Synthetic contrast-enhanced computed tomography generation using a deep convolutional neural network for cardiac substructure delineation in breast cancer radiation therapy: a feasibility study.
Jaehee ChunJee Suk ChangCaleb OhInKyung ParkMin Seo ChoiChae-Seon HongHojin KimGowoon YangJin Young MoonSeung Yeun ChungYoung Joo SuhJin Sung KimPublished in: Radiation oncology (London, England) (2022)
Our findings displayed the feasibility of SCECT generation from NCT and its potential for cardiac substructure delineation in patients who underwent breast radiation therapy.
Keyphrases
- contrast enhanced
- radiation therapy
- computed tomography
- convolutional neural network
- magnetic resonance imaging
- diffusion weighted
- end stage renal disease
- magnetic resonance
- left ventricular
- ejection fraction
- newly diagnosed
- positron emission tomography
- deep learning
- prognostic factors
- diffusion weighted imaging
- locally advanced
- radiation induced
- peritoneal dialysis
- squamous cell carcinoma
- young adults
- pet ct
- image quality
- childhood cancer