Pediatric evaluations for deep learning CT denoising.

Journal: Medical physics
PMID: 38127330

Abstract

BACKGROUND: Deep learning (DL) CT denoising models have the potential to improve image quality for lower radiation dose exams. These models are generally trained with large quantities of adult patient image data. However, CT, and increasingly DL denoising methods, are used in both adult and pediatric populations. Pediatric body habitus and size can differ significantly from adults and vary dramatically from newborns to adolescents. Ensuring that pediatric subgroups of different body sizes are not disadvantaged by DL methods requires evaluations capable of assessing performance in each subgroup.

Authors

  • Brandon J Nelson
    Division of Imaging, Diagnostics and Software Reliability, Office of Science and Engineering Labs, Center for Devices and Radiological Health, US Food and Drug Administration, Silver Spring, Maryland.
  • Prabhat Kc
    Center for Devices and Radiological Health, Food and Drug Administration, Silver Spring, Maryland.
  • Andreu Badal
    Center for Devices and Radiological Health, Office of Science and Engineering Labs, Division of Imaging, Diagnostics, and Software Reliability, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
  • Lu Jiang
    Institute of Materials Research and Engineering (IMRE), A*STAR, 2 Fusionopolis Way, #08-03 Innovis, Singapore 138634.
  • Shane C Masters
    Center for Drug Evaluation and Research, Office of Specialty Medicine, Division of Imaging and Radiation Medicine, U.S. Food and Drug Administration, Silver Spring, Maryland, USA.
  • Rongping Zeng
    Center for Devices and Radiological Health, US Food and Drug Administration (FDA), Silver Spring, Maryland, USA.

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