ArticleEuropean journal of nuclear medicine and molecular imaging2022
Deep learning-based time-of-flight (ToF) image enhancement of non-ToF PET scans.
Article in European journal of nuclear medicine and molecular imaging, 2022. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 26 papers.
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26 citing papers in PubMed.
- Artificial Intelligence Across the PET Reconstruction Pipeline: An Update.AJR. American journal of roentgenology · 2026Review
- Evaluation of the time‑of‑flight-enhanced deep learning image reconstruction method inPhysical and engineering sciences in medicine · 2026Article
- Evaluation of deep learning-based reconstruction models on non-TOF BGO PET/CT: impact of acquisition times and BSREM penalization factors on lesion detectability and SNR.EJNMMI physics · 2026Article
- Low-count whole-body PET denoising with deep learning in a multicenter, multi-tracer and externally validated study.European journal of nuclear medicine and molecular imaging · 2026Article
- Enhanced attenuation correction in FDG-PET/CT with respiratory mismatch correction.EJNMMI reports · 2025Article
- Deep learning-enhanced digital-BGO versus TOF PET/CT: comparative assessment of detection, quantitation, and overall image quality.EJNMMI physics · 2025Article
- Evaluation of a digital bismuth germanium oxide PET/CT system according to the Japanese brain tumor phantom test forPhysical and engineering sciences in medicine · 2025Article
- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
- Deep learning-based time-of-flight (ToF) enhancement of non-ToF PET scans for different radiotracers.European journal of nuclear medicine and molecular imaging · 2025Article
- Deep learning image enhancement algorithms in PET/CT imaging: a phantom and sarcoma patient radiomic evaluation.European journal of nuclear medicine and molecular imaging · 2025Article
- Ectopic, intra-thyroid parathyroid adenoma better visualized by deep learning enhanced choline PET/CT.QJM : monthly journal of the Association of Physicians · 2025Article
- A new design to improve time resolution in a time of flight brain PET using dual layer offset scintillator crystals.Scientific reports · 2025Article
- Effects of a deep learning-based image quality enhancement method on a digital-BGO PET/CT system forEJNMMI physics · 2025Article
- Translation of PET radiotracers for cancer imaging: recommendations from the National Cancer Imaging Translational Accelerator (NCITA) consensus meeting.BMC medicine · 2025Article
- Deep Convolutional Backbone Comparison for Automated PET Image Quality Assessment.IEEE transactions on radiation and plasma medical sciences · 2024Article
- Assessing the deep learning based image quality enhancements for the BGO based GE omni legend PET/CT.EJNMMI physics · 2024Article
- Influence of Covariates onDiagnostics (Basel, Switzerland) · 2024Article
- Sequential deep learning image enhancement models improve diagnostic confidence, lesion detectability, and image reconstruction time in PET.EJNMMI physics · 2024Article
- Fully automated computational measurement of noise in positron emission tomography.European radiology · 2024Article
- An extended bore length solid-state digital-BGO PET/CT system: design, preliminary experience, and performance characteristics.European journal of nuclear medicine and molecular imaging · 2024Article
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11 authors.
Funding
Abstract
purposeTo improve the quantitative accuracy and diagnostic confidence of PET images reconstructed without time-of-flight (ToF) using deep learning models trained for ToF image enhancement (DL-ToF).
methodsA total of 273 [
resultsThe non-ToF, DL-ToF low, medium, and high methods resulted in - 28 ± 18, - 28 ± 19, - 8 ± 22, and 1.7 ± 24% differences (mean; SD) in the SUV
conclusionDeep learning-based image enhancement models may provide converged ToF-equivalent image quality without ToF reconstruction. In clinical scoring DL-ToF-enhanced non-ToF images (medium and high) on average scored as high as, or higher than, ToF images. The model is generalisable and hence, could be applied to non-ToF images from BGO-based PET/CT scanners.
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