Sensitivity of voxel-based morphometry analysis to choice of imaging protocol at 3 T

被引:59
|
作者
Tardif, Christine L. [1 ]
Collins, D. Louis [1 ]
Pike, G. Bruce [1 ]
机构
[1] McGill Univ, Montreal Neurol Inst, McConnell Brain Imaging Ctr, Montreal, PQ H3A 2B4, Canada
基金
加拿大健康研究院;
关键词
GRAY-MATTER ATROPHY; MP-RAGE SEQUENCES; HUMAN BRAIN; ALZHEIMERS-DISEASE; WHITE-MATTER; STATISTICAL-ANALYSIS; CORTICAL THICKNESS; LEWY BODIES; MRI; OPTIMIZATION;
D O I
10.1016/j.neuroimage.2008.09.053
中图分类号
Q189 [神经科学];
学科分类号
071006 ;
摘要
The objective of this study was to determine which 3D T-1-weighted acquisition protocol at 3 T is best suited to voxel-based morphometry (VBM), and to characterize the sensitivity of VBM to choice of acquisition. First, image quality of three commonly used protocols, FLASH, MP-RAGE and MDEFT, was evaluated in terms of SNR, CNR, image uniformity and point spread function. These image metrics were estimated from simulations, phantom imaging and human studies. We then performed a VBM study on nine subjects scanned twice using the three protocols to evaluate differences in grey matter (GM) density and scan-rescan variability between the protocols. These results reveal the relative bias and precision of the tissue classification obtained using the different protocols. MDEFT achieved the highest CNR between white and grey matter, and the lowest GM density variability of the three sequences. Each protocol is also characterized by a distinct regional bias in GM density due to the effect of transmission field inhomogeneity on image uniformity combined with spatially variant GM T-1 values and the sequence's T-1 contrast function. The required population sample size estimates to detect a difference in GM density in longitudinal VBM studies, i.e. based only on methodological variance, were lowest for MDEFT. Although MP-RAGE requires more subjects than FLASH, its higher cortical CNR improves the accuracy of the tissue classification results, particularly in the motor cortex. For cross-sectional VBM studies, the variance in morphology across the population is likely to be the primary source of variability in the power analysis. (C) 2008 Elsevier Inc. All rights reserved.
引用
收藏
页码:827 / 838
页数:12
相关论文
共 50 条
  • [41] Developmental brain structural atypicalities in autism: a voxel-based morphometry analysis
    Wang, Hui
    Ma, Zeng-Hui
    Xu, Ling-Zi
    Yang, Liu
    Ji, Zhao-Zheng
    Tang, Xin-Zhou
    Liu, Jing-Ran
    Li, Xue
    Cao, Qing-Jiu
    Liu, Jing
    CHILD AND ADOLESCENT PSYCHIATRY AND MENTAL HEALTH, 2022, 16 (01)
  • [42] Enhanced sensitivity to optimistic cues is manifested in brain structure: a voxel-based morphometry study
    Aue, Tatjana
    Dricu, Mihai
    Singh, Laura
    Moser, Dominik A.
    Kotikalapudi, Raviteja
    SOCIAL COGNITIVE AND AFFECTIVE NEUROSCIENCE, 2021, 16 (11) : 1170 - 1181
  • [43] Voxel-based morphometry (VBM) analysis in major depression and bipolar disorder
    Anand, A
    Li, Y
    BIOLOGICAL PSYCHIATRY, 2005, 57 (08) : 188S - 188S
  • [44] White matter and schizophrenia: A meta-analysis of voxel-based morphometry and diffusion tensor imaging studies
    Vitolo, Enrico
    Tatu, Mona Karina
    Pignolo, Claudia
    Cauda, Franco
    Costa, Tommaso
    Ando', Agata
    Zennaro, Alessandro
    PSYCHIATRY RESEARCH-NEUROIMAGING, 2017, 270 : 8 - 21
  • [45] Regional gray matter volume is associated with rejection sensitivity: A voxel-based morphometry study
    Jiangzhou Sun
    Haijiang Li
    Wenfu Li
    Dongtao Wei
    Glenn Hitchman
    Qinglin Zhang
    Jiang Qiu
    Cognitive, Affective, & Behavioral Neuroscience, 2014, 14 : 1077 - 1085
  • [46] Differences of white matter structure for diffusion kurtosis imaging using voxel-based morphometry and connectivity analysis
    Kanazawa, Yuki
    Ikemitsu, Natsuki
    Kinjo, Yuki
    Harada, Masafumi
    Hayashi, Hiroaki
    Taniguchi, Yo
    Ito, Kosuke
    Bito, Yoshitaka
    Matsumoto, Yuki
    Haga, Akihiro
    BJR OPEN, 2023, 6 (01):
  • [47] Brain structure in autism: a voxel-based morphometry analysis of the Autism Brain Imaging Database Exchange (ABIDE)
    Kaitlin Riddle
    Carissa J. Cascio
    Neil D. Woodward
    Brain Imaging and Behavior, 2017, 11 : 541 - 551
  • [48] Brain structure in autism: a voxel-based morphometry analysis of the Autism Brain Imaging Database Exchange (ABIDE)
    Riddle, Kaitlin
    Cascio, Carissa J.
    Woodward, Neil D.
    BRAIN IMAGING AND BEHAVIOR, 2017, 11 (02) : 541 - 551
  • [49] Regional gray matter volume is associated with rejection sensitivity: A voxel-based morphometry study
    Sun, Jiangzhou
    Li, Haijiang
    Li, Wenfu
    Wei, Dongtao
    Hitchman, Glenn
    Zhang, Qinglin
    Qiu, Jiang
    COGNITIVE AFFECTIVE & BEHAVIORAL NEUROSCIENCE, 2014, 14 (03) : 1077 - 1085
  • [50] Pain in the default mode network: a voxel-based morphometry study on thermal pain sensitivity
    Zhang, Xilei
    Chen, Qunlin
    Su, Yanhua
    Meng, Jing
    Qiu, Jiang
    Zheng, Wenming
    NEUROREPORT, 2020, 31 (14) : 1030 - 1035