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Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI‐radiomics of the brain
PURPOSE: The purpose of the paper was to use a virtual phantom to identify a set of radiomic features from T1‐weighted and T2‐weighted magnetic resonance imaging (MRI) of the brain which is stable to variations in image acquisition parameters and to evaluate the effect of image preprocessing on radi...
Autores principales: | , , |
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Formato: | Online Artículo Texto |
Lenguaje: | English |
Publicado: |
John Wiley and Sons Inc.
2019
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Materias: | |
Acceso en línea: | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6899873/ https://www.ncbi.nlm.nih.gov/pubmed/31539450 http://dx.doi.org/10.1002/mp.13834 |
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author | Bologna, Marco Corino, Valentina Mainardi, Luca |
author_facet | Bologna, Marco Corino, Valentina Mainardi, Luca |
author_sort | Bologna, Marco |
collection | PubMed |
description | PURPOSE: The purpose of the paper was to use a virtual phantom to identify a set of radiomic features from T1‐weighted and T2‐weighted magnetic resonance imaging (MRI) of the brain which is stable to variations in image acquisition parameters and to evaluate the effect of image preprocessing on radiomic features stability. METHODS: Stability to different sources of variability (time of repetition and echo, voxel size, random noise and intensity non‐uniformity) was evaluated for both T1‐weighted and T2‐weighted MRI images. A set of 107 radiomic features, accounting for shape and size, first order statistics, and textural features was used. Feature stability was quantified using intraclass correlation coefficient (ICC). For each source of variability, stability was evaluated before and after preprocessing (Z‐score normalization, resampling, gaussian filtering and bias field correction). Features that have ICC > 0.75 in all the analysis of variability are selected as stable features. Last, the robust feature sets were tested on images acquired with random simulation parameters to assess their generalizability to unseen conditions. RESULTS: Preprocessing significantly increased the robustness of radiomic features to the different sources of variability. When preprocessing is applied, a set of 67 and 61 features resulted as stable for T1‐weighted and T2‐wieghted images respectively, over 80% of which were confirmed by the analysis on the images acquired with random simulation parameters. CONCLUSION: A set of MRI‐radiomic features, robust to changes in TR/TE/PS/ST, was identified. This set of features may be used in radiomic analyses based on T1‐weighted and T2‐weighted MRI images. |
format | Online Article Text |
id | pubmed-6899873 |
institution | National Center for Biotechnology Information |
language | English |
publishDate | 2019 |
publisher | John Wiley and Sons Inc. |
record_format | MEDLINE/PubMed |
spelling | pubmed-68998732019-12-19 Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI‐radiomics of the brain Bologna, Marco Corino, Valentina Mainardi, Luca Med Phys QUANTITATIVE IMAGING AND IMAGE PROCESSING PURPOSE: The purpose of the paper was to use a virtual phantom to identify a set of radiomic features from T1‐weighted and T2‐weighted magnetic resonance imaging (MRI) of the brain which is stable to variations in image acquisition parameters and to evaluate the effect of image preprocessing on radiomic features stability. METHODS: Stability to different sources of variability (time of repetition and echo, voxel size, random noise and intensity non‐uniformity) was evaluated for both T1‐weighted and T2‐weighted MRI images. A set of 107 radiomic features, accounting for shape and size, first order statistics, and textural features was used. Feature stability was quantified using intraclass correlation coefficient (ICC). For each source of variability, stability was evaluated before and after preprocessing (Z‐score normalization, resampling, gaussian filtering and bias field correction). Features that have ICC > 0.75 in all the analysis of variability are selected as stable features. Last, the robust feature sets were tested on images acquired with random simulation parameters to assess their generalizability to unseen conditions. RESULTS: Preprocessing significantly increased the robustness of radiomic features to the different sources of variability. When preprocessing is applied, a set of 67 and 61 features resulted as stable for T1‐weighted and T2‐wieghted images respectively, over 80% of which were confirmed by the analysis on the images acquired with random simulation parameters. CONCLUSION: A set of MRI‐radiomic features, robust to changes in TR/TE/PS/ST, was identified. This set of features may be used in radiomic analyses based on T1‐weighted and T2‐weighted MRI images. John Wiley and Sons Inc. 2019-10-08 2019-11 /pmc/articles/PMC6899873/ /pubmed/31539450 http://dx.doi.org/10.1002/mp.13834 Text en © 2019 The Authors. Medical Physics published by Wiley Periodicals, Inc. on behalf of American Association of Physicists in Medicine. This is an open access article under the terms of the http://creativecommons.org/licenses/by/4.0/ License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited. |
spellingShingle | QUANTITATIVE IMAGING AND IMAGE PROCESSING Bologna, Marco Corino, Valentina Mainardi, Luca Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI‐radiomics of the brain |
title | Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI‐radiomics of the brain |
title_full | Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI‐radiomics of the brain |
title_fullStr | Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI‐radiomics of the brain |
title_full_unstemmed | Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI‐radiomics of the brain |
title_short | Technical Note: Virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for MRI‐radiomics of the brain |
title_sort | technical note: virtual phantom analyses for preprocessing evaluation and detection of a robust feature set for mri‐radiomics of the brain |
topic | QUANTITATIVE IMAGING AND IMAGE PROCESSING |
url | https://www.ncbi.nlm.nih.gov/pmc/articles/PMC6899873/ https://www.ncbi.nlm.nih.gov/pubmed/31539450 http://dx.doi.org/10.1002/mp.13834 |
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