Rapid Low-Cost Quantitative 3D MRI and Gait Assessment of the Knee

快速、低成本定量 3D MRI 和膝关节步态评估

基本信息

批准号：
10671520
负责人：
Brian Andrew Hargreaves
金额：
$ 65.93万
依托单位：
STANFORD UNIVERSITY
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：
2020-08-15 至 2025-06-30
项目状态：
未结题

来源：
https://reporter.nih.gov/project-details/10671520
关键词：
3-Dimensional Affect American Anterior Cruciate Ligament Articulation Bilateral Biochemical Biomechanics Bone Marrow Bone Spur Cartilage Chronic Clinical Cluster Analysis Collaborations Connective Tissue Cost Measures Coupled Data Data Pooling Degenerative polyarthritis Detection Development Diagnosis Diagnostic Diffusion Diffusion Magnetic Resonance Imaging Disease Enrollment Environment Equipment Etiology Evaluation Female Femur Fibrocartilages Future Gait Gait abnormality Goals Health Human Resources Image Inflammatory Injections Injury Intervention Joints Kellgren-Lawrence grade Knee Knee Osteoarthritis Left Length Lesion Ligaments Magnetic Resonance Imaging Manuals Maps Measurement Measures Meniscus structure of joint Methods Morphology Motion Motivation Orthopedics Output Pain Patient Triage Patients Population Protocols documentation Protons Quality of life Quantitative Evaluations Radiation Reader Replacement Arthroplasty Research Research Personnel Risk Risk Factors Roentgen Rays Sampling Scanning Sex Differences Slice Structure Surface Synovitis Techniques Tendon structure Testing Thick Time Tissues Traumatic Arthropathy Visualization analysis pipeline anterior cruciate ligament injury anterior cruciate ligament rupture automated analysis biomechanical test bone cohort cost deep learning density disability disease phenotype disorder risk early onset effective therapy gait examination high body mass index image reconstruction improved kinematics learning classifier meniscus injury minimally invasive novel primary outcome quantitative imaging radiological imaging reconstruction research study sensor skills societal costs therapy development tissue biomarkers ultra high resolution

项目摘要

Project Abstract Motivation: Osteoarthritis (OA) is a painful disease that affects tens of millions of Americans, but is poorly understood, resulting in a lack of treatments. Enabling low-cost approaches for widespread study of risk factors, onset and early progression of OA will enable better understanding of OA mechanisms, treatment development, and triage of patients to different treatments based on speciﬁc disease phenotypes. Multiple systemic factors, biochemical factors, and other risk factors are associated with OA, but causes are difﬁ- cult to isolate and study during slow progression. Currently OA is diagnosed as joint-space narrowing using X-ray radiography, at a stage well beyond when interventions can be effective. Magnetic resonance imaging (MRI) of- fers sensitivity to morphologic and biochemical changes, but most methods are impractical for widespread clinical or research use. Usually MRI exams study only one knee, precluding the opportunity to compare knees. Sim- ilarly, biomechanics assessment typically requires numerous tests using advanced and rarely-available equip- ment and time-intensive analysis by skilled personnel, making this a challenge for widespread use. We have shown rapid, simultaneous 3D scanning of both knees with quantitative relaxometry and diffusion map- ping of connective tissues, combined with novel visualization of longitudinal change validated in a population with anterior cruciate ligament (ACL) tears. We have developed fully-automated cartilage and meniscus seg- mentation to simplify post-processing. (Our automated cartilage segmentation variability approaches that of reader-to-reader variability.) We now propose to combine MRI acquisition, reconstruction and analysis tech- niques with simple measures of kinematics into a widely applicable low-cost imaging and biomechanical test, which we will validate in subjects with ACL-injury and subjects with varying Kellgren-Lawrence grades of OA. Approach: We will begin by developing a robust 5-to-8-minute bilateral knee MRI exam, using an efﬁcient 3D isotropic acquisition and novel deep-learning based image reconstructions. This will be followed with automated cartilage segmentation and quantitative analysis (thickness, T2, diffusion) of all 3 knee plates and automated semiquantitative scoring approaches for synovitis, bone marrow and cartilage lesions. Inertial measurement units (IMUs) will be used to measure kinematics, and gait asymmetries. We will continue our studies in ACL pa- tients to validate techniques and to develop asymmetry analyses for both imaging and biomechanical measures. Finally, in subjects with varying OA grade, we will evaluate the potential of the overall low-cost approach to relate asymmetry and longitudinal change measures to progression and OA grade. Signiﬁcance: This project will develop an acquisition and analysis pipeline to quantify knee changes and left/right asymmetries that precede OA. We will characterize methods in idiopathic OA subjects and ACL- injured subjects at risk of post-traumatic OA. The very low target cost, under $120/subject, will ultimately enable widespread study of early onset and progression of different OA types, leading to earlier and better treatments.

项目摘要动机：骨关节炎 (OA) 是一种痛苦的疾病，影响着数千万美国人，但治疗效果不佳理解，导致缺乏对风险因素进行广泛研究的低成本方法， OA 的发病和早期进展将有助于更好地了解 OA 机制、治疗开发、以及根据特定疾病表型对患者进行不同治疗的分类。多种全身因素、生化因素和其他危险因素与 OA 相关，但病因不同。目前，OA 被诊断为使用 X 射线的关节间隙狭窄。放射线照相，远远超出了干预措施可以有效的阶段—— 对形态和生化变化具有敏感性，但大多数方法对于广泛的临床来说是不切实际的通常 MRI 检查仅研究一个膝盖，排除了比较膝盖的机会。同样，生物力学评估通常需要使用先进且罕见的设备进行大量测试。技术人员的管理和时间密集型分析，使其成为广泛使用的挑战。我们已经展示了通过定量松弛测量和扩散图对双膝进行快速、同步 3D 扫描 - 结缔组织的 ping，结合在人群中验证的纵向变化的新颖可视化我们开发了全自动软骨和半月板分段手术。心理状态以简化后处理（我们的自动化软骨分割变异性接近于读者之间的差异。）我们现在建议将 MRI 采集、重建和分析技术结合起来将简单的运动学测量方法转化为广泛适用的低成本成像和生物力学测试，我们将在患有 ACL 损伤的受试者和具有不同 Kellgren-Lawrence 等级 OA 的受试者中进行验证。方法：我们将首先使用高效的 3D 技术开发强大的 5 至 8 分钟双侧膝关节 MRI 检查随后将进行各向同性采集和基于深度学习的新型图像重建。所有 3 个膝板的软骨分割和定量分析（厚度、T2、扩散）和自动化滑膜炎、骨髓和软骨病变的半定量评分方法。单位（IMU）将用于测量运动学和步态不对称性，我们将继续我们在 ACL 方面的研究。旨在验证技术并开发成像和生物力学测量的不对称分析。最后，在不同 OA 等级的受试者中，我们将评估整体低成本方法与相关的潜力进展和 OA 等级的不对称性和纵向变化测量。意义：该项目将开发一个采集和分析管道来量化膝盖变化和我们将描述特发性 OA 受试者和 ACL- 的方法。处于创伤后 OA 风险的受伤受试者目标成本非常低，低于 120 美元/受试者，最终将能够实现这一目标。对不同 OA 类型的早期发病和进展的广泛研究，有助于更早、更好的治疗。

项目成果

期刊论文数量（26）

专著数量（0）

科研奖励数量（0）

会议论文数量（0）

专利数量（0）

Imaging of Synovial Inflammation in Osteoarthritis, From the AJR Special Series on Inflammation.

骨关节炎滑膜炎症的影像学，来自 AJR 炎症特别系列。

DOI：
发表时间：
2022
期刊：
影响因子：
0
作者：
Thoenen, Jacob;MacKay, James W;Sandford, Halston J C;Gold, Garry E;Kogan, Feliks
通讯作者：
Kogan, Feliks

Challenges in ensuring the generalizability of image quantitation methods for MRI.

确保 MRI 图像定量方法的通用性面临的挑战。

DOI：
发表时间：
2022-04
期刊：
影响因子：
3.8
作者：
Keenan, Kathryn E;Delfino, Jana G;Jordanova, Kalina V;Poorman, Megan E;Chirra, Prathyush;Chaudhari, Akshay S;Baessler, Bettina;Winfield, Jessica;Viswanath, Satish E;deSouza, Nandita M
通讯作者：
deSouza, Nandita M

Improving Data-Efficiency and Robustness of Medical Imaging Segmentation Using Inpainting-Based Self-Supervised Learning.

使用基于修复的自我监督学习提高医学影像分割的数据效率和鲁棒性。

DOI：
发表时间：
2023-02-04
期刊：
影响因子：
0
作者：
Dominic, Jeffrey;Bhaskhar, Nandita;Desai, Arjun D;Schmidt, Andrew;Rubin, Elka;Gunel, Beliz;Gold, Garry E;Hargreaves, Brian A;Lenchik, Leon;Boutin, Robert;Chaudhari, Akshay S
通讯作者：
Chaudhari, Akshay S

The KNee OsteoArthritis Prediction (KNOAP2020) challenge: An image analysis challenge to predict incident symptomatic radiographic knee osteoarthritis from MRI and X-ray images.

膝骨关节炎预测 (KNOAP2020) 挑战：图像分析挑战，根据 MRI 和 X 射线图像预测事件症状性放射学膝骨关节炎。

DOI：
发表时间：
2023-01
期刊：
影响因子：
0
作者：
Hirvasniemi, J;Runhaar, J;van der Heijden, R A;Zokaeinikoo, M;Yang, M;Li, X;Tan, J;Rajamohan, H R;Zhou, Y;Deniz, C M;Caliva, F;Iriondo, C;Lee, J J;Liu, F;Martinez, A M;Namiri, N;Pedoia, V;Panfilov, E;Bayramoglu, N;Nguyen, H H;Nieminen
通讯作者：
Nieminen

Rapid Knee MRI Acquisition and Analysis Techniques for Imaging Osteoarthritis.

骨关节炎成像的快速膝关节 MRI 采集和分析技术。

DOI：
发表时间：
2020-11
期刊：
影响因子：
0
作者：
Chaudhari, Akshay S;Kogan, Feliks;Pedoia, Valentina;Majumdar, Sharmila;Gold, Garry E;Hargreaves, Brian A
通讯作者：
Hargreaves, Brian A