A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T)
By Shiva Keihaninejad, Rolf A. Heckemann, Gianlorenzo Fagiolo, Mark R. Symms, Joseph V. Hajnal, Alexander Hammers. NeuroImage, 2010
1,815 citations recorded by the source corpus.
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.
Subfields: Computer Vision and Pattern Recognition, Radiology, Nuclear Medicine and Imaging
View Paper: https://www.semanticscholar.org/paper/07183f004f76e27ee6beb204d350a40796d96bf8
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In Computer Vision and Pattern Recognition
- Bruce Fischl
- Martin Reuter
- José Vicente Manjón
- Juan Eugenio Iglesias
- Pierrick Coupé
- A. V. D. van der Kouwe
- A. M. Dale
- D. Louis Collins
In Radiology, Nuclear Medicine and Imaging
- Bruce Fischl
- Martin Reuter
- A. V. D. van der Kouwe
- A. M. Dale
- Juan Eugenio Iglesias
- Elizabeth R. Sowell
- Jonathan R. Polimeni
- Rahul Desikan
Citations in the corpus
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- A Primer on Pattern-Based Approaches to fMRI: Principles, Pitfalls, and Perspectives. (2015, methodology): “A different approach is to spatially filter the data, for example, using three-dimensional (3D) wavelet pyramids (e.g., Hackmack et al., 2012; Figure 3D).”
- BEaST: Brain extraction based on nonlocal segmentation technique (2012, background): “As shown in (Keihaninejad et al., 2010), the scanner field strength has significant impact on intra-cranial cavity segmentations.”
- Microglia in Alzheimer’s disease (2018, background): “For example, a common variant in SPI1 is associated with reduced AD risk and reduced SPI1 expression (Huang et al., 2017); SPI1 encodes the PU.”