Rapid Quantitative Assessment of Knee Joint with Compressed Sensing

利用压缩感知对膝关节进行快速定量评估

• 批准号: 10686034
• 负责人: Ravinder Regatte
• 金额: $ 62.74万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2020
• 资助国家: 美国
• 起止时间: 2020-08-03 至 2025-07-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10686034
• 关键词: 3-Dimensional; Acceleration; Affect; Binding; Biochemical; Biological Models; Bovine Cartilage; Cartilage; Clinical; Clinical Protocols; Collagen; Collagen Fiber; Data; Degenerative polyarthritis; Development; Diagnostic; Disease; Early Diagnosis; Evaluation; Extracellular Matrix; Future; Goals; Healthcare Systems; Histology; Human; Hydration status; Image; Imaging Techniques; Joints; Knee Osteoarthritis; Knee joint; Lead; Magnetic Resonance Imaging; Maps; Meniscus structure of joint; Methods; Modeling; Morphology; Musculoskeletal; Non-Invasive Detection; Outcome; Pain; Pathologic Processes; Patients; Performance; Persons; Population; Proteoglycan; Protocols documentation; Relaxation; Research Personnel; Resolution; Sampling; Scanning; Slice; Specificity; Structure; T2 weighted imaging; Techniques; Therapeutic Agents; Thick; Time; Tissue Engineering; Tissues; Translating; Treatment Protocols; United States; Validation; Water; articular cartilage; cartilage degradation; cohort; data space; efficacy evaluation; healing; imaging biomarker; improved; in vivo; macromolecule; morphometry; non-invasive imaging; novel; personalized medicine; premature; prevent; reconstruction; repaired

PROJECT SUMMARY Osteoarthritis (OA) is a degenerative joint disease, affecting more than 27 million people in the United States alone. This large affected population and the severe consequent debility of OA lead to significant expenses to the health care system. OA is characterized by biochemical, structural and morphologic degradation of components of the extracellular matrix (ECM) of articular cartilage. The ECM is composed of primarily two groups of macromolecules including proteoglycan (PG) and collagen fibers. Early diagnosis of cartilage degeneration would require the ability to non-invasively detect changes in PG concentration and collagen integrity before morphological changes occur. T1ρ and T2 relaxation times are affected by these pathological processes and are the most widely used biochemical cartilage MRI sequences worldwide. . The overarching goal of this proposal is to develop, optimize, and translate high spatial resolution 3D-isotropic- MRI sequence (700µmX700µmX700µm) for simultaneous assessment of morphometry and biexponential 3D- T1ρ (T2) mapping sequences (each protocol under 10 minutes) for improved quantitative assessment of morphological (cartilage thickness and volume) and biochemical characterization of cartilage on a standard clinical 3T scanner employing combinations of compressed sensing (CS) and parallel imaging (PI). The proposed accelerated, isotropic, high spatial resolution, multicomponent 3D-T1ρ (T2) mapping techniques can be easily incorporated into routine clinical protocols for biochemical assessment of cartilage in addition to standard morphological evaluation and could serve as future imaging biomarkers for disease modifying therapies for OA. The outcome of this proposed study will significantly impact our ability for personalized treatment regimens and possibly prevent the development of premature OA.

项目概要 骨关节炎 (OA) 是一种退行性关节疾病，影响美国超过 2700 万人 独自的。如此庞大的受影响人口以及随之而来的严重 OA 衰弱导致了巨额费用 医疗保健系统。 OA 的特征是生物化学、结构和形态降解 关节软骨细胞外基质（ECM）的成分。 ECM 主要由两个部分组成 大分子组，包括蛋白聚糖 (PG) 和胶原纤维。软骨的早期诊断 变性需要能够非侵入性地检测 PG 浓度和胶原蛋白的变化 形态变化发生前的完整性。 T1ρ 和 T2 弛豫时间受这些病理的影响 过程，是全世界使用最广泛的生化软骨 MRI 序列。 。 该提案的总体目标是开发、优化和转换高空间分辨率 3D 各向同性 MRI 序列 (700µmX700µmX700µm)，用于同时评估形态测量和双指数 3D- T1ρ (T2) 映射序列（每个协议不到 10 分钟），用于改进的定量评估 标准软骨的形态学（软骨厚度和体积）和生化特征 采用压缩传感 (CS) 和并行成像 (PI) 组合的临床 3T 扫描仪。这 提出的加速、各向同性、高空间分辨率、多分量 3D-T1ρ (T2) 映射技术可以 除了以下之外，还可以轻松纳入软骨生化评估的常规临床方案中 标准形态学评估，可作为未来疾病修饰的成像生物标志物 OA 的治疗。这项拟议研究的结果将显着影响我们个性化的能力 治疗方案并可能预防过早 OA 的发展。

项目成果

Musculoskeletal MR Imaging Applications at Ultra-High (7T) Field Strength.

• DOI: 10.1016/j.mric.2020.09.008
• 发表时间: 2021-03
• 期刊: Magnetic resonance imaging clinics of North America
• 影响因子: 1.6
• 作者: Menon RG;Chang G;Regatte RR
• 通讯作者: Regatte RR

Emerging Trends in Fast MRI Using Deep-Learning Reconstruction on Undersampled k-Space Data: A Systematic Review.

• DOI: 10.3390/bioengineering10091012
• 发表时间: 2023-08-26
• 期刊: Bioengineering (Basel, Switzerland)

3D magnetic resonance fingerprinting for rapid simultaneous T1, T2, and T1ρ volumetric mapping of human articular cartilage at 3 T.

• DOI: 10.1002/nbm.4800
• 发表时间: 2022-12
• 期刊: NMR in biomedicine
• 影响因子: 2.9

Updates on Compositional MRI Mapping of the Cartilage: Emerging Techniques and Applications.

• DOI: 10.1002/jmri.28689
• 发表时间: 2023-07
• 期刊: JOURNAL OF MAGNETIC RESONANCE IMAGING
• 影响因子: 4.4
• 作者: Zibetti, Marcelo V. W.;Menon, Rajiv G. G.;de Moura, Hector L. L.;Zhang, Xiaoxia;Kijowski, Richard;Regatte, Ravinder R. R.
• 通讯作者: Regatte, Ravinder R. R.

Alternating Learning Approach for Variational Networks and Undersampling Pattern in Parallel MRI Applications.

• DOI: 10.1109/tci.2022.3176129
• 发表时间: 2022
• 期刊: IEEE TRANSACTIONS ON COMPUTATIONAL IMAGING
• 影响因子: 5.4
• 作者: Zibetti, Marcelo Victor Wust;Knoll, Florian;Regatte, Ravinder R.
• 通讯作者: Regatte, Ravinder R.