目的基于中老年腰椎矢状面MRI测量椎基静脉孔(basivertebral foramen,BF)的解剖学参数,为相关手术提供解剖学依据。方法回顾2018年2月至2022年12月广州中医药大学第一附属医院43例成年患者,共计172例椎体的腰椎矢状面MRI,使用PACS影像...目的基于中老年腰椎矢状面MRI测量椎基静脉孔(basivertebral foramen,BF)的解剖学参数,为相关手术提供解剖学依据。方法回顾2018年2月至2022年12月广州中医药大学第一附属医院43例成年患者,共计172例椎体的腰椎矢状面MRI,使用PACS影像系统测量L_(1)~L_(4)BF的深(basivertebral foramen depth,BFD)、BF的高(basivertebral foramen height,BFH)、椎体宽(vertebra width,VW)、椎基静脉孔上端至上终板距离(the upper end of BF between the upper boundary and the endplate,VH1)以及椎基静脉孔下端至下终板距离(the lower end of BF between the lower boundary and the endplate,VH2),计算BFD与VW的比值BFD/VW以及BFH与椎体高(vertebra height,VH)的比值BFH/VH。结果在STIR序列中,L_(1)椎体BFD(3.93±1.58)mm低于L_(3)(5.26±2.34)mm与L_(4)(5.82±2.99)mm,有统计学意义(P<0.05);L_(1)椎体BFD/VW(0.12±0.05)与L_(4)(0.17±0.09)对比有统计学意义(P<0.05),且L_(1)~L_(4)的BFD/VW组间对比有统计学差异(P<0.05),各椎体的BFH/VH对比无统计学意义(P>0.05)。男性与女性对比,T2WI序列中L_(3)椎体BFH男性(7.95±2.84)mm高于女性(6.30±1.93)mm,有统计学意义(P<0.05)。随着椎体序列的增长,BFD呈现逐渐增加的趋势,在STIR序列中更加明显。而BFH随椎体序列增加无明显起伏。结论中老年MRI矢状位STIR序列可作为其BF的临床测量标准,为腰椎手术方案的选择提供解剖学依据。展开更多
With the development of human-computer interaction technology,brain-computer interface(BCI)has been widely used in medical,entertainment,military,and other fields.Imagined speech is the latest paradigm of BCI and repr...With the development of human-computer interaction technology,brain-computer interface(BCI)has been widely used in medical,entertainment,military,and other fields.Imagined speech is the latest paradigm of BCI and represents the mental process of imagining a word without making a sound or making clear facial movements.Imagined speech allows patients with physical disabilities to communicate with the outside world and use smart devices through imagination.Imagined speech can meet the needs of more complex manipulative tasks considering its more intuitive features.This study proposes a classification method of imagined speech Electroencephalogram(EEG)signals with discrete wavelet transform(DWT)and support vector machine(SVM).An open dataset that consists of 15 subjects imagining speaking six different words,namely,up,down,left,right,backward,and forward,is used.The objective is to improve the classification accuracy of imagined speech BCI system.The features of EEG signals are first extracted by DWT,and the imagined words are clas-sified by SVM with the above features.Experimental results show that the proposed method achieves an average accuracy of 61.69%,which is better than those of existing methods for classifying imagined speech tasks.展开更多
文摘目的基于中老年腰椎矢状面MRI测量椎基静脉孔(basivertebral foramen,BF)的解剖学参数,为相关手术提供解剖学依据。方法回顾2018年2月至2022年12月广州中医药大学第一附属医院43例成年患者,共计172例椎体的腰椎矢状面MRI,使用PACS影像系统测量L_(1)~L_(4)BF的深(basivertebral foramen depth,BFD)、BF的高(basivertebral foramen height,BFH)、椎体宽(vertebra width,VW)、椎基静脉孔上端至上终板距离(the upper end of BF between the upper boundary and the endplate,VH1)以及椎基静脉孔下端至下终板距离(the lower end of BF between the lower boundary and the endplate,VH2),计算BFD与VW的比值BFD/VW以及BFH与椎体高(vertebra height,VH)的比值BFH/VH。结果在STIR序列中,L_(1)椎体BFD(3.93±1.58)mm低于L_(3)(5.26±2.34)mm与L_(4)(5.82±2.99)mm,有统计学意义(P<0.05);L_(1)椎体BFD/VW(0.12±0.05)与L_(4)(0.17±0.09)对比有统计学意义(P<0.05),且L_(1)~L_(4)的BFD/VW组间对比有统计学差异(P<0.05),各椎体的BFH/VH对比无统计学意义(P>0.05)。男性与女性对比,T2WI序列中L_(3)椎体BFH男性(7.95±2.84)mm高于女性(6.30±1.93)mm,有统计学意义(P<0.05)。随着椎体序列的增长,BFD呈现逐渐增加的趋势,在STIR序列中更加明显。而BFH随椎体序列增加无明显起伏。结论中老年MRI矢状位STIR序列可作为其BF的临床测量标准,为腰椎手术方案的选择提供解剖学依据。
基金supported in part by the Fundamental Research Funds for the Central Universities(xcxjh20210104).
文摘With the development of human-computer interaction technology,brain-computer interface(BCI)has been widely used in medical,entertainment,military,and other fields.Imagined speech is the latest paradigm of BCI and represents the mental process of imagining a word without making a sound or making clear facial movements.Imagined speech allows patients with physical disabilities to communicate with the outside world and use smart devices through imagination.Imagined speech can meet the needs of more complex manipulative tasks considering its more intuitive features.This study proposes a classification method of imagined speech Electroencephalogram(EEG)signals with discrete wavelet transform(DWT)and support vector machine(SVM).An open dataset that consists of 15 subjects imagining speaking six different words,namely,up,down,left,right,backward,and forward,is used.The objective is to improve the classification accuracy of imagined speech BCI system.The features of EEG signals are first extracted by DWT,and the imagined words are clas-sified by SVM with the above features.Experimental results show that the proposed method achieves an average accuracy of 61.69%,which is better than those of existing methods for classifying imagined speech tasks.
