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High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
Published on: May 10, 2012
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Automatic brain segmentation using fractional signal modeling of a multiple flip angle, spoiled gradient-recalled
André Ahlgren1, Ronnie Wirestam, Freddy Ståhlberg
1Department of Medical Radiation Physics, Skåne University Hospital, Lund University, Barngatan 2B, 221 85, Lund, Sweden, Andre.Ahlgren@med.lu.se.
Magma (New York, N.Y.)
|March 19, 2014
Summary
A novel automatic brain segmentation method using magnetic resonance imaging (MRI) provides reliable T1 mapping and partial volume estimation. This technique offers robust segmentation with high accuracy, comparable to established methods, and requires no additional scans.
Area of Science:
- Neuroimaging
- Medical Physics
- Biomedical Engineering
Background:
- Accurate brain segmentation is crucial for quantitative analysis in neuroimaging.
- Existing methods may have limitations in reliability or require specialized protocols.
- Partial volume estimation enhances the precision of tissue quantification.
Purpose of the Study:
- To introduce and validate a new automatic method for brain segmentation in MRI.
- To assess the method's performance in partial volume estimation.
- To demonstrate its utility in T1 mapping.
Main Methods:
- Utilized spoiled gradient-recalled echo (SPGR) sequences with multiple flip angles for T1 mapping.
- Applied a multi-compartment model to derive parametric maps for partial volume estimation.
- Evaluated the method through simulations and in-vivo experiments on healthy volunteers.
Main Results:
- Simulations showed robust segmentation maps with over 95% Dice's coefficient and <3% bias.
- In-vivo experiments produced realistic segmentation comparable to established methods.
- Relative tissue volumes (CSF, grey matter, white matter) were consistent between the proposed and reference methods.
Conclusions:
- The developed method shows promise for accurate brain segmentation and partial volume estimation.
- Its straightforward implementation is advantageous for existing SPGR-based T1 mapping protocols.
- No additional MRI scans are required, facilitating integration into current workflows.
