ArticleEJNMMI physics2025
Effects of a deep learning-based image quality enhancement method on a digital-BGO PET/CT system for
Article in EJNMMI physics, 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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5 citing papers in PubMed.
- Straightforward PET phantom preparation usingAnnals of nuclear medicine · 2026Article
- 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
- Deep learning-enhanced digital-BGO versus TOF PET/CT: comparative assessment of detection, quantitation, and overall image quality.EJNMMI physics · 2025Article
- Innovations in clinical PET image reconstruction: advances in Bayesian penalized likelihood algorithm and deep learning.Annals of nuclear medicine · 2025Review
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11 authors.
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Abstract
backgroundThe digital-BGO PET/CT system, Omni Legend 32, incorporates modified block sequential regularized expectation maximization (BSREM) image reconstruction and a deep learning-based time-of-flight (TOF)-like image quality enhancement process called Precision DL (PDL). The present study aimed to define the fundamental characteristics of PDL using phantom and clinical images.
methodsA NEMA IEC body phantom was scanned using the Omni Legend 32 PET/CT system. All PET/CT images were acquired over 60 and 90 s per bed position, with a 384 × 384 matrix. Phantom images were reconstructed using OSEM + PSF and BSREM at β values of 100-1,000, combined with low (LPDL), medium (MPDL), and high (HPDL) PDL. We evaluated contrast recovery, background variability, and the contrast-to-noise ratio (CNR) of a 10 mm hot sphere. Thirty clinical whole-body
resultsContrast recovery and background variability decreased as the β value increased. This trend was consistent even when PDL processing was added to BSREM. Increased strength of the PDL models led to higher CNR. Noise levels decreased as a function of increasing β values in BSREM, resulting in a higher SNR, but lower SBR. Combining PDL with BSREM resulted in all β values producing better results in terms of noise, SBR, and SNR than OSEM + PSF. As the PDL increased (LPDL < MPDL < HPDL), noise levels, SBR, and SNR became higher. The β values of 400, 200, 300, and 300 for BSREM, LPDL, MPDL, and HPDL, respectively, resulted in noise equivalent to OSEM + PSF but significantly increased the SUV
conclusionThe combination of BSREM and PDL significantly enhanced the SUV
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