AI in motion: the impact of data augmentation strategies on mitigating MRI motion artifacts.
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| Title: | AI in motion: the impact of data augmentation strategies on mitigating MRI motion artifacts. |
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| Authors: | Westfechtel, Simon D.1 (AUTHOR) swestfechtel@ukaachen.de, Kußmann, Kristoffer1 (AUTHOR), Aßmann, Cederic1 (AUTHOR), Huppertz, Marc S.1 (AUTHOR), Siepmann, Robert M.1 (AUTHOR), Lemainque, Teresa1 (AUTHOR), Winter, Vera R.1 (AUTHOR), Barabasch, Alexandra1 (AUTHOR), Kuhl, Christiane K.1 (AUTHOR), Truhn, Daniel1 (AUTHOR), Nebelung, Sven1 (AUTHOR) |
| Source: | European Radiology. Nov2025, Vol. 35 Issue 11, p6865-6878. 14p. |
| Subjects: | Magnetic resonance imaging, Data augmentation, Artificial intelligence |
| Abstract: | Objectives: Artifacts in clinical MRI can compromise the performance of AI models. This study evaluates how different data augmentation strategies affect an AI model's segmentation performance under variable artifact severity. Materials and methods: We used an AI model based on the nnU-Net architecture to automatically quantify lower limb alignment using axial T2-weighted MR images. Three versions of the AI model were trained with different augmentation strategies: (1) no augmentation ("baseline"), (2) standard nnU-net augmentations ("default"), and (3) "default" plus augmentations that emulate MR artifacts ("MRI-specific"). Model performance was tested on 600 MR image stacks (right and left; hip, knee, and ankle) from 20 healthy participants (mean age, 23 ± 3 years, 17 men), each imaged five times under standardized motion to induce artifacts. Two radiologists graded each stack's artifact severity as none, mild, moderate, and severe, and manually measured torsional angles. Segmentation quality was assessed using the Dice similarity coefficient (DSC), while torsional angles were compared between manual and automatic measurements using mean absolute deviation (MAD), intraclass correlation coefficient (ICC), and Pearson's correlation coefficient (r). Statistical analysis included parametric tests and a Linear Mixed-Effects Model. Results: MRI-specific augmentation resulted in slightly (yet not significantly) better performance than the default strategy. Segmentation quality decreased with increasing artifact severity, which was partially mitigated by default and MRI-specific augmentations (e.g., severe artifacts, proximal femur: DSCbaseline = 0.58 ± 0.22; DSCdefault = 0.72 ± 0.22; DSCMRI-specific = 0.79 ± 0.14 [p < 0.001]). These augmentations also maintained precise torsional angle measurements (e.g., severe artifacts, femoral torsion: MADbaseline = 20.6 ± 23.5°; MADdefault = 7.0 ± 13.0°; MADMRI-specific = 5.7 ± 9.5° [p < 0.001]; ICCbaseline = −0.10 [p = 0.63; 95% CI: −0.61 to 0.47]; ICCdefault = 0.38 [p = 0.08; −0.17 to 0.76]; ICCMRI-specific = 0.86 [p < 0.001; 0.62 to 0.95]; rbaseline = 0.58 [p < 0.001; 0.44 to 0.69]; rdefault = 0.68 [p < 0.001; 0.56 to 0.77]; rMRI-specific = 0.86 [p < 0.001; 0.81 to 0.9]). Conclusion: Motion artifacts negatively impact AI models, but general-purpose augmentations enhance robustness effectively. MRI-specific augmentations offer minimal additional benefit. Key Points: QuestionMotion artifacts negatively impact the performance of diagnostic AI models for MRI, but mitigation methods remain largely unexplored. FindingsDomain-specific augmentation during training can improve the robustness and performance of a model for quantifying lower limb alignment in the presence of severe artifacts. Clinical relevanceExcellent robustness and accuracy are crucial for deploying diagnostic AI models in clinical practice. Including domain knowledge in model training can benefit clinical adoption. [ABSTRACT FROM AUTHOR] |
| Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract. (Copyright applies to all Abstracts.) | |
| Database: | Engineering Source |
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| Items | – Name: Title Label: Title Group: Ti Data: AI in motion: the impact of data augmentation strategies on mitigating MRI motion artifacts. – Name: Author Label: Authors Group: Au Data: <searchLink fieldCode="AR" term="%22Westfechtel%2C+Simon+D%2E%22">Westfechtel, Simon D.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<i> swestfechtel@ukaachen.de</i><br /><searchLink fieldCode="AR" term="%22Kußmann%2C+Kristoffer%22">Kußmann, Kristoffer</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Aßmann%2C+Cederic%22">Aßmann, Cederic</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Huppertz%2C+Marc+S%2E%22">Huppertz, Marc S.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Siepmann%2C+Robert+M%2E%22">Siepmann, Robert M.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Lemainque%2C+Teresa%22">Lemainque, Teresa</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Winter%2C+Vera+R%2E%22">Winter, Vera R.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Barabasch%2C+Alexandra%22">Barabasch, Alexandra</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Kuhl%2C+Christiane+K%2E%22">Kuhl, Christiane K.</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Truhn%2C+Daniel%22">Truhn, Daniel</searchLink><relatesTo>1</relatesTo> (AUTHOR)<br /><searchLink fieldCode="AR" term="%22Nebelung%2C+Sven%22">Nebelung, Sven</searchLink><relatesTo>1</relatesTo> (AUTHOR) – Name: TitleSource Label: Source Group: Src Data: <searchLink fieldCode="JN" term="%22European+Radiology%22">European Radiology</searchLink>. Nov2025, Vol. 35 Issue 11, p6865-6878. 14p. – Name: Subject Label: Subjects Group: Su Data: <searchLink fieldCode="DE" term="%22Magnetic+resonance+imaging%22">Magnetic resonance imaging</searchLink><br /><searchLink fieldCode="DE" term="%22Data+augmentation%22">Data augmentation</searchLink><br /><searchLink fieldCode="DE" term="%22Artificial+intelligence%22">Artificial intelligence</searchLink> – Name: Abstract Label: Abstract Group: Ab Data: Objectives: Artifacts in clinical MRI can compromise the performance of AI models. This study evaluates how different data augmentation strategies affect an AI model's segmentation performance under variable artifact severity. Materials and methods: We used an AI model based on the nnU-Net architecture to automatically quantify lower limb alignment using axial T2-weighted MR images. Three versions of the AI model were trained with different augmentation strategies: (1) no augmentation ("baseline"), (2) standard nnU-net augmentations ("default"), and (3) "default" plus augmentations that emulate MR artifacts ("MRI-specific"). Model performance was tested on 600 MR image stacks (right and left; hip, knee, and ankle) from 20 healthy participants (mean age, 23 ± 3 years, 17 men), each imaged five times under standardized motion to induce artifacts. Two radiologists graded each stack's artifact severity as none, mild, moderate, and severe, and manually measured torsional angles. Segmentation quality was assessed using the Dice similarity coefficient (DSC), while torsional angles were compared between manual and automatic measurements using mean absolute deviation (MAD), intraclass correlation coefficient (ICC), and Pearson's correlation coefficient (r). Statistical analysis included parametric tests and a Linear Mixed-Effects Model. Results: MRI-specific augmentation resulted in slightly (yet not significantly) better performance than the default strategy. Segmentation quality decreased with increasing artifact severity, which was partially mitigated by default and MRI-specific augmentations (e.g., severe artifacts, proximal femur: DSCbaseline = 0.58 ± 0.22; DSCdefault = 0.72 ± 0.22; DSCMRI-specific = 0.79 ± 0.14 [p < 0.001]). These augmentations also maintained precise torsional angle measurements (e.g., severe artifacts, femoral torsion: MADbaseline = 20.6 ± 23.5°; MADdefault = 7.0 ± 13.0°; MADMRI-specific = 5.7 ± 9.5° [p < 0.001]; ICCbaseline = −0.10 [p = 0.63; 95% CI: −0.61 to 0.47]; ICCdefault = 0.38 [p = 0.08; −0.17 to 0.76]; ICCMRI-specific = 0.86 [p < 0.001; 0.62 to 0.95]; rbaseline = 0.58 [p < 0.001; 0.44 to 0.69]; rdefault = 0.68 [p < 0.001; 0.56 to 0.77]; rMRI-specific = 0.86 [p < 0.001; 0.81 to 0.9]). Conclusion: Motion artifacts negatively impact AI models, but general-purpose augmentations enhance robustness effectively. MRI-specific augmentations offer minimal additional benefit. Key Points: QuestionMotion artifacts negatively impact the performance of diagnostic AI models for MRI, but mitigation methods remain largely unexplored. FindingsDomain-specific augmentation during training can improve the robustness and performance of a model for quantifying lower limb alignment in the presence of severe artifacts. Clinical relevanceExcellent robustness and accuracy are crucial for deploying diagnostic AI models in clinical practice. Including domain knowledge in model training can benefit clinical adoption. [ABSTRACT FROM AUTHOR] – Name: AbstractSuppliedCopyright Label: Group: Ab Data: <i>Copyright of European Radiology is the property of Springer Nature and its content may not be copied or emailed to multiple sites without the copyright holder's express written permission. Additionally, content may not be used with any artificial intelligence tools or machine learning technologies. However, users may print, download, or email articles for individual use. This abstract may be abridged. No warranty is given about the accuracy of the copy. Users should refer to the original published version of the material for the full abstract.</i> (Copyright applies to all Abstracts.) |
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| RecordInfo | BibRecord: BibEntity: Identifiers: – Type: doi Value: 10.1007/s00330-025-11670-6 Languages: – Code: eng Text: English PhysicalDescription: Pagination: PageCount: 14 StartPage: 6865 Subjects: – SubjectFull: Magnetic resonance imaging Type: general – SubjectFull: Data augmentation Type: general – SubjectFull: Artificial intelligence Type: general Titles: – TitleFull: AI in motion: the impact of data augmentation strategies on mitigating MRI motion artifacts. Type: main BibRelationships: HasContributorRelationships: – PersonEntity: Name: NameFull: Westfechtel, Simon D. – PersonEntity: Name: NameFull: Kußmann, Kristoffer – PersonEntity: Name: NameFull: Aßmann, Cederic – PersonEntity: Name: NameFull: Huppertz, Marc S. – PersonEntity: Name: NameFull: Siepmann, Robert M. – PersonEntity: Name: NameFull: Lemainque, Teresa – PersonEntity: Name: NameFull: Winter, Vera R. – PersonEntity: Name: NameFull: Barabasch, Alexandra – PersonEntity: Name: NameFull: Kuhl, Christiane K. – PersonEntity: Name: NameFull: Truhn, Daniel – PersonEntity: Name: NameFull: Nebelung, Sven IsPartOfRelationships: – BibEntity: Dates: – D: 01 M: 11 Text: Nov2025 Type: published Y: 2025 Identifiers: – Type: issn-print Value: 09387994 Numbering: – Type: volume Value: 35 – Type: issue Value: 11 Titles: – TitleFull: European Radiology Type: main |
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