Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: Applications to a large-scale, clinically heterogeneous dataset.
- DOI
- 10.1016/j.media.2026.104295
- Published
- 2027 Jan
- Container
- Medical image analysis
- Publisher
- Not recorded
- Open access
- no
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Cite this work
BibTeX
@article{allodium:10.1016/j.media.2026.104295,
title = {Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: Applications to a large-scale, clinically heterogeneous dataset.},
author = {van Lune J and Mandija S and van der Heide O and Maspero M and Schilder MB and Dankbaar JW and van den Berg CAT and Sbrizzi A},
year = {2027},
journal = {Medical image analysis},
doi = {10.1016/j.media.2026.104295},
url = {https://doi.org/10.1016/j.media.2026.104295}
}RIS
TY - JOUR TI - Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: Applications to a large-scale, clinically heterogeneous dataset. AU - van Lune J AU - Mandija S AU - van der Heide O AU - Maspero M AU - Schilder MB AU - Dankbaar JW AU - van den Berg CAT AU - Sbrizzi A PY - 2027 JO - Medical image analysis DO - 10.1016/j.media.2026.104295 UR - https://doi.org/10.1016/j.media.2026.104295 ER -
APA
J, V. L., S, M., O, V. D. H., M, M., MB, S., JW, D., CAT, V. D. B., & A, S. (2027). Quantitative mapping from conventional MRI using self-supervised physics-guided deep learning: Applications to a large-scale, clinically heterogeneous dataset.. Medical image analysis. https://doi.org/10.1016/j.media.2026.104295
Source records
- pubmed · retrieved 2026-11-10T12:15:52.491Z
- europe-pmc · retrieved 2026-11-10T12:15:52.498Z