Interpretable Motion Artificat Detection in structural Brain MRI
Automated quality assessment of structural brain MRI is an important prerequisite for reliable neuroimaging analysis, but yet remains challenging due to motion artifacts and poor generalization across acquisition sites. Existing approaches based on image quality metrics (IQMs) or deep learning either requires extensive preprocessing, which incurs high computational cost, or poor generalization to unseen data. In this work, we propose a lightweight and interpretable framework for detecting motion related artifacts in T1 weighted brain MRI by extending the Discriminative Histogram of Gradient Magnitude (DHoGM) to a three dimensional space. The proposed method integrates complementary slice-...
| Authors | Naveetha Nithianandam, Prabhjot Kaur, Anil Kumar Sao |
|---|---|
| Primary category | eess.IV |
| Published | 5 March 2026 |
| Last updated at source | 5 March 2026 |
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