Chengyue Wu

Assistant Professor


Curriculum vitae



Imaging Physics

The University of Texas MD Anderson Cancer Center



An untrained deep learning method for reconstructing dynamic MR images from accelerated model‐based data


Journal article


Kalina P. Slavkova, J. DiCarlo, Viraj Wadhwa, Sidharth Kumar, Chengyue Wu, John Virostko, T. Yankeelov, Jonathan I. Tamir
Magnetic Resonance in Medicine, 2022

Semantic Scholar ArXiv DOI PubMed
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APA   Click to copy
Slavkova, K. P., DiCarlo, J., Wadhwa, V., Kumar, S., Wu, C., Virostko, J., … Tamir, J. I. (2022). An untrained deep learning method for reconstructing dynamic MR images from accelerated model‐based data. Magnetic Resonance in Medicine.


Chicago/Turabian   Click to copy
Slavkova, Kalina P., J. DiCarlo, Viraj Wadhwa, Sidharth Kumar, Chengyue Wu, John Virostko, T. Yankeelov, and Jonathan I. Tamir. “An Untrained Deep Learning Method for Reconstructing Dynamic MR Images from Accelerated Model‐Based Data.” Magnetic Resonance in Medicine (2022).


MLA   Click to copy
Slavkova, Kalina P., et al. “An Untrained Deep Learning Method for Reconstructing Dynamic MR Images from Accelerated Model‐Based Data.” Magnetic Resonance in Medicine, 2022.


BibTeX   Click to copy

@article{kalina2022a,
  title = {An untrained deep learning method for reconstructing dynamic MR images from accelerated model‐based data},
  year = {2022},
  journal = {Magnetic Resonance in Medicine},
  author = {Slavkova, Kalina P. and DiCarlo, J. and Wadhwa, Viraj and Kumar, Sidharth and Wu, Chengyue and Virostko, John and Yankeelov, T. and Tamir, Jonathan I.}
}

Abstract

To implement physics‐based regularization as a stopping condition in tuning an untrained deep neural network for reconstructing MR images from accelerated data.


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