title: A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T).
abstract: As population-based studies may obtain images from scanners with different field strengths, a method to normalize regional brain volumes according to intracranial volume (ICV) independent of field strength is needed. We found systematic differences in ICV estimation, tested in a cohort of healthy subjects (n=5) that had been imaged using 1.5T and 3T scanners, and confirmed in two independent cohorts. This was related to systematic differences in the intensity of cerebrospinal fluid (CSF), with higher intensities for CSF located in the ventricles compared with CSF in the cisterns, at 3T versus 1.5T, which could not be removed with three different applied bias correction algorithms. We developed a method based on tissue probability maps in MNI (Montreal Neurological Institute) space and reverse normalization (reverse brain mask, RBM) and validated it against manual ICV measurements. We also compared it with alternative automated ICV estimation methods based on Statistical Parametric Mapping (SPM5) and Brain Extraction Tool (FSL). The proposed RBM method was equivalent to manual ICV normalization with a high intraclass correlation coefficient (ICC=0.99) and reliable across different field strengths. RBM achieved the best combination of precision and reliability in a group of healthy subjects, a group of patients with Alzheimer's disease (AD) and mild cognitive impairment (MCI) and can be used as a common normalization framework.
authors.affiliation: Division of Neuroscience and Mental Health, MRC Clinical Sciences Centre, Imperial College London, London, UK.
authors.forename: Shiva
authors.initials: S
authors.lastname: Keihaninejad
authors.forename: Rolf A
authors.initials: RA
authors.lastname: Heckemann
authors.forename: Gianlorenzo
authors.initials: G
authors.lastname: Fagiolo
authors.forename: Mark R
authors.initials: MR
authors.lastname: Symms
authors.forename: Joseph V
authors.initials: JV
authors.lastname: Hajnal
authors.forename: Alexander
authors.initials: A
authors.lastname: Hammers
mesh_terms: D000368:Aged; D000465:Algorithms; D000544:Alzheimer Disease / Q000473:pathology; D001331:Automation; D001921:Brain / Q000033:anatomy & histology* / Q000473:pathology; D002552:Cerebral Ventricles / Q000033:anatomy & histology / Q000473:pathology; D002555:Cerebrospinal Fluid; D003072:Cognition Disorders / Q000473:pathology; D015331:Cohort Studies; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008279:Magnetic Resonance Imaging / Q000295:instrumentation* / Q000379:methods*; D009929:Organ Size; D011336:Probability; D015203:Reproducibility of Results