Author: Liu, Juan; Malekzadeh, Masoud; Mirian, Niloufar; Song, Tzu-An; Liu, Chi; Dutta, Joyita
Title: Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement. Cord-id: gszsv5cj Document date: 2021_10_1
ID: gszsv5cj
Snippet: High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. Artificial intelligence models for image denoising and deblurring are becoming increasingly popular for the postreconstruction enhancement of PET images. We present a detailed review of recent efforts for artificial intelligence-based PET image enhancement with a focus on network architectures, data types, loss functions, and evaluation metrics. We also highli
Document: High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. Artificial intelligence models for image denoising and deblurring are becoming increasingly popular for the postreconstruction enhancement of PET images. We present a detailed review of recent efforts for artificial intelligence-based PET image enhancement with a focus on network architectures, data types, loss functions, and evaluation metrics. We also highlight emerging areas in this field that are quickly gaining popularity, identify barriers to large-scale adoption of artificial intelligence models for PET image enhancement, and discuss future directions.
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