Current status of functional MRI of osteoarthritis for diagnosis and prognosis

被引:16
|
作者
Juras, Vladimir [1 ,2 ,3 ]
Chang, Gregory [1 ]
Regatte, Ravinder R. [1 ]
机构
[1] NYU, Dept Radiol, Ctr Biomed Imaging, Sch Med, 560 1St Ave, New York, NY 10016 USA
[2] Med Univ Vienna, High Field MR Ctr, Dept Biomed Imaging & Image Guided Therapy, Waehringer Guertel 18-20, A-1090 Vienna, Austria
[3] Slovak Acad Sci, Inst Measurement Sci, Bratislava, Slovakia
基金
奥地利科学基金会;
关键词
diagnosis; functional; MRI; osteoarthritis; prognosis; HUMAN ARTICULAR-CARTILAGE; KNEE OSTEOARTHRITIS; IN-VIVO; MAGNETIC-RESONANCE; SODIUM MRI; 7; T; ACHILLES-TENDON; RELAXATION; IMAGES; CLASSIFICATION;
D O I
10.1097/BOR.0000000000000674
中图分类号
R5 [内科学];
学科分类号
1002 ; 100201 ;
摘要
Purpose of review Osteoarthritis is a major source of disability, pain and socioeconomic cost worldwide. The epidemiology of the disorder is multifactorial including genetic, biological and biomechanical components, some of them detectable by MRI. This review provides the most recent update on MRI biomarkers which can provide functional information of the joint structures for diagnosis, prognosis and treatment response monitoring in osteoarthritis trials. Recent findings Compositional or functional MRI can provide clinicians with valuable information on glycosaminoglycan content (chemical exchange saturation transfer, sodium MRI, T-1 rho) and collagen organization (T-2, T-2*, apparent diffusion coefficient, magnetization transfer) in joint structures. Other parameters may also provide useful information, such as volumetric measurements of joint structures or advanced image data postprocessing and analysis. Automated tools seem to have a great potential to be included in these efforts providing standardization and acceleration of the image data analysis process. Functional or compositional MRI has great potential to provide noninvasive imaging biomarkers for osteoarthritis. Osteoarthritis as a whole joint condition needs to be diagnosed in early stages to facilitate selection of patients into clinical trials and/or to measure treatment effectiveness. Advanced evaluation including machine learning, neural networks and multidimensional data analysis allow for wall-to-wall understanding of parameter interactions and their role in clinical evaluation of osteoarthritis.
引用
收藏
页码:102 / 109
页数:8
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