Advances in physics-informed deep learning for imaging data: a review of methods and applications

Abstract

Abstract Deep learning (DL) has transformed numerous application domains owing its ability to automatically extract features from data. However, training DL models typically requires large datasets, which are often unavailable for scientific research. In recent years, the integration of physics with DL, known as physics-informed DL (PIDL), has emerged as a promising approach that enables models to learn from limited data. This survey provides an overview of recent advancements in PIDL methods, summarizing the various incorporation techniques and physical priors used in inverse imaging applications. This review highlights the strengths of PIDL, including improved interpretability, data efficiency, robustness, and generalization. It also discusses shortcomings, such as the lack of formulated physics representations, the need for domain-specific knowledge, and the high computational costs. Although PIDL is a relatively new methodology, it has significant potential for creating resilient, efficient, precise, and adaptable models for real-world applications. This survey offers insights into the fundamentals of PIDL in imaging and emphasizes its growing importance in bridging the gap between data-driven approaches and physics-based modeling in scientific research. As the field progresses, PIDL is likely to play an increasingly crucial role in advancing scientific understanding and real-world applications.

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