PDF(Pubmed)
Abstract：
The osteoarthritis (OA) research community has been advocating a shift from radiography-based screening criteria and outcome measures in OA clinical trials to a magnetic resonance imaging (MRI)-based definition of eligibility and endpoint. For conventional morphological MRI, various semiquantitative evaluation tools are available. We have lately witnessed a remarkable technological advance in MRI techniques, including compositional/physiologic imaging and automated quantitative analyses of articular and periarticular structures. More recently, additional technologies were introduced, including positron emission tomography (PET)-MRI, weight-bearing computed tomography (CT), photon-counting spectral CT, shear wave elastography, contrast-enhanced ultrasound, multiscale X-ray phase contrast imaging, and spectroscopic photoacoustic imaging of cartilage. On top of these, we now live in an era in which artificial intelligence is increasingly utilized in medicine. Osteoarthritis imaging is no exception. Successful implementation of artificial intelligence (AI) will hopefully improve the workflow of radiologists, as well as the level of precision and reproducibility in the interpretation of images.
摘要：
骨关节炎（OA）研究界一直主张将OA临床试验中基于X射线照相术的筛查标准和结局指标转变为基于磁共振成像（MRI）的资格和终点定义。对于常规形态学MRI，各种半定量评估工具可用。我们最近目睹了MRI技术的显着技术进步，包括成分/生理成像和关节和关节周围结构的自动定量分析。最近,引入了其他技术，包括正电子发射断层扫描（PET）-MRI，负重计算机断层扫描（CT），光子计数能谱CT,剪切波弹性成像，超声造影,多尺度X射线相衬成像,和软骨的光谱光声成像。在这些之上,我们现在生活在一个人工智能越来越多地应用于医学的时代。骨关节炎影像学也不例外。人工智能（AI）的成功实施有望改善放射科医生的工作流程，以及图像解释的精度和再现性水平。
