Exploring Methods for MRI Artifact Correction
A Scoping Review
DOI:
https://doi.org/10.15665/rp.v24i1.3853Keywords:
artifact, MRI, Deep Learning, Image Correction, UNet, CNNAbstract
Artifact correction in magnetic resonance imaging (MRI) spans from acquisition/reconstruction and hardware strategies to rapidly evolving deep learning (DL) approaches. We conducted a PRISMA-ScR–aligned scoping review to map what is corrected, how it is evaluated, and where evidence gaps persist. PubMed and Scopus were searched over the last five years and complemented by hand-searching. For each record we charted artifact family, MRI sequence and field strength, data source (real vs. simulated), method class, evaluation metrics, and code/data availability. The core synthesis comprises 16 MRI studies: 11 MRI+DL investigations (dominated by U-Net variants with some recurrent/transformer models) and 5 traditional or hybrid MRI techniques (e.g., motion-robust acquisitions, metal-artifact reduction). Two additional DL papers in related modalities were retained as context only to discuss transferability and were excluded from counts, tables, and metrics. DL methods show strong gains in targeted scenarios, while traditional techniques remain reliable baselines. However, heterogeneity in datasets and protocols, scarce multicenter validation, and the lack of open, task-standardized benchmarks limit comparability and clinical generalizability.
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