title: Automatic brain segmentation using fractional signal modeling of a multiple flip angle, spoiled gradient-recalled echo acquisition.
abstract: OBJECTIVE
  The aim of this study was to demonstrate a new automatic brain segmentation method in magnetic resonance imaging (MRI).
  
  
  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.
  
  
  CONCLUSIONS
  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.
authors.affiliation: Department of Medical Radiation Physics, Skåne University Hospital, Lund University, Barngatan 2B, 221 85, Lund, Sweden, Andre.Ahlgren@med.lu.se.
authors.forename: André
authors.initials: A
authors.lastname: Ahlgren
authors.forename: Ronnie
authors.initials: R
authors.lastname: Wirestam
authors.forename: Freddy
authors.initials: F
authors.lastname: Ståhlberg
authors.forename: Linda
authors.initials: L
authors.lastname: Knutsson
mesh_terms: D000328:Adult; D000465:Algorithms; D016477:Artifacts; D001921:Brain / Q000033:anatomy & histology*; D017352:Echo-Planar Imaging / Q000295:instrumentation / Q000379:methods*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods*; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods; D008297:Male; D008959:Models, Neurological; D015233:Models, Statistical; D010363:Pattern Recognition, Automated / Q000379:methods*; D019047:Phantoms, Imaging; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity; D012815:Signal Processing, Computer-Assisted