Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study

Aditya Rastogi, Gianluca Brugnara, Martha Foltyn-Dumitru, Mustafa Ahmed Mahmutoglu, Chandrakanth J Preetha, Erich Kobler, Irada Pflüger, Marianne Schell, Katerina Deike-Hofmann, Tobias Kessler, Martin J van den Bent, Ahmed Idbaih, Michael Platten, Alba A Brandes, Burt Nabors, Roger Stupp, Denise Bernhardt, Jürgen Debus, Amir Abdollahi, Thierry Gorlia, Jörg-Christian Tonn, Michael Weller, Klaus H Maier-Hein, Alexander Radbruch, Wolfgang Wick, Martin Bendszus, Hagen Meredig, Felix T Kurz, Philipp Vollmuth

Open source

DOI: 10.1016/s1470-2045(23)00641-1
Published: 2024-03
Container: The Lancet Oncology
Publisher: Elsevier BV
Open access: unknown

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Cite this work

BibTeX

@article{allodium:10.1016/s1470-2045-23-00641-1,
  title = {Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study},
  author = {Aditya Rastogi and Gianluca Brugnara and Martha Foltyn-Dumitru and Mustafa Ahmed Mahmutoglu and Chandrakanth J Preetha and Erich Kobler and Irada Pflüger and Marianne Schell and Katerina Deike-Hofmann and Tobias Kessler and Martin J van den Bent and Ahmed Idbaih and Michael Platten and Alba A Brandes and Burt Nabors and Roger Stupp and Denise Bernhardt and Jürgen Debus and Amir Abdollahi and Thierry Gorlia and Jörg-Christian Tonn and Michael Weller and Klaus H Maier-Hein and Alexander Radbruch and Wolfgang Wick and Martin Bendszus and Hagen Meredig and Felix T Kurz and Philipp Vollmuth},
  year = {2024},
  journal = {The Lancet Oncology},
  doi = {10.1016/s1470-2045(23)00641-1},
  url = {https://doi.org/10.1016/s1470-2045(23)00641-1}
}

RIS

TY  - JOUR
TI  - Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study
AU  - Aditya Rastogi
AU  - Gianluca Brugnara
AU  - Martha Foltyn-Dumitru
AU  - Mustafa Ahmed Mahmutoglu
AU  - Chandrakanth J Preetha
AU  - Erich Kobler
AU  - Irada Pflüger
AU  - Marianne Schell
AU  - Katerina Deike-Hofmann
AU  - Tobias Kessler
AU  - Martin J van den Bent
AU  - Ahmed Idbaih
AU  - Michael Platten
AU  - Alba A Brandes
AU  - Burt Nabors
AU  - Roger Stupp
AU  - Denise Bernhardt
AU  - Jürgen Debus
AU  - Amir Abdollahi
AU  - Thierry Gorlia
AU  - Jörg-Christian Tonn
AU  - Michael Weller
AU  - Klaus H Maier-Hein
AU  - Alexander Radbruch
AU  - Wolfgang Wick
AU  - Martin Bendszus
AU  - Hagen Meredig
AU  - Felix T Kurz
AU  - Philipp Vollmuth
PY  - 2024
JO  - The Lancet Oncology
DO  - 10.1016/s1470-2045(23)00641-1
UR  - https://doi.org/10.1016/s1470-2045(23)00641-1
ER  -

APA

Rastogi, A., Brugnara, G., Foltyn-Dumitru, M., Mahmutoglu, M. A., Preetha, C. J., Kobler, E., Pflüger, I., Schell, M., Deike-Hofmann, K., Kessler, T., Bent, M. J. V. D., Idbaih, A., Platten, M., Brandes, A. A., Nabors, B., Stupp, R., Bernhardt, D., Debus, J., Abdollahi, A., Gorlia, T., Tonn, J., Weller, M., Maier-Hein, K. H., Radbruch, A., Wick, W., Bendszus, M., Meredig, H., Kurz, F. T., & Vollmuth, P. (2024). Deep-learning-based reconstruction of undersampled MRI to reduce scan times: a multicentre, retrospective, cohort study. The Lancet Oncology. https://doi.org/10.1016/s1470-2045(23)00641-1

Source records

crossref · retrieved 2026-09-26T13:34:14.605Z