Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity
Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstruc...
| Main Authors: | , , , , , , , , , , , , , |
|---|---|
| Format: | Article |
| Language: | English |
| Published: |
Elsevier
2022-11-01
|
| Series: | NeuroImage |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922006449 |
| _version_ | 1828287323896283136 |
|---|---|
| author | Siawoosh Mohammadi Tobias Streubel Leonie Klock Luke J. Edwards Antoine Lutti Kerrin J. Pine Sandra Weber Patrick Scheibe Gabriel Ziegler Jürgen Gallinat Simone Kühn Martina F. Callaghan Nikolaus Weiskopf Karsten Tabelow |
| author_facet | Siawoosh Mohammadi Tobias Streubel Leonie Klock Luke J. Edwards Antoine Lutti Kerrin J. Pine Sandra Weber Patrick Scheibe Gabriel Ziegler Jürgen Gallinat Simone Kühn Martina F. Callaghan Nikolaus Weiskopf Karsten Tabelow |
| author_sort | Siawoosh Mohammadi |
| collection | DOAJ |
| description | Multi-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1, and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the method's sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox. |
| first_indexed | 2024-04-13T09:45:54Z |
| format | Article |
| id | doaj.art-a413d32925064f17897fdfa222e184e3 |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-04-13T09:45:54Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-a413d32925064f17897fdfa222e184e32022-12-22T02:51:45ZengElsevierNeuroImage1095-95722022-11-01262119529Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivitySiawoosh Mohammadi0Tobias Streubel1Leonie Klock2Luke J. Edwards3Antoine Lutti4Kerrin J. Pine5Sandra Weber6Patrick Scheibe7Gabriel Ziegler8Jürgen Gallinat9Simone Kühn10Martina F. Callaghan11Nikolaus Weiskopf12Karsten Tabelow13Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Corresponding author at: Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany.Department of Systems Neuroscience, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Department of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, GermanyDepartment of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, GermanyDepartment of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, GermanyLaboratory for Research in Neuroimaging, Department of Clinical Neurosciences, Lausanne University Hospital and University of Lausanne, Lausanne, SwitzerlandDepartment of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, GermanyDepartment of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, GermanyDepartment of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, GermanyInstitute of Cognitive Neurology and Dementia Research, Otto-von-Guericke-University Magdeburg, Germany; Germany Weierstrass Institute for Applied Analysis and Stochastics, German Center for Neurodegenerative Diseases, Magdeburg, Berlin, GermanyDepartment of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, GermanyDepartment of Psychiatry and Psychotherapy, University Medical Center Hamburg-Eppendorf, Hamburg, Germany; Max Planck Institute for Human Development, Lise Meitner Group for Environmental Neuroscience, Berlin, GermanyWellcome Centre for Human Neuroimaging, UCL Queen Square Institute of Neurology, UCL, London, UKDepartment of Neurophysics, Max Planck Institute for Human Cognitive and Brain Sciences, Leipzig, Germany; Felix Bloch Institute for Solid State Physics, Leipzig University, Linnéstraße 5, Leipzig 04103, GermanyWeierstrass Institute for Applied Analysis and Stochastics, Mohrenstrasse 39, Berlin 10117, GermanyMulti-Parameter Mapping (MPM) is a comprehensive quantitative neuroimaging protocol that enables estimation of four physical parameters (longitudinal and effective transverse relaxation rates R1 and R2*, proton density PD, and magnetization transfer saturation MTsat) that are sensitive to microstructural tissue properties such as iron and myelin content. Their capability to reveal microstructural brain differences, however, is tightly bound to controlling random noise and artefacts (e.g. caused by head motion) in the signal. Here, we introduced a method to estimate the local error of PD, R1, and MTsat maps that captures both noise and artefacts on a routine basis without requiring additional data. To investigate the method's sensitivity to random noise, we calculated the model-based signal-to-noise ratio (mSNR) and showed in measurements and simulations that it correlated linearly with an experimental raw-image-based SNR map. We found that the mSNR varied with MPM protocols, magnetic field strength (3T vs. 7T) and MPM parameters: it halved from PD to R1 and decreased from PD to MTsat by a factor of 3-4. Exploring the artefact-sensitivity of the error maps, we generated robust MPM parameters using two successive acquisitions of each contrast and the acquisition-specific errors to down-weight erroneous regions. The resulting robust MPM parameters showed reduced variability at the group level as compared to their single-repeat or averaged counterparts. The error and mSNR maps may better inform power-calculations by accounting for local data quality variations across measurements. Code to compute the mSNR maps and robustly combined MPM maps is available in the open-source hMRI toolbox.http://www.sciencedirect.com/science/article/pii/S1053811922006449Multi-parameter mappingQuantitative MRIError propagationSignal-to-noise ratioRobust estimate |
| spellingShingle | Siawoosh Mohammadi Tobias Streubel Leonie Klock Luke J. Edwards Antoine Lutti Kerrin J. Pine Sandra Weber Patrick Scheibe Gabriel Ziegler Jürgen Gallinat Simone Kühn Martina F. Callaghan Nikolaus Weiskopf Karsten Tabelow Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity NeuroImage Multi-parameter mapping Quantitative MRI Error propagation Signal-to-noise ratio Robust estimate |
| title | Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity |
| title_full | Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity |
| title_fullStr | Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity |
| title_full_unstemmed | Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity |
| title_short | Error quantification in multi-parameter mapping facilitates robust estimation and enhanced group level sensitivity |
| title_sort | error quantification in multi parameter mapping facilitates robust estimation and enhanced group level sensitivity |
| topic | Multi-parameter mapping Quantitative MRI Error propagation Signal-to-noise ratio Robust estimate |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922006449 |
| work_keys_str_mv | AT siawooshmohammadi errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity AT tobiasstreubel errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity AT leonieklock errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity AT lukejedwards errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity AT antoinelutti errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity AT kerrinjpine errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity AT sandraweber errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity AT patrickscheibe errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity AT gabrielziegler errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity AT jurgengallinat errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity AT simonekuhn errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity AT martinafcallaghan errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity AT nikolausweiskopf errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity AT karstentabelow errorquantificationinmultiparametermappingfacilitatesrobustestimationandenhancedgrouplevelsensitivity |