目的探讨合成磁共振成像(SynMRI)联合三维动脉自旋标记成像(3D-ASL)序列在AD中双侧海马定量值的变化,研究二者联合对阿尔兹海默病(AD)早期诊断的价值。方法收集2022年1月-6月在郑州大学第二附属医院就诊经临床诊断为AD患者24例作为阿尔...目的探讨合成磁共振成像(SynMRI)联合三维动脉自旋标记成像(3D-ASL)序列在AD中双侧海马定量值的变化,研究二者联合对阿尔兹海默病(AD)早期诊断的价值。方法收集2022年1月-6月在郑州大学第二附属医院就诊经临床诊断为AD患者24例作为阿尔兹海默病组(AD组),同时按照年龄、性别和教育程度相匹配的条件下从健康体检者中纳入26例作为本次实验的健康对照组(HC组)。所有受试者均在3.0T GE MRI扫描仪上进行MAGiC序列和3D-ASL序列扫描,选取双测海马作为感兴趣区(ROI),并分别测量其感兴趣区的T1、T2、PD及CBF值,并且所有受试者均获得简易精神状态检查(MMSE)评分。记录AD组与HC组双侧海马值,并与MMSE评分之间进行Pearson相关分析。结果AD组患者与HC组之间双侧海马的T1值及PD值比较,差异无统计学意义(P>0.05);AD组的T2值高于HC组,而CBF值低于HC组(P<0.05);AD患者随着MMSE评分的降低,双侧海马的T1值、T2值升高、CBF值降低(P<0.05)。结论合成MRI联合3D-ASL对AD患者双侧海马区域具有很好的定量价值,可以对AD进行早期诊断。展开更多
A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition analyzed, compared and assessed detailedly. F...A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition analyzed, compared and assessed detailedly. First, radar imaging model and super-resolution reconstruction mechanism were outlined. Then, the adaptive-threshold SVD super-resolution algorithm, and its two key aspects, namely the determination method of point spread function （PSF） matrix T and the selection scheme of singular value threshold, were presented. Finally, the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar （SAR） images, and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio （SNR）. Five versions of SVD algorithms, namely 1 ） using all singular values, 2） using the top 80% singular values, 3） using the top 50% singular values, 4） using the top 20% singular values and 5） using singular values s such that S2≥/max（s2）/rinsNR were tested. The experimental results indicate that when the singular value threshold is set as Smax/（rinSNR）1/2, the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.展开更多
The running correlation coefficient(RCC)is useful for capturing temporal variations in correlations between two time series.The local running correlation coefficient(LRCC)is a widely used algorithm that directly appli...The running correlation coefficient(RCC)is useful for capturing temporal variations in correlations between two time series.The local running correlation coefficient(LRCC)is a widely used algorithm that directly applies the Pearson correlation to a time window.A new algorithm called synthetic running correlation coefficient(SRCC)was proposed in 2018 and proven to be rea-sonable and usable;however,this algorithm lacks a theoretical demonstration.In this paper,SRCC is proven theoretically.RCC is only meaningful when its values at different times can be compared.First,the global means are proven to be the unique standard quantities for comparison.SRCC is the only RCC that satisfies the comparability criterion.The relationship between LRCC and SRCC is derived using statistical methods,and SRCC is obtained by adding a constraint condition to the LRCC algorithm.Dividing the temporal fluctuations into high-and low-frequency signals reveals that LRCC only reflects the correlation of high-frequency signals;by contrast,SRCC reflects the correlations of high-and low-frequency signals simultaneously.Therefore,SRCC is the ap-propriate method for calculating RCCs.展开更多
文摘目的探讨合成磁共振成像(SynMRI)联合三维动脉自旋标记成像(3D-ASL)序列在AD中双侧海马定量值的变化,研究二者联合对阿尔兹海默病(AD)早期诊断的价值。方法收集2022年1月-6月在郑州大学第二附属医院就诊经临床诊断为AD患者24例作为阿尔兹海默病组(AD组),同时按照年龄、性别和教育程度相匹配的条件下从健康体检者中纳入26例作为本次实验的健康对照组(HC组)。所有受试者均在3.0T GE MRI扫描仪上进行MAGiC序列和3D-ASL序列扫描,选取双测海马作为感兴趣区(ROI),并分别测量其感兴趣区的T1、T2、PD及CBF值,并且所有受试者均获得简易精神状态检查(MMSE)评分。记录AD组与HC组双侧海马值,并与MMSE评分之间进行Pearson相关分析。结果AD组患者与HC组之间双侧海马的T1值及PD值比较,差异无统计学意义(P>0.05);AD组的T2值高于HC组,而CBF值低于HC组(P<0.05);AD患者随着MMSE评分的降低,双侧海马的T1值、T2值升高、CBF值降低(P<0.05)。结论合成MRI联合3D-ASL对AD患者双侧海马区域具有很好的定量价值,可以对AD进行早期诊断。
基金Project(2008041001) supported by the Academician Foundation of China Project(N0601-041) supported by the General Armament Department Science Foundation of China
文摘A super-resolution reconstruction approach of （SVD） technique was presented, and its performance was radar image using an adaptive-threshold singular value decomposition analyzed, compared and assessed detailedly. First, radar imaging model and super-resolution reconstruction mechanism were outlined. Then, the adaptive-threshold SVD super-resolution algorithm, and its two key aspects, namely the determination method of point spread function （PSF） matrix T and the selection scheme of singular value threshold, were presented. Finally, the super-resolution algorithm was demonstrated successfully using the measured synthetic-aperture radar （SAR） images, and a Monte Carlo assessment was carried out to evaluate the performance of the algorithm by using the input/output signal-to-noise ratio （SNR）. Five versions of SVD algorithms, namely 1 ） using all singular values, 2） using the top 80% singular values, 3） using the top 50% singular values, 4） using the top 20% singular values and 5） using singular values s such that S2≥/max（s2）/rinsNR were tested. The experimental results indicate that when the singular value threshold is set as Smax/（rinSNR）1/2, the super-resolution algorithm provides a good compromise between too much noise and too much bias and has good reconstruction results.
基金This study was supported by the National Natural Sci-ence Foundation of China(Nos.41976022,41941012)the Major Scientific and Technological Innovation Projects of Shandong Province(No.2018SDKJ0104-1).
文摘The running correlation coefficient(RCC)is useful for capturing temporal variations in correlations between two time series.The local running correlation coefficient(LRCC)is a widely used algorithm that directly applies the Pearson correlation to a time window.A new algorithm called synthetic running correlation coefficient(SRCC)was proposed in 2018 and proven to be rea-sonable and usable;however,this algorithm lacks a theoretical demonstration.In this paper,SRCC is proven theoretically.RCC is only meaningful when its values at different times can be compared.First,the global means are proven to be the unique standard quantities for comparison.SRCC is the only RCC that satisfies the comparability criterion.The relationship between LRCC and SRCC is derived using statistical methods,and SRCC is obtained by adding a constraint condition to the LRCC algorithm.Dividing the temporal fluctuations into high-and low-frequency signals reveals that LRCC only reflects the correlation of high-frequency signals;by contrast,SRCC reflects the correlations of high-and low-frequency signals simultaneously.Therefore,SRCC is the ap-propriate method for calculating RCCs.
