Deep Learning Reconstruction for Rapid Multi-Component Relaxometry

快速多分量松弛测量的深度学习重建

• 批准号: 10598038
• 负责人: Fang Liu
• 金额: $ 21.02万
• 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: 2022-04-01 至 2024-12-31
• 项目状态: 已结题
• 来源: https://reporter.nih.gov/project-details/10598038
• 关键词: Acceleration; Address; Algorithms; Anatomy; Architecture; Behavior; Biochemical; Biological Markers; Body Regions; Cartilage; Characteristics; Clinical; Computer Vision Systems; Data; Data Set; Degenerative polyarthritis; Demyelinations; Disease; Ensure; Environment; Foundations; Goals; Image; Imaging Techniques; Knee; Knee Osteoarthritis; Knowledge; Magnetic Resonance Imaging; Maps; Measures; Methods; Modeling; Multiple Sclerosis; Myelin; Noise; Pathology; Patients; Performance; Process; Property; Protons; Relaxation; Reproducibility; Research; Research Proposals; Resolution; Sampling; Scanning; Signal Transduction; Staging; Structure; System; Techniques; Time; Tissues; Training; Water; clinical application; clinical translation; contrast imaging; convolutional neural network; deep learning; deep learning model; digital; generative adversarial network; human tissue; image reconstruction; improved; learning strategy; neural network; novel; pre-clinical; prospective; rapid technique; reconstruction; simulation; tissue mapping

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.

项目摘要 松弛计是量化组织特性的最常用的MRI技术之一。多组件 放松测量多个水成分的松弛特征，因此可以提供两个敏感的 以及用于评估软骨和组织的组成和微观结构的特定MR生物标志物，例如软骨和 髓线。但是，由于需要拟合复杂的噪声敏感MR信号模型，多组分 放松映射需要在扫描时间长的时间内重复扫描，从而限制其宽度临床的使用。目标 这项研究建议是通过利用最新的深度学习技术来开发一种新颖的方法 在快速，临床可行的采集中实现准确和高质量的多组分放松映射。 尽管许多最近的深度学习重建研究都集中在静态MR图像的快速成像上 通过有希望的结果，深度学习在加速放松映射中的应用受到限制。在 我们建议该项目开发，优化和评估一种新的深度学习技术，以实现 具有多组分放松特性的组织的准确表征和数量。建立 我们新开发的深度学习方法的基础，我们提出的方法将 利用有效的端到端卷积神经网络直接将其采样的MR图像转换为 用于多组分放松的精确参数图。一种新型的基于Bloch仿真的算法 应用于精确建模多组分放松行为，以确保准确性，可靠性和 深度学习培训过程中的鲁棒性。生成对抗网络将合并以进一步 提高重建性能，以确保高质量的多组分放松映射 加速度。该建议还将通过使用合成来探索新的数据增强方法 图像数据集以创建一个广泛的深度学习模型。这确保了拟议的深处 学习方法可以应用于许多身体区域的不同放松类型（例如T2，T1和T1ρ），甚至 如果有限的培训数据集可用。我们的建议包括两个具体目标：（i）开发基于模型的目标 快速多组分松弛计的深度学习方法，以及（ii）研究合成图像的使用 用于培训深度学习模型的数据集。总体假设是提出的重建技术 可以提供一个独特的机会来探索多组分放松的加速 最新的深度学习技术，导致了一种准确，高效且可靠的模型，可以广泛 可推广。该项目的成功完成将提供临床适用的多组件 宽松计，用于更好的研究，理解和分期疾病，例如骨关节炎和 多发性硬化症。这个概念可以显着提高临床翻译的定量MRI。