title: Evaluation of automatic measurement of the intracranial volume based on quantitative MR imaging.
abstract: OBJECTIVE
  Brain size is commonly described in relation to ICV, whereby accurate assessment of this quantity is fundamental. Recently, an optimized MR sequence (QRAPMASTER) was developed for simultaneous quantification of T1, T2, and proton density. ICV can be measured automatically within minutes from QRAPMASTER outputs and a dedicated software, SyMRI. Automatic estimations of ICV were evaluated against the manual segmentation.
  
  
  METHODS
  In 19 healthy subjects, manual segmentation of ICV was performed by 2 neuroradiologists (Obs1, Obs2) by using QBrain software and conventional T2-weighted images. The automatic segmentation from the QRAPMASTER output was performed by using SyMRI. Manual corrections of the automatic segmentation were performed (corrected-automatic) by Obs1 and Obs2, who were blinded from each other. Finally, the repeatability of the automatic method was evaluated in 6 additional healthy subjects, each having 6 repeated QRAPMASTER scans. The time required to measure ICV was recorded.
  
  
  RESULTS
  No significant difference was found between reference and automatic (and corrected-automatic) ICV (P > .25). The mean difference between the reference and automatic measurement was -4.84 ± 19.57 mL (or 0.31 ± 1.35%). Mean differences between the reference and the corrected-automatic measurements were -0.47 ± 17.95 mL (-0.01 ± 1.24%) and -1.26 ± 17.68 mL (-0.06 ± 1.22%) for Obs1 and Obs2, respectively. The repeatability errors of the automatic and the corrected-automatic method were <1%. The automatic method required 1 minute 11 seconds (SD = 12 seconds) of processing. Adding manual corrections required another 1 minute 32 seconds (SD = 38 seconds).
  
  
  CONCLUSIONS
  Automatic and corrected-automatic quantification of ICV showed good agreement with the reference method. SyMRI software provided a fast and reproducible measure of ICV.
authors.affiliation: Department of Radiation Science, Umeå University, Umeå, Sweden. Khalid.Ambarki@vll.se
authors.forename: K
authors.initials: K
authors.lastname: Ambarki
authors.forename: T
authors.initials: T
authors.lastname: Lindqvist
authors.forename: A
authors.initials: A
authors.lastname: Wåhlin
authors.forename: E
authors.initials: E
authors.lastname: Petterson
authors.forename: M J B
authors.initials: MJ
authors.lastname: Warntjes
authors.forename: R
authors.initials: R
authors.lastname: Birgander
authors.forename: J
authors.initials: J
authors.lastname: Malm
authors.forename: A
authors.initials: A
authors.lastname: Eklund
mesh_terms: D000328:Adult; D000465:Algorithms; D001921:Brain / Q000033:anatomy & histology* / Q000502:physiology*; D005260:Female; D006801:Humans; D007089:Image Enhancement / Q000379:methods; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D021621:Imaging, Three-Dimensional / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000379:methods*; D008297:Male; D009929:Organ Size / Q000502:physiology; D010363:Pattern Recognition, Automated / Q000379:methods*; D015203:Reproducibility of Results; D012680:Sensitivity and Specificity