Ultra-Fast Knee MRI with Deep Learning

摘要 快速、稳健和可靠的定量膝关节磁共振成像将是关节研究的重要一步 退变、损伤和骨关节炎(OA)。成分和形态特征提取的自动化 在膝关节组织中，它是将有希望的定量的 技巧。它将使大量患者队列的分析成为可能，并帮助放射科医生/临床医生扩大 磁共振成像的价值。 在过去的几年里，通过使用深度学习，已经实现了几个人工任务的自动化 技巧。大量带注释的数据的可用性和处理能力，使用概念 通过观察实例将数据转换为知识，监督学习今天可以完成 挑战从来没有展示过。除了图像分析和解释，深度学习还 使管道的收购和重建方面发生革命性变化。模型可以学习直接映射 在欠采样k空间和图像域之间。 而深度学习在肌肉骨骼成像中的应用显示了良好的结果 在受控设置下，很好地理解，超出训练集的统计分布的泛化是 这仍然是一个未被满足的挑战。在磁共振成像中，当训练好的模型在 不同的成像协议或在不同的MRI系统上获取的图像。 通过这项提议，我们的目标是利用这一最近的进展并填补现有的差距。我们的目标是研究 能够同时加速MRI采集和图像处理自动化的新型集成模型 克服了单域应用的局限性。快速图像采集和准确的图像发布 处理通常被认为是单独的问题。然而，神经网络的优化设计 使我们有机会集成两者，以最大限度地提高加速和基于机器的图像处理能力 和解释。我们将使用公开可用的基准数据集(FastMRI)和内部收集的数据 数据集，以构建深度学习模型，能够在采样的MRI采集下准确重建。我们会 使用在本研究过程中预期获得的数据集来验证该方法的临床适用性 开发的方法。具体地说，我们将测试所提议的集成管道可以应用的假设 在临床环境下进行快速和智能膝关节扫描，获得与标准采集相当的图像质量 以及可与人类重复性媲美的自动化处理精度。 此外，我们建议使我们的带注释的图像数据集和训练的模型成为共享资源 集中、开放的MRI重建和图像后处理技术评估平台。

ABSTRACT Fast, robust and reliable quantitative knee joint MR imaging would be a significant step forward in studying joint degeneration, injury and osteoarthritis (OA). Automation of compositional and morphological feature extraction of the tissues in the knee it is an essential step for translation to clinical practice of promising quantitative techniques. It would enable the analysis of large patient cohorts and assist the radiologist/clinician in augmenting the value of MRI. Automation of several human tasks has been achieved in the last few years by the usage of Deep Learning techniques. With the availability of large amounts of annotated data and processing power, using the concepts of transforming data to knowledge by the observation of examples, supervised learning can today accomplish challenges never demonstrated before. In addition to image analysis and interpretation, Deep Learning is revolutionizing the acquisition and reconstruction aspects of the pipeline. Models can learn a direct mapping between under sampled k-space and image domain. While Deep Learning application to musculoskeletal imaging showed promising results when applied in a controlled setting, it is well understood that generalization beyond the statistical distribution of the training set is still an unmet challenge. In MRI this translates into poor performances when trained models are tested on different imaging protocols or images acquired on different MRI systems. With this proposal, we aim to leverage on this recent advancement and filling the existing gaps. We aim to study novel integrated models able to simultaneously accelerate MRI acquisition and automate the image processing that can overcome the limitation of single domain application. Fast image acquisition and accurate image post processing are typically considered to be separate problems. However, the neural networks optimization design gives us an opportunity to integrate the two to maximize both acceleration and machine-based image processing and interpretation. We will use both publicly available benchmark dataset (FastMRI) and internally collected dataset to build deep learning models able to accurately reconstruct under sampled MRI acquisitions. We will use a dataset prospectively acquired during the course of this study to validate the clinical applicability of the developed methods. Specifically, we will test the hypothesis that the proposed integrated pipeline can be applied in clinical setting for a fast and intelligent knee scan obtaining image quality comparable to standard acquisition and automated processing accuracy comparable with human reproducibility. Additionally, we propose to make our annotated image datasets and trained models a shared resource, a centralized, open evaluation platform for MRI reconstruction and image post processing techniques.