SENSE factors for reliable cortical thickness measurement.
ABSTRACT The purposes of this study were to examine the effect of sensitivity encoding (SENSE) factors on cortical thickness measurements and to determine which SENSE factor to use to reliably measure cortical thickness in 3.0 T and 1.5 T T1-weighted MRI images. The 3D T1-TFE images were acquired from 11 healthy volunteers with 6 different SENSE acceleration factors from 1.0 (without SENSE acceleration) to 4.0 on a 1.5 T scanner, and 9 different SENSE factors from 1.0 to 6.0, plus a second-day 1.0 acquisition on a 3.0 T scanner. Cortical thickness was calculated for the entire cortical surface that was further subdivided into 33 regions. Repeated measures multivariate analysis of variance revealed that the main effect of SENSE factors (F=12.485, df=7, p=0.006) was a significant underestimation of cortical thickness at SENSE 5.0 (p=0.022) and 6.0 (p=0.011) at 3.0 T and at SENSE 4.0 (p<0.000) at 1.5 T. Repeated measures ANOVA showed that thickness measurements at the insula, superior temporal sulcus, the medial part of the superior frontal lobe, and cingulate cortex are highly affected by SENSE factors. SENSE factors affect thickness estimation more significantly at 1.5 T and thus 1.5 T imaging provides less reliable estimates using SENSE techniques. Faster imaging can be done without too much loss of reliability using a high SENSE factor, such as 3.0, at 3.0 T with acquisition time being inversely proportional to the SENSE factor.
- [Show abstract] [Hide abstract]
ABSTRACT: To explore the morphological aspects of the functional reorganization of the blind's visual cortex, we analyzed the regional cortical thickness and cortical surface area in the congenitally blind subjects (CB) compared to the late-onset blind (LB) and sighted controls (SC). Cortical thickness was calculated from high-resolution T1-weighted magnetic resonance images of 21 young CB (blind from birth, mean age=27.1 yr), 12 LB, and 35 young SC. Analysis of covariance of cortical layer thickness with global thickness, age, and gender as covariates was done node-by-node on the entire cortical surface. Further analysis of mean thickness and surface area was performed for 33 automatically parceled cortical regions. Voxel-based morphometry was also conducted to compare results with cortical thickness and surface area. We found increased cortical thickness in the regions involved in vision and eye movement, such as the pericalcarine sulcus, cingulate cortex, and right frontal eye field, but cortical thinning in the left somatosensory cortex and right auditory cortex of CB compared to SC. CB had significantly reduced surface extent in the primary and associated visual areas, which explains volumetric atrophies in the visual cortex of CB despite increased cortical thickness. Conversely, LB tended to have cortical thinning in the primary visual cortex with a slight or no significant reduction in the surface extent. These morphological alterations in CB may indicate cortical reorganization at the visual cortex in connection with other sensory cortices.NeuroImage 05/2009; 47(1):98-106. · 6.25 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: To determine whether a spin-echo-based sequence, which are inherently insensitive to magnetic field inhomogeneity, can be used for brain cortical thickness measurement studies. By using a double inversion recovery (DIR) spin-echo-based sequence, cortical thickness estimates were performed from data acquired from seven healthy volunteers. The cortical thickness was also calculated from data acquired using an MPRAGE sequence and the Bland-Altman analysis was performed for comparison of the two methods. The average signal and contrast to noise ratios (SNR, CNR) of the two methods were also calculated. The bias over the entire brain between DIR and MPRAGE was 0.87 ± 0.08 mm. The bias calculated in the major regional lobes were temporal: 0.76 ± 0.09 mm, frontal: 0.89 ± 0.07 mm, parietal: 0.92 ± 0.10 mm, occipital: 0.75 ± 0.12 mm, and cingulate: 0.79 ± 0.10 mm. This thickness difference was due mainly to the boundary difference in the MPRAGE and DIR at the grey matter/cerebral spinal fluid (GM/CSF) regions. The mean SNR and CNR was CNR(MPRAGE) = 47.8 ± 8.4 and CNR(DIR) = 19.2 ± 2.9, SNR(MPRAGE) = 76.8 ± 10.5 and SNR(DIR) = 21.1 ± 2.8. The study suggests that cortical thickness measurements can be performed using a DIR spin-echo sequence, which is inherently immune to main field inhomogeneity. Larger thickness measurements were consistently observed in DIR compared with MPRAGE.Journal of Magnetic Resonance Imaging 05/2011; 33(5):1218-23. · 2.57 Impact Factor
- [Show abstract] [Hide abstract]
ABSTRACT: Human precentral and postcentral cortical areas interact to generate sensorimotor functions. Recent imaging work suggests that pre- and postcentral cortical thicknesses of an individual vary over time-scales of years and decades due to aging, disease, and other factors. In contrast, there is little understanding of how thicknesses of these areas vary in an individual over time-scales of minutes and weeks. This study used longitudinal magnetic resonance imaging (MRI) and computational morphometry approaches in 5 healthy subjects to assess how mean thicknesses, and intra- and interhemispheric relationships in mean thicknesses, of these areas vary in an individual subject over minutes and weeks. Within each individual, absolute differences in thicknesses over these times were small and similar in the precentral (mean = 0.02-0.04 mm) and postcentral (mean = 0.03-0.05 mm) areas. Each individual also had a consistent intrahemispheric disparity and interhemispheric asymmetrical or symmetrical relationship in thicknesses of these areas over these times. The results provide new understanding of within-individual cortical thickness variability in these areas and raise the possibility that longitudinal thickness profiling can provide a baseline definition of short time-scale thickness variability that can be used to detect acute and subacute changes in pre- and postcentral thicknesses at an individual subject level.Cerebral Cortex 10/2009; 20(7):1513-22. · 8.31 Impact Factor
SENSE factors for reliable cortical thickness measurement
H-J. Park1 and E. Kim1
1Diagnostic Radiology, Yonsei University, Seodaemun, Seoul, Korea, Republic of
With the advent of parallel imaging such as sensitivity encoding (SENSE), fast MR imaging is feasible. This may be beneficial for pediatric
neuroimaging for reducing acquisition time. However, parallel imaging has a drawback of decrease in signal-to-noise ratio, which may affect the
results of visual analysis, volumetry, and cortical thickness measurement. Cortical thickness as a promising index for brain researches can be
measured by using a precise computation without manual drawing due to the innate 3-D folding patterns of the brain (1, 2). According to Han et al (3),
cortical thickness measurement can be affected by the scanner manufacturer, scanner field strength, upgrade, pulse sequence, and parameters used in
the post processing. The reliability for the effect of SENSE factor has not been evaluated. The purpose of this study was to examine the effect of
SENSE factor according the scanner field strength, i.e., 1.5 T and 3.0 T on the cortical thickness measurement.
Ten healthy volunteers underwent MRI using 3D T1 turbo field echo (TFE) sequence at both 1.5 T and 3.0 T MRI scanners (Intera Achieva, Philips
Medical Systems) with different SENSE factors. The SENSE factors compared were 1.0 (without SENSE), 1.5, 2.0, 2.5, 3.0, and 4.0 at both 1.5T and
3.0 T scanners. The images acquired without SENSE at 3.0T scanner was regarded as a reference for all other images since it has the highest SNR
among the acquisition in theory. Twelve scans per subject and in total 120 T1-TFE images were acquired. All 3D T1-TFE sequences compared in this
study comprised the following acquisition parameters: 182 coronal acquisition with a 224×256 matrix; 220 mm field of view; 0.98×0.98×1.2 mm3
voxels; TE, 4.6 ms; TR, 9.7 ms; flip angle, 8°; slice gap, 0 mm; 1 averaging per slice. Cortical thickness of the entire brain was automatically
calculated using Freesurfer v.3.0.3 (MGH, Harvard, http://surfer.nmr.mgh.harvard.edu), one of most widely used software for cortical thickness
measurement. Automated calculation of thickness from all images was performed without any manual intervention at a quad PowerMac which takes
about 28 hours per image. In order to compare cortical thickness between scans and between subjects, all cortical thickness measurement were
transformed into a template surface space using a surface-based registration scheme. The template surface was generated from the data used in the
study. In order to simplify comparison between scans, cortical surface of the template was subdivided into 34 regions (4) and mean thickness at each
cortical region was calculated for each scan. The global cortical thickness was compared among the MRI at each scanner with various SENSE factors
by using repeated measures one-way ANOVA. Between the scanners, Wilcoxon sign ranks test was performed to test difference of the mean global
The acquisition time and global mean thickness of the entire cortical gray matter for each protocol was displayed in the Table 1. The maximum
SENSE factor without significant difference of the mean global cortical thickness among MRI was 3 at 3.0 T and 2.5 at 1.5 T. All mean global
cortical thickness was significantly higher at 3.0 T (p = 0.005). Repeated measures of ANOVA of 3.0 T data showed the significant effect (p<0.001)
of SENSE factors on the mean cortical thickness at visual cortex (p=0.000001), postcentral gyrus (p=0.0006), superior temporal gyrus (p=0.00005)
except for lateral orbito-frontal lobe (p=0.00007). In 1.5 T scanner, most cortical regions (22 out of 34 for p<0.01 and 18 for p<0.001) were
significantly affected by the SENSE factor in terms of the measurement of mean cortical thickness.
Discussion and Conclusion
The time required for structural scan was reduced almost inversely proportional to the SENSE factor. The cortical thickness measured at 3.0 T biased
to be thicker than that of 1.5 T. The SENSE factors affect cortical thickness measurements highly in the 1.5T while little in the 3.0 T. According to the
results, fast imaging can be done with high SENSE factor for example, 2.5 and 3.0 without loss of big image quality at 3.0 T. In this study, the
comparison data includes the surface registration with surface smoothing which might cause measurement errors. However, most of neuroimaging
area requires the basic procedure done by our experiments and the results of our data explains the reliability of cortical thickness measurement on the
practical environment of the research. In the current study, the effect of the SENSE factor on subcortical regions was not tested. By comparing data
using voxel-based morphometry, the reliability could also be estimated. In conclusion, high SENSE factor in 3.0 T can be acceptable for the current
state-of-the art computational algorithm to estimate reliable cortical thickness.
Table 1. Acquistion time and mean gray matter thickness of entire cortical surface accoring to the SENSE factor and the field strength.
S1.0-3.0T S1.5-3.0T S2.0-3.0T S2.5-3.0T S3.0-3.0T S4.0-3.0T S1.0-1.5T S1.5-1.5T S2.0-1.5T S2.5-1.5T S3.0-1.5T S4.0-1.5T
11:28 8:32 6:23 5:5 4:14 3:14
2.37(.036) 2.39(.056) 2.39(.056) 2.39(.064) 2.39(.066) 2.37(.066) 2.29(.056) 2.31(.056) 2.31(.067) 2.30(.064) 2.29(.051) 2.23(.066)
11:28 8:32 6:23 5:5 4:14 3:14
*Thickness: Mean(SD) in mm unit.
1. Fischl B, Sereno MI, Dale AM. Cortical surface-based analysis. II: Inflation, flattening, and a surface-based coordinate system. NeuroImage
2. Dale AM, Fischl B, Sereno MI. Cortical surface-based analysis. I. Segmentation and surface reconstruction. NeuroImage 1999;9(2):179-194.
3. Han X, Jovicich J, Salat D, van der Kouwe A, Quinn B, Czanner S, Busa E, Pacheco J, Albert M, Killiany R, Maguire P, Rosas D, Makris N, Dale
A, Dickerson B, Fischl B. Reliability of MRI-derived measurements of human cerebral cortical thickness: the effects of field strength, scanner
upgrade and manufacturer. NeuroImage 2006;32(1):180-194.
4. Desikan RS, Segonne F, Fischl B, Quinn BT, Dickerson BC, Blacker D, Buckner RL, Dale AM, Maguire RP, Hyman BT, Albert MS, Killiany RJ.
An automated labeling system for subdividing the human cerebral cortex on MRI scans into gyral based regions of interest. NeuroImage
Proc. Intl. Soc. Mag. Reson. Med. 15 (2007) 3714