论文标题
物理驱动的生物医学磁共振的合成数据学习
Physics-driven Synthetic Data Learning for Biomedical Magnetic Resonance
论文作者
论文摘要
深度学习创新了计算成像领域。它的瓶颈之一是不可用的或不足的培训数据。本文回顾了新兴的范式，基于物理学的数据合成（iPad），可以在没有或很少的实际数据的情况下提供生物医学磁共振共鸣的巨大训练数据。遵循磁共振的物理定律，iPadS从微分方程或分析解决方案模型中产生信号，从而使学习更加可扩展，可解释和更好地保护隐私。讨论了iPad学习的关键组成部分，包括信号生成模型，基本深度学习网络结构，增强的数据生成和学习方法。 iPad的巨大潜力已通过快速成像，超快信号重建和准确的参数定量中的代表性应用。最后，讨论了开放的问题和未来的工作。
Deep learning has innovated the field of computational imaging. One of its bottlenecks is unavailable or insufficient training data. This article reviews an emerging paradigm, imaging physics-based data synthesis (IPADS), that can provide huge training data in biomedical magnetic resonance without or with few real data. Following the physical law of magnetic resonance, IPADS generates signals from differential equations or analytical solution models, making the learning more scalable, explainable, and better protecting privacy. Key components of IPADS learning, including signal generation models, basic deep learning network structures, enhanced data generation, and learning methods are discussed. Great potentials of IPADS have been demonstrated by representative applications in fast imaging, ultrafast signal reconstruction and accurate parameter quantification. Finally, open questions and future work have been discussed.
