Challenges in ensuring the generalizability of image quantitation methods for MRI. Keenan, K. E., Delfino, J. G., Jordanova, K. V., Poorman, M. E., Chirra, P., Chaudhari, A. S., Baessler, B., Winfield, J., Viswanath, S. E., & deSouza , N. M. Medical Physics, 49(4):2820–2835, 2022. _eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.15195![Challenges in ensuring the generalizability of image quantitation methods for MRI [link]](https://bibbase.org/img/filetypes/link.svg "link")
Paper doi abstract bibtex Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.
@article{keenan_challenges_2022,
title = {Challenges in ensuring the generalizability of image quantitation methods for {MRI}},
volume = {49},
copyright = {© 2021 American Association of Physicists in Medicine. This article has been contributed to by US Government employees and their work is in the public domain in the USA.},
issn = {2473-4209},
url = {https://onlinelibrary.wiley.com/doi/abs/10.1002/mp.15195},
doi = {10.1002/mp.15195},
abstract = {Image quantitation methods including quantitative MRI, multiparametric MRI, and radiomics offer great promise for clinical use. However, many of these methods have limited clinical adoption, in part due to issues of generalizability, that is, the ability to translate methods and models across institutions. Researchers can assess generalizability through measurement of repeatability and reproducibility, thus quantifying different aspects of measurement variance. In this article, we review the challenges to ensuring repeatability and reproducibility of image quantitation methods as well as present strategies to minimize their variance to enable wider clinical implementation. We present possible solutions for achieving clinically acceptable performance of image quantitation methods and briefly discuss the impact of minimizing variance and achieving generalizability towards clinical implementation and adoption.},
language = {en},
number = {4},
urldate = {2023-10-23},
journal = {Medical Physics},
author = {Keenan, Kathryn E. and Delfino, Jana G. and Jordanova, Kalina V. and Poorman, Megan E. and Chirra, Prathyush and Chaudhari, Akshay S. and Baessler, Bettina and Winfield, Jessica and Viswanath, Satish E. and deSouza, Nandita M.},
year = {2022},
note = {\_eprint: https://onlinelibrary.wiley.com/doi/pdf/10.1002/mp.15195},
keywords = {magnetic resonance imaging, multiparametric MRI, quantitative MRI, radiomics},
pages = {2820--2835},
}
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