Related Experiment Video
Updated: May 2, 2026
High-resolution Functional Magnetic Resonance Imaging Methods for Human Midbrain
Published on: May 10, 2012
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.
Object:
The aim of this study was to demonstrate a new automatic brain segmentation method in magnetic resonance imaging (MRI).
Materials And Methods:
The signal of a spoiled gradient-recalled echo (SPGR) sequence acquired with multiple flip angles was used to map T1, and a subsequent fit of a multi-compartment model yielded parametric maps of partial volume estimates of the different compartments. The performance of the proposed method was assessed through simulations as well as in-vivo experiments in five healthy volunteers.
Results:
Simulations indicated that the proposed method was capable of producing robust segmentation maps with good reliability. Mean bias was below 3% for all tissue types, and the corresponding similarity index (Dice's coefficient) was over 95% (SNR = 100). In-vivo experiments yielded realistic segmentation maps, with comparable quality to results obtained with an established segmentation method. Relative whole-brain cerebrospinal fluid, grey matter, and white matter volumes were (mean ± SE) respectively 6.8 ± 0.5, 47.3 ± 1.1, and 45.9 ± 1.3% for the proposed method, and 7.5 ± 0.6, 46.2 ± 1.2, and 46.3 ± 0.9% for the reference method.
Conclusion:
The proposed approach is promising for brain segmentation and partial volume estimation. The straightforward implementation of the method is attractive, and protocols that already rely on SPGR-based T1 mapping may employ this method without additional scans.
