Practical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot study
Abstract Purpose Radiomics features have been utilised as group metrics of image quality in many areas of diagnostic radiology. In this pilot study, the relationship between technical metrics used in image quality assurance and visual grading scores provided by a radiologist were evaluated. Image da...
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Springer
2025-05-01
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| Series: | EJNMMI Reports |
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| Online Access: | https://doi.org/10.1186/s41824-025-00243-x |
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| author | Jane Burns Hannah O’Driscoll Eamon Loughman |
| author_facet | Jane Burns Hannah O’Driscoll Eamon Loughman |
| author_sort | Jane Burns |
| collection | DOAJ |
| description | Abstract Purpose Radiomics features have been utilised as group metrics of image quality in many areas of diagnostic radiology. In this pilot study, the relationship between technical metrics used in image quality assurance and visual grading scores provided by a radiologist were evaluated. Image dataset harmonisation allowed comparison between the two and allowed trends to be extracted. We propose a reproducible technique to identify the metrics. Methods A retrospective chart review of 30 [18F] FDG-PET/CT performed in a nuclear medicine referral centre was performed. Image datasets were reprocessed to correspond to a bed duration of 180, 120, 60, 30 s per bed position and were analysed according to a pre-set bank of semi-quantitative features by a radiology resident. The extraction of radiomic features in PET images was performed using SLICER-RADIOMICS Module version 5.2.2. To facilitate the comparison of radiomic features and radiologist scoring data, normalisation was performed on both data sets. Fréchet distance analysis, Mean Square Error and Mean Absolute Error display the level of agreement between features and radiologist following the rescale of the data. Results Of the 120 reprocessed image datasets, 115 were included in the study. We focused on overall image quality score rather than individual radiomic metrics as this identified the most robust trend. A significant difference in the 30 s image dataset with respect to each group individually and combined for the radiologist overall score was observed. Conclusion Our results show that a large percentage change in certain features can indicate a significant change in quality in clinically processed images. |
| format | Article |
| id | doaj-art-5c71a34e994742e0aff8d2ef363a1841 |
| institution | OA Journals |
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| language | English |
| publishDate | 2025-05-01 |
| publisher | Springer |
| record_format | Article |
| series | EJNMMI Reports |
| spelling | doaj-art-5c71a34e994742e0aff8d2ef363a18412025-08-20T01:49:35ZengSpringerEJNMMI Reports3005-074X2025-05-019111110.1186/s41824-025-00243-xPractical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot studyJane Burns0Hannah O’Driscoll1Eamon Loughman2Department of Radiology, Mater Misericordiae University HospitalDepartment of Medical Physics, Mater Private NetworkDepartment of Medical Physics, Mater Private NetworkAbstract Purpose Radiomics features have been utilised as group metrics of image quality in many areas of diagnostic radiology. In this pilot study, the relationship between technical metrics used in image quality assurance and visual grading scores provided by a radiologist were evaluated. Image dataset harmonisation allowed comparison between the two and allowed trends to be extracted. We propose a reproducible technique to identify the metrics. Methods A retrospective chart review of 30 [18F] FDG-PET/CT performed in a nuclear medicine referral centre was performed. Image datasets were reprocessed to correspond to a bed duration of 180, 120, 60, 30 s per bed position and were analysed according to a pre-set bank of semi-quantitative features by a radiology resident. The extraction of radiomic features in PET images was performed using SLICER-RADIOMICS Module version 5.2.2. To facilitate the comparison of radiomic features and radiologist scoring data, normalisation was performed on both data sets. Fréchet distance analysis, Mean Square Error and Mean Absolute Error display the level of agreement between features and radiologist following the rescale of the data. Results Of the 120 reprocessed image datasets, 115 were included in the study. We focused on overall image quality score rather than individual radiomic metrics as this identified the most robust trend. A significant difference in the 30 s image dataset with respect to each group individually and combined for the radiologist overall score was observed. Conclusion Our results show that a large percentage change in certain features can indicate a significant change in quality in clinically processed images.https://doi.org/10.1186/s41824-025-00243-xRadiomicsImage quality[18F] FDG-PET/CTQuantitative imaging |
| spellingShingle | Jane Burns Hannah O’Driscoll Eamon Loughman Practical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot study EJNMMI Reports Radiomics Image quality [18F] FDG-PET/CT Quantitative imaging |
| title | Practical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot study |
| title_full | Practical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot study |
| title_fullStr | Practical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot study |
| title_full_unstemmed | Practical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot study |
| title_short | Practical use of radiomic features as a metric for image quality discrimination in [18F] FDG-PET: a pilot study |
| title_sort | practical use of radiomic features as a metric for image quality discrimination in 18f fdg pet a pilot study |
| topic | Radiomics Image quality [18F] FDG-PET/CT Quantitative imaging |
| url | https://doi.org/10.1186/s41824-025-00243-x |
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