Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance
Abstract
Background: Synthetic positron emission tomography (PET) imaging, enabled by deep learning, represents a promising approach to minimize radiation exposure while preserving diagnostic accuracy. However, variability in methodologies, performance metrics, and clinical applications needs to be assessed. This systematic mapping review examines the current state of research in synthetic PET generation, analyzing their methodological frameworks and evaluating the clinical relevance.
Materials and methods: A systematic search in Scopus, PubMed, and Google Scholar (2019-2024) identified peer-reviewed studies on deep learning-based synthetic PET. Review articles, conference abstracts, and inaccessible full texts were excluded. Data extraction covered study characteristics, imaging modalities, architectures, and evaluation metrics. Due to study heterogeneity, the risk of bias was not formally assessed. Results were synthesized through descriptive and quantitative analysis.
Results: Of the initial 116 studies retrieved, 34 were included, 25 of them (73.5%) on brain/neuro using magnetic resonance imaging, computed tomography, or low-dose PET data to generate full-dose or tracer-specific PET. Common architectures included convolutional neural networks, generative adversarial networks, and U-Nets. Peak signal-to-noise ratio (PSNR) ranged 22.69-56.87 dB, structural similarity index measure (SSIM) 0.38-1.00 and mean absolute error (MAE) 1.37-72.00%. Whole-body applications were less frequent…
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