目的基于ACR体模对MRI系统进行质量控制,设计图像信噪比(Signal to Noise Ratio,SNR)及均匀度自动测量软件,提高质控检测参数测算的效率。方法基于ACR体模质控检测SNR及图像均匀度测算方法,并根据图像特征,在Matlab R2013b平台设计编写...目的基于ACR体模对MRI系统进行质量控制,设计图像信噪比(Signal to Noise Ratio,SNR)及均匀度自动测量软件,提高质控检测参数测算的效率。方法基于ACR体模质控检测SNR及图像均匀度测算方法,并根据图像特征,在Matlab R2013b平台设计编写图像预处理、特征提取等算法,实现对SNR及图像均匀度自动化处理及分析,并采用Bland-Altman统计学方法评价手工测算与自动测量两种方法的一致性。结果基于Matlab R2013b平台设计编写的自动检测程序实现了对SNR及图像均匀度的自动测量,Bland-Altman图显示,手工测算和自动测算测得的SNR的数据点均位于一致性界限的范围内(-53.1,40.5),图像均匀度的数据点均位于一致性界限的范围内(-4.7%,3.0%),说明两种方法的一致性较好。结论基于Matlab平台的自动化评估算法易于实现,可极大提高质控参数测算效率,有望与手工测量相互替代。展开更多
In this study, water permeation through cementitious materials was observed using magnetic resonance imaging (MRI). The influence of cement type on the magnetic resonance signal was studied subsequent to determining t...In this study, water permeation through cementitious materials was observed using magnetic resonance imaging (MRI). The influence of cement type on the magnetic resonance signal was studied subsequent to determining the parameters required for imaging. Consequently, adequate imaging of water permeating through hardened cement paste (HCP) made with white Portland cement was achieved, while water permeation through ordinary Portland cement-based HCP yielded poor signal. HCPs maintained at various levels of relative humidity (RH) were observed, and the signal was detected only from those maintained at an RH of higher than 85%. The water permeation depths in HCP were observed by using MRI, and the measured depths were compared to those measured via a spraying water detector on the split surface of the specimens. As a result, good agreement was confirmed between the two methods. Additionally, MRI was applied to concrete specimens;although it was found that water was not detected when a lightweight aggregate was used, water permeation through concrete with limestone aggregate was detectable via MRI. MRI will help in understanding how water permeation causes and accelerates concrete deteriorations such as rebar corrosion and freezing and thawing.展开更多
Background: Discrete clinical and pathological subtypes of Alzheimer’s disease (AD) with variable presentations and rates of progression are well known. These subtypes may have specific patterns of regional brain atr...Background: Discrete clinical and pathological subtypes of Alzheimer’s disease (AD) with variable presentations and rates of progression are well known. These subtypes may have specific patterns of regional brain atrophy, which are identifiable on MRI scans. Methods: To examine distinct regions which had distinct underlying patterns of cortical atrophy, factor analytic techniques applied to structural MRI volumetric data from cognitively normal (CN) (n = 202), amnestic mild cognitive impairment (aMCI) (n = 333) or mild AD (n = 146) subjects, in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database was applied. This revealed the existence of two neocortical (NeoC-1 and NeoC-2), and a limbic cluster of atrophic brain regions. The frequency and clinical correlates of these regional patterns of atrophy were evaluated among the three diagnostic groups, and the rates of progression from aMCI to AD, over 24 months were evaluated. Results: Discernable patterns of regional atrophy were observed in about 29% of CN, 55% of aMCI and 83% of AD subjects. Heterogeneity in clinical presentation and APOE ε4 frequency were associated with regional patterns of atrophy on MRI scans. The most rapid progression rates to dementia among aMCI subjects (n = 224), over a 24-month period, were in those with NeoC-1 regional impairment (68.2%), followed by the Limbic regional impairment (48.8%). The same pattern of results was observed when only aMCI amyloid positive subjects were examined. Conclusions: The neuroimaging results closely parallel findings described recently among AD patients with the hippocampal sparing and limbic subtypes of AD neuropathology at autopsy. We conclude that NeoC-1, Limbic and other patterns of MRI atrophy may be useful markers for predicting the rate of progression of aMCI to AD and could have utility selecting individuals at higher risk for progression in clinical trials.展开更多
文摘In this study, water permeation through cementitious materials was observed using magnetic resonance imaging (MRI). The influence of cement type on the magnetic resonance signal was studied subsequent to determining the parameters required for imaging. Consequently, adequate imaging of water permeating through hardened cement paste (HCP) made with white Portland cement was achieved, while water permeation through ordinary Portland cement-based HCP yielded poor signal. HCPs maintained at various levels of relative humidity (RH) were observed, and the signal was detected only from those maintained at an RH of higher than 85%. The water permeation depths in HCP were observed by using MRI, and the measured depths were compared to those measured via a spraying water detector on the split surface of the specimens. As a result, good agreement was confirmed between the two methods. Additionally, MRI was applied to concrete specimens;although it was found that water was not detected when a lightweight aggregate was used, water permeation through concrete with limestone aggregate was detectable via MRI. MRI will help in understanding how water permeation causes and accelerates concrete deteriorations such as rebar corrosion and freezing and thawing.
文摘Background: Discrete clinical and pathological subtypes of Alzheimer’s disease (AD) with variable presentations and rates of progression are well known. These subtypes may have specific patterns of regional brain atrophy, which are identifiable on MRI scans. Methods: To examine distinct regions which had distinct underlying patterns of cortical atrophy, factor analytic techniques applied to structural MRI volumetric data from cognitively normal (CN) (n = 202), amnestic mild cognitive impairment (aMCI) (n = 333) or mild AD (n = 146) subjects, in the Alzheimer’s Disease Neuroimaging Initiative (ADNI) database was applied. This revealed the existence of two neocortical (NeoC-1 and NeoC-2), and a limbic cluster of atrophic brain regions. The frequency and clinical correlates of these regional patterns of atrophy were evaluated among the three diagnostic groups, and the rates of progression from aMCI to AD, over 24 months were evaluated. Results: Discernable patterns of regional atrophy were observed in about 29% of CN, 55% of aMCI and 83% of AD subjects. Heterogeneity in clinical presentation and APOE ε4 frequency were associated with regional patterns of atrophy on MRI scans. The most rapid progression rates to dementia among aMCI subjects (n = 224), over a 24-month period, were in those with NeoC-1 regional impairment (68.2%), followed by the Limbic regional impairment (48.8%). The same pattern of results was observed when only aMCI amyloid positive subjects were examined. Conclusions: The neuroimaging results closely parallel findings described recently among AD patients with the hippocampal sparing and limbic subtypes of AD neuropathology at autopsy. We conclude that NeoC-1, Limbic and other patterns of MRI atrophy may be useful markers for predicting the rate of progression of aMCI to AD and could have utility selecting individuals at higher risk for progression in clinical trials.
