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Machine learning

A generalist model for enhancing brain MRIs

Nature Biomedical Engineering volume 9, pages 441–442 (2025)Cite this article

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Training a model on fetal and paediatric magnetic resonance images with synthesized artefacts enhances the model’s generalization across downstream tasks and patient populations.

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Fig. 1: A generalist machine-learning model for enhancing MRIs.

References

  1. Coupé, P. et al. Hum. Brain Mapp. 36, 4758–4770 (2015).

    Article  PubMed  PubMed Central  Google Scholar 

  2. Pardoe, H. R., Hiess, R. K. & Kuzniecky, R. NeuroImage 135, 177–185 (2016).

    Article  PubMed  Google Scholar 

  3. Kruggel, F., Turner, J., Muftuler, L. T. & Alzheimer’s Disease Neuroimaging Initiative. NeuroImage 49, 2123–2133 (2010).

    Article  PubMed  Google Scholar 

  4. Johnson, P. M. et al. in Proc. Machine Learning for Medical Image Reconstruction: 4th International Workshop (eds Haq, N. et al.) 25–34 (Springer, 2021).

  5. Iglesias, J. E. et al. Sci. Adv. 9, eadd3607 (2023).

    Article  PubMed  PubMed Central  Google Scholar 

  6. Sun, Y., Wang, L., Li, G., Lin, W. & Wang, L. Nat. Biomed. Eng. https://doi.org/10.1038/s41551-024-01283-7 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  7. Vernooij, M. W. et al. Neuroradiology 61, 633–642 (2019).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  8. Moor, M. et al. Nature 616, 259–265 (2023).

    Article  CAS  PubMed  Google Scholar 

  9. Stallone, M. et al. Preprint at https://arxiv.org/abs/2407.13739 (2024).

  10. Zhou, Y. et al. Nature 622, 156–163 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  11. Lu, M. Y. et al. Nat. Med. 30, 863–874 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  12. Huang, K. et al. Nat. Med. 30, 3601–3613 (2024).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  13. Billot, B. et al. Proc. Natl Acad. Sci. USA 120, e2216399120 (2023).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

  14. Khosravi, B. et al. eBioMedicine 104, 105174 (2024).

    Article  PubMed  PubMed Central  Google Scholar 

  15. Seyyed-Kalantari, L. et al. Nat. Med. 27, 2176–2182 (2021).

    Article  CAS  PubMed  PubMed Central  Google Scholar 

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Authors and Affiliations

  1. Department of Experimental Psychology, University College London, London, UK

    Yael Balbastre

  2. Athinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, MA, USA

    Bruce Fischl

Authors
  1. Yael Balbastre
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  2. Bruce Fischl
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Corresponding author

Correspondence to Yael Balbastre.

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Competing interests

B.F. is an advisor to DeepHealth, a company whose medical pursuits focus on medical imaging and measurement technologies. His interests were reviewed and are managed by Massachusetts General Hospital and Partners HealthCare in accordance with their conflict-of-interest policies. Y.B. has no competing interests.

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Balbastre, Y., Fischl, B. A generalist model for enhancing brain MRIs. Nat. Biomed. Eng 9, 441–442 (2025). https://doi.org/10.1038/s41551-024-01320-5

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