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  1. Home
  3. Deep Learning For Brain Mri Artifact Correction: Current Challenges And Future Directions.
  1. Home
  3. Deep Learning For Brain Mri Artifact Correction: Current Challenges And Future Directions.

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Deep Learning for Brain MRI Artifact Correction: Current Challenges and Future Directions.

Jiangfan Yu1,2, Sibusiso Mdletshe1, Hamid Abbasi2,3

  • 1Department of Anatomy and Medical Imaging, Faculty of Medical and Health Sciences, University of Auckland, Auckland 1023, New Zealand.

Bioengineering (Basel, Switzerland)
|June 26, 2026

View abstract on PubMed

Summary
This summary is machine-generated.

Deep learning methods show promise for correcting artifacts in structural MRI scans, achieving high fidelity. Further research is needed to address challenges like hallucination and over-smoothing in clinical applications.

Keywords:
artifact correctionbrain MRIdeep learning

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Area of Science:

  • Medical Imaging
  • Artificial Intelligence
  • Neuroscience

Background:

  • Structural magnetic resonance imaging (sMRI) is crucial for diagnosing brain diseases.
  • Artifacts, such as motion artifacts, frequently corrupt sMRI scans, impacting diagnostic accuracy.
  • Deep learning (DL) algorithms are increasingly studied for artifact correction, but their clinical performance needs quantitative evaluation.

Purpose of the Study:

  • To quantitatively review deep learning-based artifact correction studies for clinical-field-strength sMRI (≥1.5T) in non-pediatric settings.
  • To assess the performance and identify challenges of current DL artifact correction methods.
  • To explore potential future directions, including physics-informed neural networks (PINNs).

Main Methods:

  • Conducted a structured literature review of 30 DL-based artifact correction studies.
  • Focused on studies using clinical-field-strength sMRI (≥1.5T) in non-pediatric populations.
  • Quantitatively analyzed performance metrics like SSIM and PSNR and identified factors contributing to hallucination/over-smoothing.

    • Main Results:

    • Current DL methods demonstrate promising artifact correction fidelity with high SSIM (0.92 ± 0.05) and PSNR (32.85 ± 4.53 dB).
    • Factors influencing hallucination and over-smoothing were linked to neural network architecture and training processes.
    • Frequency-aware neural networks show potential advantages for artifact correction.

    Conclusions:

    • DL-based artifact correction in sMRI shows significant potential for clinical applications.
    • Addressing hallucination and over-smoothing through optimized NN architecture and training is critical.
    • Future research should explore advanced DL paradigms like PINNs for robust artifact correction.