ArticleJournal of applied clinical medical physics2024
Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.
Article in Journal of applied clinical medical physics, 2024. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Cited by 8 papers.
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8 citing papers in PubMed.
- A BMC-Net model for the recognition and segmentation of mandibular canal bifurcation.Oral radiology · 2026Article
- A multimodal explainable ai framework to assist in the differential diagnosis of head and neck reactive follicular hyperplasia and follicular lymphoma: an international multicentre study.Virchows Archiv : an international journal of pathology · 2026Article
- Scanner-integrated reconstruction versus post-processing deep learning for low-countEJNMMI physics · 2026Article
- Artificial Intelligence for the Diagnosis and Management of Cancers: Potentials and Challenges.MedComm · 2025Review
- Clinical validation of a deep learning model for low-count PET image enhancement.European journal of nuclear medicine and molecular imaging · 2025Article
- UR-cycleGAN: Denoising full-body low-dose PET images using cycle-consistent Generative Adversarial Networks.Journal of applied clinical medical physics · 2025Article
- Review
- Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.Journal of applied clinical medical physics · 2024Article
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9 authors.
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Abstract
purposeThis study aims to evaluate the clinical performance of a deep learning (DL)-enhanced two-fold accelerated PET imaging method in patients with lymphoma.
methodsA total of 123 cases devoid of lymphoma underwent whole-body 18F-FDG-PET/CT scans to facilitate the development of an advanced SAU2Net model, which combines the advantages of U2Net and attention mechanism. This model integrated inputs from simulated 1/2-dose (0.07 mCi/kg) PET acquisition across multiple slices to generate an estimated standard dose (0.14 mCi/kg) PET scan. Additional 39 cases with confirmed lymphoma pathology were utilized to evaluate the model's clinical performance. Assessment criteria encompassed peak-signal-to-noise ratio (PSNR), structural similarity index (SSIM), a 5-point Likert scale rated by two experienced physicians, SUV features, image noise in the liver, and contrast-to-noise ratio (CNR). Diagnostic outcomes, including lesion numbers and Deauville score, were also compared.
resultsImages enhanced by the proposed DL method exhibited superior image quality (P < 0.001) in comparison to low-dose acquisition. Moreover, they illustrated equivalent image quality in terms of subjective image analysis and lesion maximum standardized uptake value (SUVmax) as compared to the standard acquisition method. A linear regression model with y = 1.017x + 0.110 (
conclusionThe proposed DL method could facilitate a 50% reduction in PET imaging duration for lymphoma patients, while concurrently preserving image quality and diagnostic accuracy.
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