Model-based Reconstruction with Learning: From Unsupervised to Supervised and Beyond

TL;DR

This paper reviews the evolution of image reconstruction techniques in medical imaging, highlighting the integration of classical model-based methods with modern learning-based approaches, including unsupervised and supervised learning, to improve image quality from limited data.

Contribution

It provides a comprehensive overview of recent advances in combining model-based and learning-based reconstruction methods, emphasizing new frameworks that integrate multiple learned models.

Findings

Learning-based methods outperform classical techniques in limited data scenarios.

Supervised learning approaches achieve higher reconstruction accuracy.

Hybrid models effectively combine physics-based and data-driven information.

Abstract

Many techniques have been proposed for image reconstruction in medical imaging that aim to recover high-quality images especially from limited or corrupted measurements. Model-based reconstruction methods have been particularly popular (e.g., in magnetic resonance imaging and tomographic modalities) and exploit models of the imaging system's physics together with statistical models of measurements, noise and often relatively simple object priors or regularizers. For example, sparsity or low-rankness based regularizers have been widely used for image reconstruction from limited data such as in compressed sensing. Learning-based approaches for image reconstruction have garnered much attention in recent years and have shown promise across biomedical imaging applications. These methods include synthesis dictionary learning, sparsifying transform learning, and different forms of deep learning involving complex neural networks. We briefly discuss classical model-based reconstruction methods and then review reconstruction methods at the intersection of model-based and learning-based paradigms in detail. This review includes many recent methods based on unsupervised learning, and supervised learning, as well as a framework to combine multiple types of learned models together.