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Congress: ECR25
Poster Number: C-18416
Type: Poster: EPOS Radiologist (scientific)
DOI: 10.26044/ecr2025/C-18416
Authorblock: O. N. Samuel, C. Saidlear, E. L. Twomey, *. Chi Temple St Group, *. Chi Crumlin Group, J. Cooke, M. Kelly; Dublin/IE
Disclosures:
Osahenre Noelle Samuel: Nothing to disclose
Colm Saidlear: Nothing to disclose
Eilish L. Twomey: Nothing to disclose
* Chi Temple St Group: Nothing to disclose
* Chi Crumlin Group: Nothing to disclose
Jennie Cooke: Nothing to disclose
Michael Kelly: Nothing to disclose
Keywords: Artificial Intelligence, MR physics, Paediatric, MR, Diagnostic procedure, Efficacy studies, Physics, Education and training, Image verification, Quality assurance
References

[1] Lebel RM. Performance characterization of a novel deep learning-based MR image reconstruction pipeline. ArXiv. 2020;abs/2008.06559.

[2] Harris H, Lawson S, Peters RD. The clinical benefits of AIR™ Recon DL for MR image reconstruction. GE Healthcare. 2020.

[3] Allen TJ, Henze Bancroft LC, Unal O, Estkowski LD, Cashen TA, Korosec F, Strigel RM, Kelcz F, Fowler AM, Gegios A, et al. Evaluation of a Deep Learning Reconstruction for High-Quality T2-Weighted Breast Magnetic Resonance Imaging. Tomography. 2023; 9(5):1949-1964. https://doi.org/10.3390/tomography9050152.

[4] Zochowski KC, Tan ET, Argentieri EC, et al. Improvement of peripheral nerve visualization using a deep learning-based MR reconstruction algorithm. Magn Reson Imaging. 2022;85:186-192. doi:10.1016/j.mri.2021.10.038

[5] Koch KM, Sherafati M, Arpinar VE, et al. Analysis and Evaluation of a Deep Learning Reconstruction Approach with Denoising for Orthopedic MRI. Radiol Artif Intell. 2021;3(6):e200278. Published 2021 Aug 11. doi:10.1148/ryai.2021200278

[6] Kim SH, Choi YH, Lee JS, et al. Deep learning reconstruction in pediatric brain MRI: comparison of image quality with conventional T2-weighted MRI. Neuroradiology. 2023;65(1):207-214. doi:10.1007/s00234-022-03053-1

[7] U.S. Food and Drug Administration (FDA). Artificial intelligence/machine learning (ai/ml) based software as a medical device (SAMD) action plan. Online, January 2021.

[8]Youssef A, Pencina M, Thakur A, Zhu T, Clifton D, Shah NH. External validation of AI models in health should be replaced with recurring local validation. Nat Med. 2023;29(11):2686-2687. doi:10.1038/s41591-023-02540-z

[9] Park SH, Choi J, Byeon JS. Key Principles of Clinical Validation, Device Approval, and Insurance Coverage Decisions of Artificial Intelligence. Korean J Radiol. 2021;22(3):442-453. doi:10.3348/kjr.2021.0048

[10] Smith SM, Jenkinson M, Woolrich MW, et al. Advances in functional and structural MR image analysis and implementation as FSL. Neuroimage. 2004;23 Suppl 1:S208-S219. doi:10.1016/j.neuroimage.2004.07.051

[11] Lorry Rui. Calculate spatial frequency response (sfr) for a slanted edge. Fremont, USA, https://github.com/Lorrytoolcenter/quickMTF/tree/master; 2022 [ Accessed: 2024-08-12].

[12] Bender R, Grouven U. Ordinal logistic regression in medical research. J R Coll Physicians Lond. 1997;31(5):546-551.

[13] Fritz M, Berger P. Will anybody buy? Logistic regression. In: Business Analytics: The Science of Predictive Modeling and Data Visualization. 1st ed. Academic Press; 2015:185-210. doi:10.1016/B978-0-12-800635-1.00011-2.

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