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Found 4 papers, 1 papers with code
Score-Based Generative Models for PET Image Reconstruction
Score-based generative models have demonstrated highly promising results for medical image reconstruction tasks in magnetic resonance imaging or computed tomography.
Artificial Intelligence in PET: an Industry Perspective
Artificial intelligence (AI) has significant potential to positively impact and advance medical imaging, including positron emission tomography (PET) imaging applications.
Improved MR to CT synthesis for PET/MR attenuation correction using Imitation Learning
Quantitative results show that the network generates pCTs that seem less accurate when evaluating the Mean Absolute Error on the pCT (69. 68HU) compared to a baseline CNN (66. 25HU), but lead to significant improvement in the PET reconstruction - 115a. u.
Deep Boosted Regression for MR to CT Synthesis
Attenuation correction is an essential requirement of positron emission tomography (PET) image reconstruction to allow for accurate quantification.
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