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.
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.
Authors: Berengère Aubert-Broche; Mark Griffin; G Bruce Pike; Alan C Evans; D Louis Collins Journal: IEEE Trans Med Imaging Date: 2006-11 Impact factor: 10.048
Authors: Manus J Donahue; Hanzhang Lu; Craig K Jones; Richard A E Edden; James J Pekar; Peter C M van Zijl Journal: Magn Reson Med Date: 2006-12 Impact factor: 4.668
Authors: Stefan Klein; Marius Staring; Keelin Murphy; Max A Viergever; Josien P W Pluim Journal: IEEE Trans Med Imaging Date: 2009-11-17 Impact factor: 10.048