Data-Driven Learning Framework for Fast Quantitative Knee Joint Mapping

用于快速定量膝关节绘图的数据驱动学习框架

• 批准号: 10430275
• 负责人: Ravinder Regatte
• 金额: $ 53.44万
• 依托单位: NEW YORK UNIVERSITY SCHOOL OF MEDICINE
• 依托单位国家: 美国
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-07-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10430275
• 关键词: 3-Dimensional; Acceleration; Affect; Algorithms; Biochemical; Biological Models; Cartilage; Chronic; Clinical; Clinical Protocols; Collagen; Collagen Fiber; Contrast Media; Data; Data Set; Degenerative polyarthritis; Detection; Diagnostic; Discrimination; Early Diagnosis; Elderly; Evaluation; Extracellular Matrix; Extracellular Matrix Degradation; Future; Goals; Heterogeneity; Human; Hydration status; Image; Imaging Techniques; Knee; Knee Osteoarthritis; Knee joint; Learning; Life; Machine Learning; Magnetic Resonance Imaging; Maps; Meniscus structure of joint; Meta-Analysis; Methods; Modeling; Modification; Morphology; Musculoskeletal; Pathologic Processes; Patients; Pattern; Performance; Persons; Population; Process; Proteoglycan; Protocols documentation; Relaxation; Research Personnel; Resolution; Sampling; Scanning; Screening procedure; Slice; Structure; T2 weighted imaging; Techniques; Therapeutic Agents; Thick; Thinness; Time; Tissue Engineering; Tissues; Translating; Validation; Water; articular cartilage; base; cartilage degradation; curative treatments; design; disability; early screening; efficacy evaluation; healing; human data; improved; in vivo; learning algorithm; macromolecule; prevent; reconstruction; repaired; tool; water environment

PROJECT SUMMARY Osteoarthritis (OA), a leading cause of chronic disability in the elderly population, occurs with the degradation of the extracellular matrix of articular cartilage, mainly composed of proteoglycan, collagen fibers, and water. Early diagnosis of cartilage degeneration requires the detection of changes in proteoglycan concentration and collagen integrity, preferably non-invasively and before any morphological changes occur. Spin-spin relaxation time (T2) and spin-lattice relaxation time in the rotating frame (T1ρ) can provide quantitative information about the structure and biochemical composition of the cartilage before morphological changes occur. Mono-exponential (ME) models can characterize the T2 and T1ρ relaxation processes and map it for articular cartilage in the knee joint. A recent meta-analysis showed that T1ρ provides more discrimination than T2 for OA. However, the ME model alone cannot provide distinct information from different compartments of the cartilage. Recent studies have shown that T1ρ relaxation might have bi-exponential (BE) components, following the hypothesis of the multi- compartmental structure of the cartilage. BE T2 relaxation has shown better diagnostic performance than ME for OA and can show the dispersion of the relaxation times, reflecting the heterogeneity in the macromolecular environment of water in the cartilage. BE analysis of cartilage typically requires a larger number of acquisitions with different spin-lock times (TSLs) or echo times (TEs), resulting in long scan time. High spatial resolution is also needed to visualize the thin and curved cartilage and fine structures in the knee joint. As a result, in vivo application of BE three-dimensional (3D) T1ρ and T2 mapping techniques is still very limited. Compressed sensing (CS) combined with parallel imaging (PI) can accelerate acquisition and reduce the scan time required for ME 3D T1ρ and T2 mappings. T1ρ scans can be reduced from 30 min to ~3 min with an error smaller than 6.5%. However, the error is two to three times larger for BE mapping. This problem can be potentially solved by optimizing the sampling times (TSLs for T1ρ and TEs for T2) and the free parameters of the CS approach (k- space sampling pattern, regularization function, regularization parameter, and minimization algorithm parameters) using fully sampled 3D knee joint datasets, supported by machine learning tools. The overarching goal of this proposal is to develop, optimize, and translate a high-spatial-resolution, rapid 3D magnetic resonance imaging sequence using data-driven learning-based CS for assessment of the human knee joint and using ME and BE 3D T1ρ (T2) mapping for improved biochemical characterization of cartilage and menisci on a standard clinical 3T scanner.

项目总结 骨关节炎(OA)是老年人慢性残疾的主要原因之一，其发生的原因是关节退行性变 关节软骨的细胞外基质，主要由蛋白多糖、胶原纤维和水组成。早些时候 诊断软骨退变需要检测蛋白多糖浓度和胶原蛋白的变化 完整性，最好是非侵入性的，并在任何形态变化发生之前。自旋-自旋弛豫时间(T2) 旋转标架中的自旋-晶格弛豫时间(T1ρ)可以提供关于结构的定量信息 在形态发生变化之前，软骨的生物化学成分。单指数(ME) 模型可以表征T2和T1的ρ松弛过程，并将其映射到膝关节的关节软骨。 最近的一项荟萃分析表明，T1ρ比T2对OA的区分能力更强。然而，ME模式 单独从软骨的不同隔间提供不同的信息是不够的。最近的研究表明 结果表明，T1ρ弛豫可能具有双指数(BE)成分，遵循多指数(BE)的假设。 软骨的间隔性结构。BE T2弛豫显示出比ME更好的诊断性能 并能显示弛豫时间的离散性，反映了大分子中的不均一性 软骨中的水环境。对软骨的BE分析通常需要大量的采集 具有不同的自旋锁定时间(TSL)或回波时间(TES)，导致较长的扫描时间。高空间分辨率是 还需要显示膝关节中薄而弯曲的软骨和精细结构。因此，在活体内 BE三维(3D)T1、ρ和T2标测技术的应用仍然非常有限。压缩感知 (CS)结合并行成像(PI)可以加快采集速度并减少ME所需的扫描时间 3D T1、ρ和T2映射。T1ρ扫描可从30分钟减少到~3分钟，误差小于6.5%。 然而，BE映射的误差要大两到三倍。这个问题有可能通过以下方式解决 优化CS方法的采样时间(T1ρ的TSLs和T2的TES)和自由参数(k- 空间采样模式、正则化函数、正则化参数和最小化算法 参数)使用完全采样的3D膝关节数据集，并由机器学习工具支持。最重要的是 这项提议的目标是开发、优化和转换高空间分辨率、快速3D磁共振 基于数据驱动学习的CS成像序列用于人体膝关节的评估 和BE 3DT1ρ(T2)标测用于改善标准上软骨和半月板的生化特征 临床3T扫描仪。