Noise-Based Image Harmonization Significantly Increases Repeatability and Reproducibility of Radiomics Features in PET Images: A Phantom Study
| Autor: | Harald Keller, Tina Shek, Brandon Driscoll, Yiwen Xu, Brian Nghiem, Sadek Nehmeh, Milan Grkovski, Charles Ross Schmidtlein, Mikalai Budzevich, Yoganand Balagurunathan, John J. Sunderland, Reinhard R. Beichel, Carlos Uribe, Ting-Yim Lee, Fiona Li, David A. Jaffray, Ivan Yeung |
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| Jazyk: | angličtina |
| Rok vydání: | 2022 |
| Předmět: | |
| Zdroj: | Tomography, Vol 8, Iss 2, Pp 1113-1128 (2022) |
| Druh dokumentu: | article |
| ISSN: | 2379-139X 2379-1381 |
| DOI: | 10.3390/tomography8020091 |
| Popis: | For multicenter clinical studies, characterizing the robustness of image-derived radiomics features is essential. Features calculated on PET images have been shown to be very sensitive to image noise. The purpose of this work was to investigate the efficacy of a relatively simple harmonization strategy on feature robustness and agreement. A purpose-built texture pattern phantom was scanned on 10 different PET scanners in 7 institutions with various different image acquisition and reconstruction protocols. An image harmonization technique based on equalizing a contrast-to-noise ratio was employed to generate a “harmonized” alongside a “standard” dataset for a reproducibility study. In addition, a repeatability study was performed with images from a single PET scanner of variable image noise, varying the binning time of the reconstruction. Feature agreement was measured using the intraclass correlation coefficient (ICC). In the repeatability study, 81/93 features had a lower ICC on the images with the highest image noise as compared to the images with the lowest image noise. Using the harmonized dataset significantly improved the feature agreement for five of the six investigated feature classes over the standard dataset. For three feature classes, high feature agreement corresponded with higher sensitivity to the different patterns, suggesting a way to select suitable features for predictive models. |
| Databáze: | Directory of Open Access Journals |
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