Ultra-Fast Knee MRI with Deep Learning

摘要 快速、可靠、稳定的定量膝关节MR成像将是研究关节的重要一步。 退行性变、损伤和骨关节炎（OA）。成分和形态特征提取的自动化 这是将有前途的定量分析转化为临床实践的重要步骤。 技术.它将能够分析大型患者队列，并协助放射科医生/临床医生增强 MRI的价值。 过去几年，通过使用深度学习，实现了多项人工任务的自动化 技术.随着大量带注释的数据和处理能力的可用性， 通过观察示例将数据转换为知识，监督学习今天可以实现 前所未有的挑战。除了图像分析和解释之外，深度学习还 彻底改变了管道的获取和重建方面。模型可以学习直接映射 在欠采样k空间和图像域之间。 虽然深度学习在肌肉骨骼成像中的应用显示出了有希望的结果， 控制设置，很好地理解，超出训练集的统计分布的泛化是 仍然是一个未被满足的挑战。在MRI中，当对训练模型进行测试时， 不同的成像协议或在不同的MRI系统上获取的图像。 通过这一提议，我们的目标是利用这一最新进展并填补现有空白。我们的目标是研究 能够同时加速MRI采集和自动化图像处理的新型集成模型 可以克服单一领域应用的局限性。快速图像采集和准确图像后处理 处理通常被认为是单独的问题。然而，神经网络优化设计 让我们有机会将两者结合起来，以最大限度地提高加速和基于机器的图像处理 和解释。我们将使用公开的基准数据集（FastMRI）和内部收集的数据集。 数据集来构建深度学习模型，能够在采样的MRI采集下准确重建。我们将 使用本研究过程中前瞻性采集的数据集来验证 开发方法。具体来说，我们将测试的假设，建议的综合管道可以应用 在临床环境中，为了获得与标准采集相当图像质量， 和可与人类再现性相媲美的自动化处理精度。 此外，我们建议将我们的注释图像数据集和训练模型作为共享资源， MRI重建和图像后处理技术的集中式、开放式评估平台。

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.