Practical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot study

Our results show that a large percentage change in certain features can indicate a significant change in quality in clinically processed images.

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Publication Details

Journal
EJNMMI Reports
Published
2025-05-07
DOI
https://doi.org/10.1186/s41824-025-00243-x
Primary Topic
Radiomics and Machine Learning in Medical Imaging
Type
article
Field-Weighted Citation Impact
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article

Practical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot study

H. O’Driscoll, Jane C. Burns, Eamon Loughman
EJNMMI Reports
Radiomics and Machine Learning in Medical Imaging
article

Practical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot study

H. O’Driscoll, Jane C. Burns, Eamon Loughman
article en

Abstract

Our results show that a large percentage change in certain features can indicate a significant change in quality in clinically processed images.

EJNMMI ReportsVol. 9(1)
Mater Private Hospital (AU)
Reduced inequalities
Openalex Percentile: Top 85%
Radiomics and Machine Learning in Medical Imaging
DOIOpen Access PDFOpenAlex
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Practical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot study — H. O’Driscoll, Jane C. Burns, et al. · EJNMMI Reports (2025) | TGRS Research Map | TGRS