Estimating axial diffusivity in the NODDI model
To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the...
| Main Authors: | , , , , , , , , , , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2022-11-01
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| Series: | NeuroImage |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S1053811922006504 |
| _version_ | 1811215916744048640 |
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| author | Amy FD Howard Michiel Cottaar Mark Drakesmith Qiuyun Fan Susie Y. Huang Derek K. Jones Frederik J. Lange Jeroen Mollink Suryanarayana Umesh Rudrapatna Qiyuan Tian Karla L Miller Saad Jbabdi |
| author_facet | Amy FD Howard Michiel Cottaar Mark Drakesmith Qiuyun Fan Susie Y. Huang Derek K. Jones Frederik J. Lange Jeroen Mollink Suryanarayana Umesh Rudrapatna Qiyuan Tian Karla L Miller Saad Jbabdi |
| author_sort | Amy FD Howard |
| collection | DOAJ |
| description | To estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d∥=1.7μm2/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼2−2.5μm2/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data. |
| first_indexed | 2024-04-12T06:31:22Z |
| format | Article |
| id | doaj.art-60ebccdea499425c9fe378949fbeeb2e |
| institution | Directory Open Access Journal |
| issn | 1095-9572 |
| language | English |
| last_indexed | 2024-04-12T06:31:22Z |
| publishDate | 2022-11-01 |
| publisher | Elsevier |
| record_format | Article |
| series | NeuroImage |
| spelling | doaj.art-60ebccdea499425c9fe378949fbeeb2e2022-12-22T03:44:01ZengElsevierNeuroImage1095-95722022-11-01262119535Estimating axial diffusivity in the NODDI modelAmy FD Howard0Michiel Cottaar1Mark Drakesmith2Qiuyun Fan3Susie Y. Huang4Derek K. Jones5Frederik J. Lange6Jeroen Mollink7Suryanarayana Umesh Rudrapatna8Qiyuan Tian9Karla L Miller10Saad Jbabdi11Corresponding author.; FMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United KingdomFMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United KingdomCardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United KingdomAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Department of Biomedical Engineering, College of Precision Instruments and Optoelectronics Engineering, Tianjin University, Tianjin, China; Academy of Medical Engineering and Translational Medicine, Medical College, Tianjin University, Tianjin, ChinaAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United States; Harvard-MIT Division of Health Sciences and Technology, Massachusetts Institute of Technology, Cambridge, Massachusetts, United StatesCardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Neuroscience and Mental Health Research Institute, Cardiff University, Cardiff, United KingdomFMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United KingdomFMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United KingdomCardiff University Brain Research Imaging Centre, Cardiff University, Cardiff, United Kingdom; Philips Innovation Campus, Bangalore, IndiaAthinoula A. Martinos Center for Biomedical Imaging, Department of Radiology, Massachusetts General Hospital, Charlestown, Massachusetts, United States; Harvard Medical School, Boston, Massachusetts, United StatesFMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United KingdomFMRIB Centre, Wellcome Centre for Integrative Neuroimaging, Nuffield Department of Clinical Neurosciences, University of Oxford, Oxford, United KingdomTo estimate microstructure-related parameters from diffusion MRI data, biophysical models make strong, simplifying assumptions about the underlying tissue. The extent to which many of these assumptions are valid remains an open research question. This study was inspired by the disparity between the estimated intra-axonal axial diffusivity from literature and that typically assumed by the Neurite Orientation Dispersion and Density Imaging (NODDI) model (d∥=1.7μm2/ms). We first demonstrate how changing the assumed axial diffusivity results in considerably different NODDI parameter estimates. Second, we illustrate the ability to estimate axial diffusivity as a free parameter of the model using high b-value data and an adapted NODDI framework. Using both simulated and in vivo data we investigate the impact of fitting to either real-valued or magnitude data, with Gaussian and Rician noise characteristics respectively, and what happens if we get the noise assumptions wrong in this high b-value and thus low SNR regime. Our results from real-valued human data estimate intra-axonal axial diffusivities of ∼2−2.5μm2/ms, in line with current literature. Crucially, our results demonstrate the importance of accounting for both a rectified noise floor and/or a signal offset to avoid biased parameter estimates when dealing with low SNR data.http://www.sciencedirect.com/science/article/pii/S1053811922006504Diffusion MRINODDIAxial diffusivityOrientation dispersionHigh b-valueWhite matter |
| spellingShingle | Amy FD Howard Michiel Cottaar Mark Drakesmith Qiuyun Fan Susie Y. Huang Derek K. Jones Frederik J. Lange Jeroen Mollink Suryanarayana Umesh Rudrapatna Qiyuan Tian Karla L Miller Saad Jbabdi Estimating axial diffusivity in the NODDI model NeuroImage Diffusion MRI NODDI Axial diffusivity Orientation dispersion High b-value White matter |
| title | Estimating axial diffusivity in the NODDI model |
| title_full | Estimating axial diffusivity in the NODDI model |
| title_fullStr | Estimating axial diffusivity in the NODDI model |
| title_full_unstemmed | Estimating axial diffusivity in the NODDI model |
| title_short | Estimating axial diffusivity in the NODDI model |
| title_sort | estimating axial diffusivity in the noddi model |
| topic | Diffusion MRI NODDI Axial diffusivity Orientation dispersion High b-value White matter |
| url | http://www.sciencedirect.com/science/article/pii/S1053811922006504 |
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