Dynamic cone-beam CT reconstruction using spatial and temporal implicit neural representation learning (STINR).
You ZhangHua-Chieh ShaoTinsu PanTielige MengkePublished in: Physics in medicine and biology (2023)
STINR offers a general framework allowing accurate dynamic CBCT reconstruction for image-guided radiotherapy. It is a one-shot learning method that does not rely on pre-training and is not susceptible to generalizability issues. It also allows natural super-resolution. It can be readily applied to other imaging modalities as well.
Keyphrases
- cone beam
- image quality
- high resolution
- early stage
- computed tomography
- dual energy
- contrast enhanced
- locally advanced
- radiation therapy
- radiation induced
- magnetic resonance imaging
- positron emission tomography
- virtual reality
- cone beam computed tomography
- magnetic resonance
- mass spectrometry
- squamous cell carcinoma
- pet ct
- photodynamic therapy
- rectal cancer