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

用于膝关节病理学快速形态学和定量成像的深度学习技术

• 批准号: 10444468
• 负责人: Fang Liu
• 金额: $ 39.61万
• 依托单位: MASSACHUSETTS GENERAL HOSPITAL
• 依托单位国家: 美国
• 财政年份: 2022
• 资助国家: 美国
• 起止时间: 2022-06-01 至 2027-05-31
• 项目状态: 未结题
• 来源: https://reporter.nih.gov/project-details/10444468
• 关键词: 3-Dimensional; Acceleration; Adult; Advanced Development; Body part; Brain; Cartilage; Clinical; Costs and Benefits; Development; Economic Burden; Evaluation; Foundations; General Population; Goals; Health Care Costs; Health Personnel; Healthcare; High Prevalence; Image; Imaging Techniques; Incidence; Investigation; Joints; Knee; Knee Injuries; Knee Osteoarthritis; Knee joint; Magnetic Resonance Imaging; Medicare; Methods; Modeling; Morphology; Musculoskeletal; Noise; Pathology; Patients; Peripheral; Population; Prevalence; Protocols documentation; Public Health; Relaxation; Research; Resolution; Sampling; Scanning; Schedule; Signal Transduction; Slice; Societies; Structure; Techniques; Technology; Time; Tissues; Training; United States; Validation; Vertebral column; base; care burden; clinical imaging; clinical practice; deep learning; imaging modality; improved; joint destruction; knee pain; learning strategy; neural network; novel; operation; prevent; quantitative imaging; rapid technique; reconstruction; two-dimensional

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.

项目概要 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影像学选择。事实上，周围关节位居第三位。 使用 MRI 成像最常见的身体部位，其中膝关节是迄今为止最常评估的关节。给定 随着膝关节疾病发病率的增加，未来十年膝关节 MRI 检查的数量也会增加 损伤和膝骨关节炎患病率的增加，临床迫切需要减少 膝关节 MRI 的经济负担，最直接的方法是减少膝关节 MRI 所需的总时间 进行 MRI 检查。在过去的十年中，人们尝试了多种技术来加速 膝盖 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，当前的所有方法都有局限性，包括信噪比降低 比例、图像模糊、无法呈现必要的组织对比以及无法评估所有关节 结构。缺乏适当的加速方法也会妨碍定量 MRI，例如 T2 弛豫时间 尽管映射对于检测关节退化的早期迹象具有明显的价值，但其在临床上的应用尚未得到广泛应用。这 该应用旨在开发新颖的快速采集和重建技术，以最大限度地提高 MR 扫描仪的性能 效率、改进成像管理并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，方便临床影像操作。我们提出的新方法将是 基于开发先进的深度学习重建，结合新颖的快速图像采集和 自动化处理，所有这些都是我们研究小组首创的。我们建议开发、优化、 并评估由所有临床序列和附加 T2 映射组成的快速 5 分钟膝部 MRI 方案 序列，能够对整个膝盖进行快速成像，以进行形态学和定量评估 无缝融入临床工作流程。总体假设是，快速 5 分钟膝盖 MRI 方案 可以相当于标准的 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) 开发一种强大的深度学习方法，用于 4 分钟快速多平面形态膝盖成像， (ii) 开发 1 分钟全膝覆盖高分辨率 T2 映射的深度学习方法，以及 (iii) 对膝骨关节炎患者快速膝关节 MR 方案的综合评估研究。 该项目的成功完成将提供快速的 5 分钟膝关节 MRI 方案，包括常规临床 成像和额外的 T2 映射，可以适应标准的 15 分钟临床时间段。该协议将是 在临床环境中进行充分评估和实施，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来改善关节保健、降低医疗成本并使大量人员受益 患有膝盖疼痛和关节不适的人群。