Complexities of deep learning-based undersampled MR image reconstruction

📚 Citation

C. R. Noordman, D. Yakar, J. S. Bosma, F.F.J. Simonis and H. Huisman, "Complexities of deep learning-based undersampled MR image reconstruction." European Radiology Experimental 7, 58 (2023).

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📖 Abstract

Artificial intelligence has opened a new path of innovation in magnetic resonance (MR) image reconstruction of undersampled k-space acquisitions. This review offers readers an analysis of the current deep learning-based MR image reconstruction methods. The literature in this field shows exponential growth, both in volume and complexity, as the capabilities of machine learning in solving inverse problems such as image reconstruction are explored. We review the latest developments, aiming to assist researchers and radiologists who are developing new methods or seeking to provide valuable feedback. We shed light on key concepts by exploring the technical intricacies of MR image reconstruction, highlighting the importance of raw datasets and the difficulty of evaluating diagnostic value using standard metrics.