Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion Artifacts
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| Main Authors: | , , , , |
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| Format: | Preprint |
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2024
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| _version_ | 1866908409966624768 |
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| author | Marchetto, Elisa Eichhorn, Hannah Gallichan, Daniel Schnabel, Julia A. Ganz, Melanie |
| author_facet | Marchetto, Elisa Eichhorn, Hannah Gallichan, Daniel Schnabel, Julia A. Ganz, Melanie |
| contents | Purpose: Reliable image quality assessment is crucial for evaluating new motion correction methods for magnetic resonance imaging. In this work, we compare the performance of commonly used reference-based and reference-free image quality metrics on a unique dataset with real motion artifacts. We further analyze the image quality metrics' robustness to typical pre-processing techniques.
Methods: We compared five reference-based and five reference-free image quality metrics on data acquired with and without intentional motion (2D and 3D sequences). The metrics were recalculated seven times with varying pre-processing steps. The anonymized images were rated by radiologists and radiographers on a 1-5 Likert scale. Spearman correlation coefficients were computed to assess the relationship between image quality metrics and observer scores.
Results: All reference-based image quality metrics showed strong correlation with observer assessments, with minor performance variations across sequences. Among reference-free metrics, Average Edge Strength offers the most promising results, as it consistently displayed stronger correlations across all sequences compared to the other reference-free metrics. Overall, the strongest correlation was achieved with percentile normalization and restricting the metric values to the skull-stripped brain region. In contrast, correlations were weaker when not applying any brain mask and using min-max or no normalization.
Conclusion: Reference-based metrics reliably correlate with radiological evaluation across different sequences and datasets. Pre-processing steps, particularly normalization and brain masking, significantly influence the correlation values. Future research should focus on refining pre-processing techniques and exploring machine learning approaches for automated image quality evaluation. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2412_18389 |
| institution | arXiv |
| publishDate | 2024 |
| record_format | arxiv |
| spellingShingle | Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion Artifacts Marchetto, Elisa Eichhorn, Hannah Gallichan, Daniel Schnabel, Julia A. Ganz, Melanie Medical Physics Purpose: Reliable image quality assessment is crucial for evaluating new motion correction methods for magnetic resonance imaging. In this work, we compare the performance of commonly used reference-based and reference-free image quality metrics on a unique dataset with real motion artifacts. We further analyze the image quality metrics' robustness to typical pre-processing techniques. Methods: We compared five reference-based and five reference-free image quality metrics on data acquired with and without intentional motion (2D and 3D sequences). The metrics were recalculated seven times with varying pre-processing steps. The anonymized images were rated by radiologists and radiographers on a 1-5 Likert scale. Spearman correlation coefficients were computed to assess the relationship between image quality metrics and observer scores. Results: All reference-based image quality metrics showed strong correlation with observer assessments, with minor performance variations across sequences. Among reference-free metrics, Average Edge Strength offers the most promising results, as it consistently displayed stronger correlations across all sequences compared to the other reference-free metrics. Overall, the strongest correlation was achieved with percentile normalization and restricting the metric values to the skull-stripped brain region. In contrast, correlations were weaker when not applying any brain mask and using min-max or no normalization. Conclusion: Reference-based metrics reliably correlate with radiological evaluation across different sequences and datasets. Pre-processing steps, particularly normalization and brain masking, significantly influence the correlation values. Future research should focus on refining pre-processing techniques and exploring machine learning approaches for automated image quality evaluation. |
| title | Agreement of Image Quality Metrics with Radiological Evaluation in the Presence of Motion Artifacts |
| topic | Medical Physics |
| url | https://arxiv.org/abs/2412.18389 |