Deep Learning Reconstruction for Rapid Multi-Component Relaxometry

项目总结 松弛测量法是量化组织特性的最常用的MRI技术之一。多组分 弛豫测量法测量多种水组分的松弛特性，因此提供了两种灵敏的 和特定的磁共振生物标志物，用于评估组织的组成和微观结构，如软骨和 髓鞘。然而，由于需要拟合复杂的噪声敏感的MR信号模型，多分量 松弛成像需要重复扫描，扫描时间较长，限制了其广泛的临床应用。目标是 这项研究的建议是通过利用最新的深度学习技术来开发一种新的方法 以快速、临床可行的方式实现准确和高质量的多分量松弛标测。 虽然最近的许多深度学习重建研究都集中在静态MR图像的快速成像上 尽管深度学习在加速松弛映射中的应用前景看好，但它的应用受到了限制。在……里面 在这个项目中，我们建议开发、优化和评估一种新的深度学习技术，该技术能够 对具有多组分松弛特性的组织进行准确的表征和量化。在基础上建设 我们新开发的用于快速成像的深度学习方法的基础，我们提出的方法将 利用高效的端到端卷积神经网络将欠采样的磁共振图像直接转换为 用于多分量松弛的精确参数贴图。一种新的基于数值Bloch仿真的算法 用于精确模拟多组分松弛行为，以确保准确性、可靠性和 深度学习训练过程中的稳健性。生成性对抗性网络将被纳入，以进一步 增强重建性能，确保高质量的多分量松弛映射 加速率。该提案还将探索新的数据增强方法，通过使用合成 图像数据集，以创建可广泛推广的深度学习模型。这确保了提议的深度 学习方法可以应用于许多身体区域的不同松弛类型(例如T2、T1和T1ρ)，甚至 如果有限的训练数据集可用。我们的建议包括两个具体目标：(一)开发基于模型的 用于快速多分量弛豫测量的深度学习方法，以及(Ii)研究合成图像的使用 用于训练深度学习模型的数据集。总体假设是所提出的重建技术 可以提供一个独特的机会，通过利用 最新的深度学习技术，产生了准确、高效和可靠的模型，可以广泛应用 一概而论。该项目的成功完成将提供一种临床上适用的多组件 松弛测量技术，用于更好地研究、了解和分期疾病，如骨关节炎和 多发性硬化症。这一概念可以显著促进定量MRI在临床翻译中的应用。

PROJECT SUMMARY Relaxometry is among the most used MRI technique for quantifying tissue properties. Multi-component relaxometry measures the relaxation characteristics of multiple water components, thus delivers both sensitive and specific MR biomarkers for evaluating composition and microstructure of tissues such as cartilage and myelin. However, due to the need to fit a complicated noise-sensitive MR signal model, multi-component relaxation mapping requires repeated scans with a long scan time, limiting its widespread clinical use. The goal of this research proposal is to develop a novel method via leveraging the latest deep learning techniques for realizing accurate and high-quality multi-component relaxation mapping at a rapid, clinical feasible acquisition. While many recent deep learning reconstruction studies have focused on rapid imaging for static MR images with promising results, applications of deep learning for accelerated relaxation mapping have been limited. In this project, we propose to develop, optimize, and evaluate a new deep learning technique that enables accurate characterization and quantification of tissues with multi-component relaxation properties. Building on the foundation of our newly developed deep learning method for rapid imaging, our proposed approach will utilize an efficient end-to-end convolutional neural network to directly convert undersampled MR images into accurate parametric maps for multi-component relaxation. A novel numerical Bloch-simulation based algorithm is applied to precisely model the multi-component relaxation behavior to ensure accuracy, reliability, and robustness in the deep learning training process. Generative adversarial network will be incorporated to further enhance the reconstruction performance to ensure high-quality multi-component relaxation mapping at high acceleration rates. This proposal will also explore new data augmentation approaches by using synthetic image datasets to create a widely generalizable deep learning model. This ensures that the proposed deep learning method can be applied to different relaxation types (e.g., T2, T1 and T1ρ) in many body regions, even if limited training datasets are available. Our proposal includes two specific aims: (i) to develop model-based deep learning method for rapid multi-component relaxometry, and (ii) to investigate the use of synthetic image datasets for training deep learning model. The overall hypothesis is that the proposed reconstruction technique can offer a unique opportunity to explore the acceleration of multi-component relaxometry by leveraging the latest deep learning techniques, resulting in an accurate, efficient, and reliable model that can be widely generalizable. Successful completion of the project will provide a clinically applicable multi-component relaxometry technique for better studying, understanding, and staging diseases such as osteoarthritis and multiple sclerosis. This concept could significantly advance quantitative MRI for clinical translation.