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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

基本信息

批准号：
10296235
负责人：
Ravinder Regatte
金额：
$ 53.18万
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：
2021
资助国家：
美国
起止时间：
2021-07-01 至 2026-05-31
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10296235
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 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

项目摘要

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