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 <Emphasis Type="BoldItalic">Question</Emphasis> Motion artifacts negatively impact the performance of diagnostic AI models for MRI, but mitigation methods remain largely unexplored. <Emphasis Type="BoldItalic">Findings</Emphasis> Domain-specific augmentation during training can improve the robustness and performance of a model for quantifying lower limb alignment in the presence of severe artifacts. <Emphasis Type="BoldItalic">Clinical relevance</Emphasis> Excellent robustness and accuracy are crucial for deploying diagnostic AI models in clinical practice. Including domain knowledge in model training can benefit clinical adoption. Graphical Abstract

AI in motion: the impact of data augmentation strategies on mitigating MRI motion artifacts

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2025年5月17日

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Simon D. Westfechtel,
Kristoffer Kußmann,
Cederic Aßmann,
Marc S. Huppertz,
Robert M. Siepmann,
Teresa Lemainque,
Vera R. Winter,
Alexandra Barabasch,
Christiane K. Kuhl,
Daniel Truhn,
Sven Nebelung

摘要

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: DSC_baseline = 0.58 ± 0.22; DSC_default = 0.72 ± 0.22; DSC_MRI-specific = 0.79 ± 0.14 [p < 0.001]). These augmentations also maintained precise torsional angle measurements (e.g., severe artifacts, femoral torsion: MAD_baseline = 20.6 ± 23.5°; MAD_default = 7.0 ± 13.0°; MAD_MRI-specific = 5.7 ± 9.5° [p < 0.001]; ICC_baseline = −0.10 [p = 0.63; 95% CI: −0.61 to 0.47]; ICC_default = 0.38 [p = 0.08; −0.17 to 0.76]; ICC_MRI-specific = 0.86 [p < 0.001; 0.62 to 0.95]; r_baseline = 0.58 [p < 0.001; 0.44 to 0.69]; r_default = 0.68 [p < 0.001; 0.56 to 0.77]; r_MRI-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

Question Motion artifacts negatively impact the performance of diagnostic AI models for MRI, but mitigation methods remain largely unexplored.

Findings Domain-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 relevance Excellent robustness and accuracy are crucial for deploying diagnostic AI models in clinical practice. Including domain knowledge in model training can benefit clinical adoption.

Graphical Abstract