Deep learning for synthetic PET imaging: a systematic mapping review of techniques, metrics, and clinical relevance.
Authors
Affiliations (7)
Affiliations (7)
- Medical Physics Unit, Bambino Gesù Children's Hospital IRCCS, Rome, Italy.
- UOC Fisica per le Scienze della Vita, Dipartimento di Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy.
- eCampus University, Novedrate, Italy.
- UOC Fisica per le Scienze della Vita, Dipartimento di Diagnostica per Immagini e Radioterapia Oncologica, Fondazione Policlinico Universitario A. Gemelli IRCCS, Rome, Italy. [email protected].
- Functional and Interventional Neuroradiology Unit, Bambino Gesù Children's Hospital IRCCS, Rome, Italy.
- Advanced Cardiothoracic Imaging Unit, Bambino Gesù Children's Hospital IRCSS, Rome, Italy.
- Nuclear Medicine Unit/Imaging Department, Bambino Gesù Children's Hospital IRCCS, Rome, Italy.
Abstract
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. 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. 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 (9/34, 26.5%) but showed improvements in oncologic imaging, in particular for tumor detection and image quality. Despite promising advancements, challenges remain, including limited data availability, variability in tracer uptake, and the lack of standardized evaluation metrics. The absence of large/multicenter datasets limits the generalizability of findings. This review highlights promising advancements in synthetic PET imaging using deep learning, with several studies demonstrating the potential for high-quality image generation and substantially reduced radiation exposure. These developments are particularly significant in pediatric populations, where minimizing radiation dose is crucial to ensure patient safety and long-term health. Nonetheless, methodological variability and limited clinical validation continue to pose substantial challenges. Future research should prioritize the development of standardized evaluation protocols, the use of larger and more diverse datasets-including pediatric cohorts-and comprehensive real-world clinical validation to support the safe and effective translation of synthetic PET techniques into clinical practice. Deep learning-based synthetic PET imaging enhances diagnostics while reducing radiation, but requires methodological standardization and clinical validation for broader adoption. Deep learning can create full-dose PET images with less radiation exposure. Neurological applications dominate synthetic PET research, maintaining essential diagnostic detail. Challenges include limited datasets and variability in tracer uptake, necessitating further advancements.