CT-based thermometry with virtual monoenergetic images by dual-energy of fat, muscle and bone using FBP, iterative and deep learning-based reconstruction.
Andreas HeinrichSebastian SchenklDavid BuckreusFelix V GüttlerUlf K-M TeichgräberPublished in: European radiology (2021)
• Virtual monoenergetic images (VMI) enable a higher temperature sensitivity for fat (8%), muscle (24%) and bone (211%) compared to conventional polychromatic 120-kVp images. • With VMI, there are parameters, e.g. monoenergy and reconstruction kernel, to modulate the temperature sensitivity. In contrast, there are no parameters to influence the temperature sensitivity for conventional polychromatic 120-kVp images. • The application of adaptive statistical iterative reconstruction-Volume (ASIR-V) and deep learning-based image reconstruction (DLIR) has no effect on CT-based thermometry, opening up the potential of drastic dose reductions in clinical applications.
Keyphrases
- deep learning
- dual energy
- image quality
- computed tomography
- convolutional neural network
- artificial intelligence
- contrast enhanced
- adipose tissue
- machine learning
- skeletal muscle
- bone mineral density
- magnetic resonance imaging
- magnetic resonance
- optical coherence tomography
- fatty acid
- positron emission tomography
- bone loss
- bone regeneration
- pet ct