Evaluating a radiotherapy deep learning synthetic CT algorithm for PET-MR attenuation correction in the pelvis.
Jonathan J WyattSandeep KaushikCristina CozziniRachel A PearsonGeorge PetridesFlorian WiesingerHazel M McCallumRoss J MaxwellPublished in: EJNMMI physics (2024)
Attenuation correction using this radiotherapy ZTE-based sCT algorithm was substantially more accurate than current MRAC methods with only a 40 s increase in MR acquisition time. This did not impact tumour delineation but did significantly improve the accuracy of whole-image and tumour SUV measurements, which were clinically equivalent to CTAC. This suggests PET images reconstructed with sCTAC would enable accurate quantitative PET images to be acquired on a PET-MR scanner.
Keyphrases
- deep learning
- positron emission tomography
- computed tomography
- pet ct
- contrast enhanced
- convolutional neural network
- artificial intelligence
- machine learning
- early stage
- pet imaging
- high resolution
- magnetic resonance
- image quality
- magnetic resonance imaging
- locally advanced
- radiation induced
- squamous cell carcinoma
- optical coherence tomography
- mass spectrometry
- dual energy
- rectal cancer
- neural network