Generating synthetic hypoxia images from FDG-PET using Generative Adversarial Networks (GANs)

A. Traverso, C. Rao, A. Briassouli, A. Dekker, D. De Ruysscher, W. van Elmpt

Research output: Contribution to journal › Conference Abstract/Poster in journal › Academic

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@article{56678460e0cb4ac08e36b042cb1db58c,

title = "Generating synthetic hypoxia images from FDG-PET using Generative Adversarial Networks (GANs)",

author = "A. Traverso and C. Rao and A. Briassouli and A. Dekker and {De Ruysscher}, D. and {van Elmpt}, W.",

year = "2022",

month = may,

day = "1",

doi = "10.1016/S0167-8140(22)03573-3",

language = "English",

volume = "170",

pages = "S1396--S1397",

journal = "Radiotherapy and Oncology",

issn = "0167-8140",

publisher = "Elsevier Ireland Ltd",

}

TY - JOUR

T1 - Generating synthetic hypoxia images from FDG-PET using Generative Adversarial Networks (GANs)

AU - Traverso, A.

AU - Rao, C.

AU - Briassouli, A.

AU - Dekker, A.

AU - De Ruysscher, D.

AU - van Elmpt, W.

PY - 2022/5/1

Y1 - 2022/5/1

U2 - 10.1016/S0167-8140(22)03573-3

DO - 10.1016/S0167-8140(22)03573-3

M3 - Conference Abstract/Poster in journal

SN - 0167-8140

VL - 170

SP - S1396-S1397

JO - Radiotherapy and Oncology

JF - Radiotherapy and Oncology

ER -