ArticleEuropean journal of nuclear medicine and molecular imaging2025
Deep learning image enhancement algorithms in PET/CT imaging: a phantom and sarcoma patient radiomic evaluation.
Article in European journal of nuclear medicine and molecular imaging, 2025. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 5 papers.
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Who cites it
5 citing papers in PubMed.
- Machine Learning for Radiomics in Oncology: Challenges, Limitations, and Future Directions.Sensors (Basel, Switzerland) · 2026Article
- Impact of partial volume correction on radiomics reproducibility in theranostic SPECT/CT imaging.Medical physics · 2026Article
- The Current Landscape of Artificial Intelligence in Positron Emission Tomography (PET) Imaging Across the Cancer Continuum.Journal of clinical medicine · 2026Review
- BDNet: A Real-Time Biomedical Image Denoising Network with Gradient Information Enhancement Loss.Biosensors · 2026Article
- A CT-based deep learning model to predict local recurrence-free survival in primary retroperitoneal sarcoma.Frontiers in medicine · 2025Article
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Authors and funding
7 authors.
Funding
Abstract
purposePET/CT imaging data contains a wealth of quantitative information that can provide valuable contributions to characterising tumours. A growing body of work focuses on the use of deep-learning (DL) techniques for denoising PET data. These models are clinically evaluated prior to use, however, quantitative image assessment provides potential for further evaluation. This work uses radiomic features to compare two manufacturer deep-learning (DL) image enhancement algorithms, one of which has been commercialised, against 'gold-standard' image reconstruction techniques in phantom data and a sarcoma patient data set (N=20).
methodsAll studies in the retrospective sarcoma clinical [
resultsComparing DL-enhanced images to the 'gold-standard', 4.0% and 9.7% radiomic features measured significantly different (p
conclusionDL-enhanced images were found to be similar to images generated using the 'gold-standard' target image reconstruction method with more than 80% of radiomic features not significantly different in all comparisons across unseen phantom and sarcoma patient data. This result offers insight into the performance of the DL algorithms, and demonstrate potential applications for DL algorithms in harmonisation for radiomics and for radiomic features in quantitative evaluation of DL algorithms.
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