Deep Learning Reconstruction for Rapid Multi-Component Relaxometry

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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.