Deep Learning for Positron Emission Tomography (PET) Imaging and Signal Analysis
A special issue of Bioengineering (ISSN 2306-5354). This special issue belongs to the section "Biosignal Processing".
Deadline for manuscript submissions: 31 October 2026 | Viewed by 186
Editors
Interests: nuclear medicine imaging systems; novel PET imaging technology; flexible surface radiation detection devices; AI-powered nuclear medicine imaging
Interests: molecular imaging; bionanotechnology; radiopharmaceuticals; optical imaging; PET; radionuclide therapy
Special Issue Information
Dear Colleagues,
Positron emission tomography (PET) is an indispensable modality in molecular imaging, offering profound insights into physiological and pathological processes. However, traditional PET imaging frequently contends with inherent limitations, including spatial resolution constraints, high noise levels, and the clinical mandate to minimize radiotracer dosage. This Special Issue, "Deep Learning for Positron Emission Tomography (PET) Imaging and Signal Analysis", explores how artificial intelligence is fundamentally overcoming these historic barriers.
Deep learning algorithms are driving a paradigm shift in bioengineering, transforming how we acquire, reconstruct, and interpret PET data. This collection highlights cutting-edge research applying neural networks to enhance image quality, accelerate reconstruction times, and perform sophisticated, quantitative signal analysis.
Dr. Siwei Xie
Prof. Dr. Zhen Cheng
Guest Editors
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Keywords
- deep learning
- positron emission tomography (PET) * image reconstruction
- signal analysis
- molecular imaging
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