Sensitivity of Quantitative Susceptibility Mapping in Clinical Brain Research
Fuente:
arXiv
Guardado en:
| Autores principales: | , , , , , , , , , , , , |
|---|---|
| Formato: | Preprint |
| Publicado: |
2025
|
| Materias: | |
| Acceso en línea: | |
| Etiquetas: |
Agregar Etiqueta
Sin Etiquetas, Sea el primero en etiquetar este registro!
|
| _version_ | 1866929690134970368 |
|---|---|
| author | Salman, Fahad Ramesh, Abhisri Jochmann, Thomas Prayer, Mirjam Adegbemigun, Ademola Reeves, Jack A. Wilding, Gregory E. Cho, Junghun Jakimovski, Dejan Bergsland, Niels Dwyer, Michael G. Zivadinov, Robert Schweser, Ferdinand |
| author_facet | Salman, Fahad Ramesh, Abhisri Jochmann, Thomas Prayer, Mirjam Adegbemigun, Ademola Reeves, Jack A. Wilding, Gregory E. Cho, Junghun Jakimovski, Dejan Bergsland, Niels Dwyer, Michael G. Zivadinov, Robert Schweser, Ferdinand |
| contents | Background: Quantitative susceptibility mapping (QSM) of the brain is an advanced MRI technique for assessing tissue characteristics based on magnetic susceptibility, which varies with the composition of the tissue, such as iron, calcium, and myelin levels. QSM consists of multiple processing steps, with various choices for each step. Despite its increasing application in detecting and monitoring neurodegenerative diseases, the impact of algorithmic choices in QSM's workflow on clinical outcomes has not been thoroughly quantified.
Objective: This study aimed to evaluate how choices in background field removal (BFR), dipole inversion algorithms, and anatomical referencing impact the sensitivity and reproducibility error of QSM in detecting group-level and longitudinal changes in deep gray matter susceptibility in a clinical setting.
Methods: We compared 378 different QSM pipelines using a 10-year follow-up dataset of healthy adults. We analyzed the sensitivity of pipelines to detect known aging-related susceptibility changes in the DGM over time.
Results: We found high variability in the sensitivity of QSM pipelines to detect susceptibility changes. The study highlighted that while most pipelines could detect changes reliably, the choice of BFR algorithm and the referencing strategy substantially influenced the outcome reproducibility error and sensitivity. Notably, pipelines using RESHARP with AMP-PE, HEIDI or LSQR inversion showed the highest overall sensitivity.
Conclusions: The findings underscore the critical influence of algorithmic choices in QSM processing on the accuracy and reliability of detecting physiological changes in the brain. This has profound implications for clinical research and trials where QSM is used as a biomarker for disease progression, highlighting that careful consideration should be given to pipeline configuration to optimize clinical outcomes. |
| format | Preprint |
| id |
arxiv_https___arxiv_org_abs_2501_17158 |
| institution | arXiv |
| publishDate | 2025 |
| record_format | arxiv |
| spellingShingle | Sensitivity of Quantitative Susceptibility Mapping in Clinical Brain Research Salman, Fahad Ramesh, Abhisri Jochmann, Thomas Prayer, Mirjam Adegbemigun, Ademola Reeves, Jack A. Wilding, Gregory E. Cho, Junghun Jakimovski, Dejan Bergsland, Niels Dwyer, Michael G. Zivadinov, Robert Schweser, Ferdinand Quantitative Methods Image and Video Processing Computational Physics Medical Physics Background: Quantitative susceptibility mapping (QSM) of the brain is an advanced MRI technique for assessing tissue characteristics based on magnetic susceptibility, which varies with the composition of the tissue, such as iron, calcium, and myelin levels. QSM consists of multiple processing steps, with various choices for each step. Despite its increasing application in detecting and monitoring neurodegenerative diseases, the impact of algorithmic choices in QSM's workflow on clinical outcomes has not been thoroughly quantified. Objective: This study aimed to evaluate how choices in background field removal (BFR), dipole inversion algorithms, and anatomical referencing impact the sensitivity and reproducibility error of QSM in detecting group-level and longitudinal changes in deep gray matter susceptibility in a clinical setting. Methods: We compared 378 different QSM pipelines using a 10-year follow-up dataset of healthy adults. We analyzed the sensitivity of pipelines to detect known aging-related susceptibility changes in the DGM over time. Results: We found high variability in the sensitivity of QSM pipelines to detect susceptibility changes. The study highlighted that while most pipelines could detect changes reliably, the choice of BFR algorithm and the referencing strategy substantially influenced the outcome reproducibility error and sensitivity. Notably, pipelines using RESHARP with AMP-PE, HEIDI or LSQR inversion showed the highest overall sensitivity. Conclusions: The findings underscore the critical influence of algorithmic choices in QSM processing on the accuracy and reliability of detecting physiological changes in the brain. This has profound implications for clinical research and trials where QSM is used as a biomarker for disease progression, highlighting that careful consideration should be given to pipeline configuration to optimize clinical outcomes. |
| title | Sensitivity of Quantitative Susceptibility Mapping in Clinical Brain Research |
| topic | Quantitative Methods Image and Video Processing Computational Physics Medical Physics |
| url | https://arxiv.org/abs/2501.17158 |