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

基本信息

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
项目类别：
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

项目摘要

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.

项目摘要骨关节炎（OA）是一种退化性关节疾病，影响了美国超过2700万人独自的。受影响的大量人口和严重的OA耐用性，导致了巨大的费用医疗保健系统。 OA的特征是生化，结构和形态降解的特征关节软骨的细胞外基质（ECM）的成分。 ECM由主要两个组成包括蛋白聚糖（PG）和胶原蛋白纤维在内的大分子组。软骨的早期诊断退化将需要非侵入性检测PG浓度变化和胶原蛋白的能力形态变化之前的完整性。 T1ρ和T2松弛时间受这些病理的影响过程，是全球使用最广泛的生化软骨MRI序列。该提案的总体目标是开发，优化和翻译高空间分辨率3D-异型 - MRI序列（700µMX700µMX700µM），用于简单评估形态计量学和Biexponential 3D- T1ρ（T2）映射序列（每种协议在10分钟以下），以改善定量评估形态学（软骨厚度和体积）以及软骨的生化表征临床3T扫描仪采用压缩感应（CS）和平行成像（PI）的组合。这建议的加速，各向同性，高空间分辨率，多组分3D-T1ρ（T2）映射技术可以轻松地将其纳入常规临床方案中，以便对软骨进行生化评估。标准的形态学评估，可以用作未来的成像生物标志物来修饰疾病 OA的疗法。这项拟议的研究的结果将极大地影响我们的个性化能力治疗方案，并可能阻止过早OA的发展。

项目成果

期刊论文数量（20）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

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)
影响因子：
0
作者：
通讯作者：

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
作者：
通讯作者：

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.