Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Charlestown, Massachusetts, USA.
Harvard Medical School, Boston, Massachusetts, USA.
Magn Reson Med. 2024 Jul;92(1):98-111. doi: 10.1002/mrm.30045. Epub 2024 Feb 11.
This paper proposes a novel self-supervised learning framework that uses model reinforcement, REference-free LAtent map eXtraction with MOdel REinforcement (RELAX-MORE), for accelerated quantitative MRI (qMRI) reconstruction. The proposed method uses an optimization algorithm to unroll an iterative model-based qMRI reconstruction into a deep learning framework, enabling accelerated MR parameter maps that are highly accurate and robust.
Unlike conventional deep learning methods which require large amounts of training data, RELAX-MORE is a subject-specific method that can be trained on single-subject data through self-supervised learning, making it accessible and practically applicable to many qMRI studies. Using quantitative mapping as an example, the proposed method was applied to the brain, knee and phantom data.
The proposed method generates high-quality MR parameter maps that correct for image artifacts, removes noise, and recovers image features in regions of imperfect image conditions. Compared with other state-of-the-art conventional and deep learning methods, RELAX-MORE significantly improves efficiency, accuracy, robustness, and generalizability for rapid MR parameter mapping.
This work demonstrates the feasibility of a new self-supervised learning method for rapid MR parameter mapping, that is readily adaptable to the clinical translation of qMRI.
本文提出了一种新颖的自监督学习框架，该框架使用模型强化，即无参考潜在映射提取与模型强化（RELAX-MORE），用于加速定量磁共振成像（qMRI）重建。该方法使用优化算法将基于模型的迭代 qMRI 重建展开到深度学习框架中，从而实现高度准确和鲁棒的加速磁共振参数图。
与传统的深度学习方法需要大量训练数据不同，RELAX-MORE 是一种基于个体的方法，可以通过自监督学习在单个体数据上进行训练，使其易于使用且适用于许多 qMRI 研究。以定量映射为例，该方法应用于大脑、膝盖和幻影数据。
所提出的方法生成了高质量的磁共振参数图，可校正图像伪影、去除噪声，并恢复图像特征在图像条件不完善的区域。与其他最先进的传统和深度学习方法相比，RELAX-MORE 显著提高了快速磁共振参数映射的效率、准确性、鲁棒性和通用性。
这项工作证明了一种新的自监督学习方法用于快速磁共振参数映射的可行性，该方法易于适应 qMRI 的临床转化。
