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

项目摘要 膝关节疼痛在普通人群中的高患病率对公众提出了巨大的挑战。 健康，给我们的社会带来重大的保健和经济负担。磁共振成像（MRI）是 评估膝关节疼痛患者的首选成像方式。事实上，外周关节排名第三， 使用MRI成像的最常见的身体部位，膝盖是迄今为止最常见的关节评估。给定 在未来十年中，随着膝关节疾病发病率的增加， 损伤和膝关节骨关节炎患病率的增加，迫切需要减少 膝关节MRI的经济负担，最直接的方法是减少 进行MRI检查。在过去的十年中，已经尝试了多种技术来加速 膝关节MRI，包括并行成像、压缩感知、多切片采集和三维 各向同性分辨率成像然而，所有当前的方法都有局限性，包括降低的信噪比 比率、图像模糊、不适合提供必要的组织对比度，以及无法评价所有关节 结构.缺乏适当的加速方法也阻碍了定量MRI，如T2弛豫时间 尽管它对于检测关节退行性变的早期迹象有明显的价值，但它在临床上的应用。这 该应用旨在开发新的快速采集和重建技术，以最大限度地提高MR扫描仪 效率，改善成像管理，自动化扫描工作流程，最终目标是减少 减轻膝关节MRI的经济负担，方便临床影像学操作。我们提出的新方法将 基于开发先进的深度学习重建，结合新颖的快速图像采集， 自动化处理，所有这些都是我们研究小组开创的。我们建议开发，优化， 并评价快速5分钟膝关节MRI方案，包括所有临床序列和额外的T2标测 序列，能够对整个膝关节进行快速成像，以进行形态和定量评估， 无缝融入临床工作流程。总体假设是，快速5分钟膝关节MRI方案 可等同于标准35分钟临床膝关节MR方案。我们的建议包括三个具体目标： (i)为4分钟快速多平面形态学膝关节成像开发稳健的深度学习方法， (ii)开发用于1分钟全膝覆盖高分辨率T2标测的深度学习方法，以及 (iii)膝关节骨关节炎快速MR检查综合评价研究 该项目的成功完成将提供一个快速的5分钟膝关节MRI方案，包括常规临床 成像和额外的T2标测，可以适应标准的15分钟临床时间段。本方案将 在临床环境中得到良好评价和实施，以促进传播，以进一步验证。我们的方法 将提供一个独特的机会，以改善联合医疗保健，降低医疗保健成本，并受益于大 遭受膝关节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.