Neuroimage. 2010 May 1;50(4):1427-37. doi: 10.1016/j.neuroimage.2010.01.064. Epub 2010 Jan 28.
A robust method to estimate the intracranial volume across MRI field strengths (1.5T and 3T).
Weiner M, Aisen P, Weiner M, Aisen P, Petersen R, Jack CR Jr, Jagust W, Trojanowki J, Toga AW, Beckett L, Green RC, Gamst A, Saykin AJ, Morris J, Potter WZ, Green RC, Montine T, Petersen R, Aisen P, Gamst A, Thomas RG, Donohue M, Walter S, Jack CR Jr, Dale A, Bernstein M, Felmlee J, Fox N, Thompson P, Schuff N, Alexander G, DeCarli C, Jagust W, Bandy D, Koeppe RA, Foster N, Reiman EM, Chen K, Mathis C, Morris J, Cairns NJ, Taylor-Reinwald L, Trojanowki J, Shaw L, Lee VM, Korecka M, Toga AW, Crawford K, Neu S, Beckett L, Harvey D, Gamst A, Kornak J, Saykin AJ, Foroud TM, Potkin S, Shen L, Kachaturian Z, Frank R, Snyder PJ, Molchan S, Kaye J, Dolen S, Quinn J, Schneider L, Pawluczyk S, Spann BM, Brewer J, Vanderswag H, Heidebrink JL, Lord JL, Petersen R, Johnson K, Doody RS, Villanueva-Meyer J, Chowdhury M, Stern Y, Honig LS, Bell KL, Morris JC, Mintun MA, Schneider S, Marson D, Griffith R, Clark D, Grossman H, Tang C, Marzloff G, deToledo-Morrell L, Shah RC, Duara R, Varon D, Roberts P, Albert MS, Kozauer N, Zerrate M, Rusinek H, de Leon MJ, De Santi SM, Doraiswamy PM, Petrella JR, Aiello M, Arnold S, Karlawish JH, Wolk D, Smith CD, Given CA 2nd, Hardy P, Lopez OL, Oakley M, Simpson DM, Ismail MS, Brand C, Richard J, Mulnard RA, Thai G, Mc-Adams-Ortiz C, Diaz-Arrastia R, Martin-Cook K, DeVous M, Levey AI, Lah JJ, Cellar JS, Burns JM, Anderson HS, Laubinger MM, Apostolova L, Silverman DH, Lu PH, Graff-Radford NR, Parfitt F, Johnson H, Farlow M, Herring S, Hake AM, van Dyck CH, MacAvoy MG, Benincasa AL, Chertkow H, Bergman H, Hosein C, Black S, Stefanovic B, Caldwell C, Hsiung GY, Feldman H, Assaly M, Kertesz A, Rogers J, Trost D, Bernick C, Munic D, Wu CK, Johnson N, Mesulam M, Sadowsky C, Martinez W, Villena T, Turner RS, Johnson K, Reynolds B, Sperling RA, Rentz DM, Johnson KA, Rosen A, Tinklenberg J, Ashford W, Sabbagh M, Connor D, Jacobson S, Killiany R, Norbash A, Nair A, Obisesan TO, Jayam-Trouth A, Wang P, Lerner A, Hudson L, Ogrocki P, DeCarli C, Fletcher E, Carmichael O, Kittur S, Borrie M, Lee TY, Bartha R, Johnson S, Asthana S, Carlsson CM, Potkin SG, Preda A, Nguyen D, Tariot P, Fleisher A, Reeder S, Bates V, Capote H, Rainka M, Hendin BA, Scharre DW, Kataki M, Zimmerman EA, Celmins D, Brown AD, Pearlson G, Blank K, Anderson K, Saykin AJ, Santulli RB, Englert J, Williamson JD, Sink KM, Watkins F, Ott BR, Stopa E, Tremont G, Salloway S, Malloy P, Correia S, Rosen HJ, Miller BL, Mintzer J, Longmire CF, Spicer K.
Source
Division of Neuroscience and Mental Health, MRC Clinical Sciences Centre, Imperial College London, London, UK.
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.
2010 Elsevier Inc. All rights reserved.
- PMID:
- 20114082
- [PubMed - indexed for MEDLINE]
- PMCID:
- PMC2883144
Free PMC ArticleFig. 2
Measurement of the intracranial volume (ICV) for a subject from Group 1 scanned with the 1.5T (a) and 3T (b) scanner. For each group, the original image was reformatted to sagittal sections, which were then magnified by a factor of two. The boundary of the dura mater can not be shown clearly in the initial sagittal image (left). To improve the clarity of the boundary of the dura mater, the brightness of the image was increased (middle). The outer edge of the dura mater was traced by the rater manually. The caudal boundary of the cerebellum was considered the caudal boundary of the intracranial cavity (right).
Neuroimage. 2010 May 1;50(4):1427-1437.
Fig. 4
Coronal view of T1-weighted images of a subject from Group 1 scanned at 1.5T (a) and 3T (b). (a) The 1.5T image has a uniform image appearance, (b) The 3T image displays a central brightening artifact.
Neuroimage. 2010 May 1;50(4):1427-1437.
Fig. 6
Average positive and negative error by slice (n = 12) between manually segmented ICV and BET, SPM-tissue class, and RBM method results (Slice 19 is the left slice in the brain, Slice 129 is the top) in five subjects scanned at 1.5T and 3T.
Neuroimage. 2010 May 1;50(4):1427-1437.
Fig. 8
Histograms of the subtraction distribution for a single subject scanned with (a) 1.5T and (b) 3T.
Neuroimage. 2010 May 1;50(4):1427-1437.
Fig. 1
The data processing protocol is exemplified for one input image (the same protocol was used for each of the 10 images acquired from Group 1). Each volume was processed with three bias field correction algorithms (FAST, SPM5, N3) and three ICV measurement algorithms (two parameter sets for SPM, one for BET). See text for further details.
Neuroimage. 2010 May 1;50(4):1427-1437.
Fig. 3
A simplified diagram of the RBM method showing the data flow from a raw MRI to a completed brain mask. The steps involved in this method are tissue class segmentation with SPM5, and warping the sum of the three prior tissue probability maps using the inverted deformation from standard space to subject native space.
Neuroimage. 2010 May 1;50(4):1427-1437.
Fig. 5
Automated methods error on an MRI sagittal slice of a typical subject imaged at 1.5T (left) and 3T (right). Areas of negative error (estimate smaller than the manual reference) are shown in green, areas of positive error in red. Areas identified as intracranial by both the gold standard and the automated method segment are shown in white. Top panel: MR images with isolines of the manual delineation. Second panel: SPM-tissue class method (default setting) output. Third panel: BET output. Bottom panel: RBM output.
Neuroimage. 2010 May 1;50(4):1427-1437.
Fig. 7
Average CSF intensity of intraventricular and cisternal CSF in five subjects (10 MRIs) obtained at 1.5T and 3T with different bias correction methods. (a) FAST, (b) SPM, (c) N3. Horizontal lines: median; boxes: interquartile ranges; whiskers: range; circle: outlier. Blue: cisternal; red: intraventricular CSF intensity. For each scanner, average CSF intensity in ventricle is shown first, followed by the average cisternal CSF intensity.
Neuroimage. 2010 May 1;50(4):1427-1437.
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