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 万人光是如此庞大的受影响人口以及随之而来的严重的 OA 就导致了巨大的开支。 OA 的特点是生物化学、结构和形态退化。关节软骨细胞外基质 (ECM) 的成分 ECM 主要由两种成分组成。大分子组，包括蛋白多糖 (PG) 和胶原纤维的软骨早期诊断。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 弛豫时间受到这些病理的影响。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。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这项拟议研究的结果将显着影响我们的个性化治疗能力。治疗方案并可能预防过早 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.