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Electrical Engineering and Systems Science > Image and Video Processing

arXiv:2107.13595 (eess)
[Submitted on 28 Jul 2021]

Title:Artificial Intelligence-Based Image Enhancement in PET Imaging: Noise Reduction and Resolution Enhancement

Authors:Juan Liu, Masoud Malekzadeh, Niloufar Mirian, Tzu-An Song, Chi Liu, Joyita Dutta
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Abstract:High noise and low spatial resolution are two key confounding factors that limit the qualitative and quantitative accuracy of PET images. AI models for image denoising and deblurring are becoming increasingly popular for post-reconstruction enhancement of PET images. We present here a detailed review of recent efforts for AI-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 AI models for PET image enhancement, and discuss future directions.
Subjects: Image and Video Processing (eess.IV)
Cite as: arXiv:2107.13595 [eess.IV]
  (or arXiv:2107.13595v1 [eess.IV] for this version)
  https://doi.org/10.48550/arXiv.2107.13595
Related DOI: https://doi.org/10.1016/j.cpet.2021.06.005

Submission history

From: Joyita Dutta [view email]
[v1] Wed, 28 Jul 2021 18:53:44 UTC (6,209 KB)
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