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Clinical evaluation of deep learning-enhanced lymphoma pet imaging with accelerated acquisition.
Li, Xu; Pan, Boyang; Chen, Congxia; Yan, Dongyue; Pan, Zhenglin; Feng, Tao; Liu, Hui; Gong, Nan-Jie; Liu, Fugeng.
Afiliación
  • Li X; Department of Nuclear Medicine, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
  • Pan B; RadioDynamic Healthcare, Shanghai, People's Republic of China.
  • Chen C; Department of Nuclear Medicine, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
  • Yan D; Department of Nuclear Medicine, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
  • Pan Z; RadioDynamic Healthcare, Shanghai, People's Republic of China.
  • Feng T; Laboratory for Intelligent Medical Imaging, Tsinghua Cross-strait Research Institute, Beijing, People's Republic of China.
  • Liu H; Department of Engineering Physics, Tsinghua University, Beijing, People's Republic of China.
  • Gong NJ; Laboratory for Intelligent Medical Imaging, Tsinghua Cross-strait Research Institute, Beijing, People's Republic of China.
  • Liu F; Department of Nuclear Medicine, Beijing Hospital, National Center of Gerontology; Institute of Geriatric Medicine, Chinese Academy of Medical Sciences, Beijing, People's Republic of China.
J Appl Clin Med Phys ; : e14390, 2024 May 29.
Article en En | MEDLINE | ID: mdl-38812107
ABSTRACT

PURPOSE:

This study aims to evaluate the clinical performance of a deep learning (DL)-enhanced two-fold accelerated PET imaging method in patients with lymphoma.

METHODS:

A 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.

RESULTS:

Images 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 ( R 2 = 1.00 ${R^2} = \;1.00$ ) can be established between the enhanced scans and the standard acquisition for lesion SUVmax. With enhancement, increased signal-to-noise ratio (SNR), CNR, and reduced image noise were observed, surpassing those of the standard acquisition. DL-enhanced PET images got diagnostic results essentially equavalent to standard PET images according to two experienced readers.

CONCLUSION:

The 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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Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Appl Clin Med Phys Asunto de la revista: BIOFISICA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos
Texto completo
Añadir a Mi BVS
Imprimir
XML
PubMed Links
Buscar en Google

Texto completo: 1 Colección: 01-internacional Base de datos: MEDLINE Idioma: En Revista: J Appl Clin Med Phys Asunto de la revista: BIOFISICA Año: 2024 Tipo del documento: Article Pais de publicación: Estados Unidos