Deep Learning Technology for Rapid Morphological and Quantitative Imaging of Knee Pathology

项目总结 膝关节疼痛在普通人群中的高患病率给公众带来了巨大的挑战 健康，给我们的社会带来巨大的医疗保健和经济负担。磁共振成像(MRI)是 评估膝关节疼痛患者的首选影像方式。事实上，外围关节排在第三位，是 最常见的身体部位使用核磁共振成像，目前为止最常见的关节评估是膝盖。vt.给出 未来十年，随着膝关节发病率的增加，膝关节MRI检查的数量也在增加 损伤和膝骨性关节炎的日益流行，临床上迫切需要减少 膝关节MRI的经济负担，最直接的方法是减少所需的总体时间 进行核磁共振检查。在过去的十年里，人们尝试了多种技术来加速 膝关节MRI包括并行成像、压缩传感、多层螺旋CT采集和三维成像 各向同性分辨率成像。然而，目前所有的方法都有局限性，包括降低的信噪比 比率、图像模糊、与呈现必要的组织对比不兼容，以及无法评估所有关节 结构。缺乏适当的加速方法也阻碍了定量磁共振成像，如T2弛豫时间 尽管标测图在检测关节退变的早期迹象方面有明显的价值，但它仍不能应用于临床。这 应用的目的是开发新的快速采集和重建技术，以最大限度地提高磁共振扫描仪 提高效率、改进成像管理并自动执行扫描工作流，最终目标是减少 增加膝关节MRI的经济负担，方便临床影像操作。我们提议的新方法将是 在开发高级深度学习重建的基础上，结合新颖的快速图像获取和 自动处理，所有这些都是由我们的研究小组首创的。我们建议开发、优化、 以及评估由所有临床序列和附加T2标测组成的快速5分钟膝关节MRI方案 序列，实现了全膝关节的快速成像，用于形态和定量评估 无缝整合到临床工作流程中。总体假设是一种快速5分钟的膝关节磁共振成像方案 可以相当于标准的35分钟临床膝关节磁共振检查方案。我们的建议包括三个具体目标： (I)开发一种稳健的深度学习方法，用于4分钟快速多平面形态膝关节成像， (2)开发一种深度学习方法，用于1分钟全膝关节覆盖的高分辨率T2标测，以及 (3)对膝骨性关节炎患者进行快速膝关节磁共振检查的综合评价。 这个项目的成功完成将提供一个快速的5分钟膝关节MRI方案，包括常规的临床 成像和额外的T2标测，可以在标准的15分钟临床时间段内完成。该协议将是 经过良好评估并在临床环境中实施，以促进进一步验证的传播。我们的方法 将提供一个独特的机会来改善联合医疗保健，降低医疗保健成本，并使 患有膝部疼痛和关节不适的人群。

PROJECT SUMMARY The high prevalence of knee pain in the general population has presented an immense challenge to public health, with significant health care and economic burden to our society. Magnetic resonance imaging (MRI) is the imaging modality of choice to evaluate patients with knee pain. Indeed, peripheral joints rank third as the most frequent body parts imaged using MRI, with the knee being by far the most common joint evaluated. Given the rise of the number of knee MRI examinations over the next decade with the increasing incidence of knee injuries and the increasing prevalence of knee osteoarthritis, there is an urgent clinical need to reduce the economic burden of knee MRI, with the most direct approach being to decrease the overall time required to perform the MRI examination. Over the past decade, multiple techniques have been attempted to accelerate knee MRI including parallel imaging, compressed sensing, multi-slice acquisition, and three-dimensional isotropic resolution imaging. However, all current methods have limitations, including decreased signal-to-noise ratio, image blurring, incompatibility to present necessary tissue contrasts, and inability to evaluate all joint structures. Lack of appropriate acceleration methods also prevents quantitative MRI such as T2 relaxation time mapping from being used clinically, despite its evident value for detecting early signs of joint degeneration. This application aims to develop novel rapid acquisition and reconstruction techniques to maximize MR scanner efficiency, improve imaging management, and automate scanning workflow, with the final goal of reducing the economic burden of knee MRI and facilitating clinical imaging operation. Our proposed new methods will be based on developing advanced deep learning reconstruction, combined with novel rapid image acquisition and automatic processing, all of which are pioneered by our research group. We propose developing, optimizing, and evaluating a rapid 5-minute knee MRI protocol consisting of all clinical sequences and additional T2 mapping sequences, enabling rapid imaging of the whole knee for both morphological and quantitative assessment with seamless incorporation into clinical workflow. The overall hypothesis is that a rapid 5-minute knee MRI protocol can be equivalent to the standard 35-minute clinical knee MR protocol. Our proposal includes three specific aims: (i) development of a robust deep learning method for a 4-minute rapid multi-planar morphological knee imaging, (ii) development of a deep learning method for a 1-minute whole-knee-covered high-resolution T2 mapping, and (iii) investigation of a comprehensive evaluation for rapid knee MR protocol in patients with knee osteoarthritis. Successful completion of this project will deliver a rapid 5-minute knee MRI protocol, including routine clinical imaging and additional T2 mapping that can fit into a standard 15-minute clinical time slot. This protocol will be well-evaluated and implemented in clinical settings to facilitate dissemination for further validation. Our methods would offer a unique opportunity to improve joint health care, reduce healthcare costs, and benefit a large population that suffers knee pain and joint discomfort.