A cycle-consistent adversarial network for brain PET partial volume correction without prior anatomical information.
Amirhossein SanaatHossein ShooliAndrew Stephen BöhringerMaryam SadeghiIsaac ShiriYazdan SalimiNathalie GinovartValentina GaribottoHossein ArabiHabib ZaidiPublished in: European journal of nuclear medicine and molecular imaging (2023)
An end-to-end CycleGAN PVC method was developed and evaluated. Our model generates PVC images from the original non-PVC PET images without requiring additional anatomical information, such as MRI or CT. Our model eliminates the need for accurate registration or segmentation or PET scanner system response characterization. In addition, no assumptions regarding anatomical structure size, homogeneity, boundary, or background level are required.
Keyphrases
- positron emission tomography
- computed tomography
- deep learning
- convolutional neural network
- pet ct
- contrast enhanced
- resting state
- pet imaging
- image quality
- magnetic resonance imaging
- optical coherence tomography
- health information
- functional connectivity
- healthcare
- dual energy
- mass spectrometry
- diffusion weighted imaging
- brain injury
- blood brain barrier
- cerebral ischemia
- network analysis