目的采用非对称采集与迭代最小二乘估算法迭代水脂分离(iteraterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation,IDEAL-IQ)方法定量评估冈上肌腱损伤的严重程度与肩袖肌群脂肪...目的采用非对称采集与迭代最小二乘估算法迭代水脂分离(iteraterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation,IDEAL-IQ)方法定量评估冈上肌腱损伤的严重程度与肩袖肌群脂肪浸润程度及受试者特征之间的关系。材料与方法回顾性分析2022年8月至2024年6月本院经肩关节镜证实的33例冈上肌腱部分撕裂患者及89例完全撕裂患者,均进行了常规MRI扫描及IDEAL-IQ序列扫描。由两名放射科医生分别对所有受试者的MRI图像进行独立评估,根据常规MRI图像的冈上肌腱损伤表现,将完全撕裂组的冈上肌腱按照Patte分型分为Patte 1型(Ⅱ级)、Patte 2型(Ⅲ级)、Patte 3型(Ⅳ级),将部分撕裂组定义为Ⅰ级。同时在斜矢状位上进行Goutallier分级及Thomazeau萎缩分级,并通过GE ADW 4.7工作站后处理软件在IDEAL-IQ序列生成的脂肪分数图像上测量冈上肌、冈下肌、肩胛下肌及小圆肌脂肪分数(fat fraction,FF)。用组内相关系数(intra-class correlation coefficient,ICC)及Kappa一致性检验评估观察者间及观察者内的一致性。采用Kruskal-Wallis H检验、单因素ANOVA检验分析FF值在不同分组之间的差异,组间两两比较用Bonferroni检验。采用Pear_(s)on相关性分析肩袖肌肉FF值与年龄、症状持续时间的相关性(相关系数r),Spearman相关性分析冈上肌腱损伤分级与肩袖肌群FF值、Goutallier分级及Thomazeau萎缩分级之间的相关性(相关系数r_(s))。结果(1)冈上肌、冈下肌、肩胛下肌的FF值在冈上肌腱损伤Ⅳ级中显著高于Ⅲ级,高于Ⅱ级和Ⅰ级,差异有统计学意义(P值分别为<0.001、<0.001、0.005);小圆肌的FF值在不同分级之间差异无统计学意义(P=0.073)。组内比较Ⅰ级和Ⅱ级的冈上肌、冈下肌、肩胛下肌、小圆肌FF值差异无统计学意义(P值分别为0.026、0.102);Ⅲ级和Ⅳ级的FF值差异有统计学意义(P<0.001)。(2)冈上肌、冈下肌、小圆肌的FF值与年龄呈中等相关(r值分别为0.381、0.339、0.349,P均<0.001),肩胛下肌的FF值与年龄呈弱相关(r=0.216,P=0.017);冈上肌、冈下肌、肩胛下肌FF值与症状持续时间呈中等程度相关(r分别为0.442、0.412、0.314,P均<0.001),小圆肌的FF值与症状持续时间呈弱相关(r=0.277,P=0.002);冈上肌腱损伤程度与冈上肌FF值呈显著相关(r_(s)=0.740,P<0.001),与冈下肌的FF值呈强相关性(r_(s)=0.596,P<0.001),与肩胛下肌、小圆肌的FF值呈弱相关(r_(s)分别为0.257、0.212,P值分别为0.004、0.019);冈上肌损伤程度分级与Goutallier分级、Thomazeau分级之间呈显著正相关(r_(s)分别为0.757、0.737,P均<0.001),且冈上肌FF值在Goutallier和Thomazeau的分级中差异具有统计学意义(P均<0.001)。结论3.0 T MR IDEAL-IQ序列能量化和客观评估肩袖肌群脂肪浸润程度,肩袖肌群脂肪浸润程度与冈上肌腱损伤分级呈正相关,与年龄、症状持续时间呈正相关。展开更多
Instant Digital Express iDEAL-CIO The “Magic Lamp” for Cloud Intelligence Outlet, which has been recommended, combines innovations to satisfy modern users’ needs and efficiently sift through the ever-expanding amou...Instant Digital Express iDEAL-CIO The “Magic Lamp” for Cloud Intelligence Outlet, which has been recommended, combines innovations to satisfy modern users’ needs and efficiently sift through the ever-expanding amount of intelligent content stored in the cloud. One such innovation introduces a ground-breaking concept to remove superfluous and outdated sequential search patterns that overwhelm the user and computer in order to better serve the user in an eclectic & elastic and multidimensional approach to finding, grouping, assimilation, organizing, and delivering archival content. The cloud intelligence outlet (CIO) is presented in this article as the perfect magic lamp option for quick digital express advocacy. The grouping, indexing, folding, and targeting (GIFT) method of multidimensional online synthetic/analytical intelligent content (MOSAIC) for adaptive intelligence is the fundamental intelligent aggregation and automated process of the Magic Lamp. Three perspectives above this new ideal framework are available to observe: The Magic Lamp proposes contextual and multiple analytical tracks to improve cloud intelligence services conceptually. Technically speaking, MOSAIC combines domain-specific services for a wide range of international users, and through the usage of Cloud Intelligence Outlet, GIFT operationally activates grouping, indexing, folding, and targeting to promote decent experience and in-depth research on target for users’ wants. Because of this, iDEAL-CIO works in tandem with cloud extraction, digital transformation, and archival loading to provide improved service through the readily accessible cloud intelligence outlet.展开更多
目的 :对比研究IDEAL序列和频率选择预饱和脂肪抑制方法的信噪比(signal to noise ratio,SNR)、对比噪声比(contrast to noise ratio,CNR)和脂肪抑制的均匀性,以明确IDEAL序列在乳腺MR成像中的优缺点。方法:选取19名行乳腺MR检查的患者...目的 :对比研究IDEAL序列和频率选择预饱和脂肪抑制方法的信噪比(signal to noise ratio,SNR)、对比噪声比(contrast to noise ratio,CNR)和脂肪抑制的均匀性,以明确IDEAL序列在乳腺MR成像中的优缺点。方法:选取19名行乳腺MR检查的患者,选择病变相同的层面测量SNR和CNR;对2种脂肪抑制方法所得图像选择相同层面的20个相同感兴趣区,测量皮下脂肪的信号值,并绘图进行比较。结果:IDEAL序列的SNR=7.159±0.279,频率选择预饱和脂肪抑制的SNR=5.012±0.243(P=0.000);IDEAL序列的CNR=24.643±1.598,频率选择预饱和脂肪抑制的CNR=21.832±2.096(P=0.031)。IDEAL序列的信号空间分布曲线形态平缓,频率选择预饱和脂肪抑制的信号空间分布曲线形态陡峭。结论:IDEAL序列具有良好的信噪比、对比噪声比和脂肪抑制均匀性,可作为乳腺MRI检查的首选序列。展开更多
文摘目的采用非对称采集与迭代最小二乘估算法迭代水脂分离(iteraterative decomposition of water and fat with echo asymmetry and least-squares estimation quantitation,IDEAL-IQ)方法定量评估冈上肌腱损伤的严重程度与肩袖肌群脂肪浸润程度及受试者特征之间的关系。材料与方法回顾性分析2022年8月至2024年6月本院经肩关节镜证实的33例冈上肌腱部分撕裂患者及89例完全撕裂患者,均进行了常规MRI扫描及IDEAL-IQ序列扫描。由两名放射科医生分别对所有受试者的MRI图像进行独立评估,根据常规MRI图像的冈上肌腱损伤表现,将完全撕裂组的冈上肌腱按照Patte分型分为Patte 1型(Ⅱ级)、Patte 2型(Ⅲ级)、Patte 3型(Ⅳ级),将部分撕裂组定义为Ⅰ级。同时在斜矢状位上进行Goutallier分级及Thomazeau萎缩分级,并通过GE ADW 4.7工作站后处理软件在IDEAL-IQ序列生成的脂肪分数图像上测量冈上肌、冈下肌、肩胛下肌及小圆肌脂肪分数(fat fraction,FF)。用组内相关系数(intra-class correlation coefficient,ICC)及Kappa一致性检验评估观察者间及观察者内的一致性。采用Kruskal-Wallis H检验、单因素ANOVA检验分析FF值在不同分组之间的差异,组间两两比较用Bonferroni检验。采用Pear_(s)on相关性分析肩袖肌肉FF值与年龄、症状持续时间的相关性(相关系数r),Spearman相关性分析冈上肌腱损伤分级与肩袖肌群FF值、Goutallier分级及Thomazeau萎缩分级之间的相关性(相关系数r_(s))。结果(1)冈上肌、冈下肌、肩胛下肌的FF值在冈上肌腱损伤Ⅳ级中显著高于Ⅲ级,高于Ⅱ级和Ⅰ级,差异有统计学意义(P值分别为<0.001、<0.001、0.005);小圆肌的FF值在不同分级之间差异无统计学意义(P=0.073)。组内比较Ⅰ级和Ⅱ级的冈上肌、冈下肌、肩胛下肌、小圆肌FF值差异无统计学意义(P值分别为0.026、0.102);Ⅲ级和Ⅳ级的FF值差异有统计学意义(P<0.001)。(2)冈上肌、冈下肌、小圆肌的FF值与年龄呈中等相关(r值分别为0.381、0.339、0.349,P均<0.001),肩胛下肌的FF值与年龄呈弱相关(r=0.216,P=0.017);冈上肌、冈下肌、肩胛下肌FF值与症状持续时间呈中等程度相关(r分别为0.442、0.412、0.314,P均<0.001),小圆肌的FF值与症状持续时间呈弱相关(r=0.277,P=0.002);冈上肌腱损伤程度与冈上肌FF值呈显著相关(r_(s)=0.740,P<0.001),与冈下肌的FF值呈强相关性(r_(s)=0.596,P<0.001),与肩胛下肌、小圆肌的FF值呈弱相关(r_(s)分别为0.257、0.212,P值分别为0.004、0.019);冈上肌损伤程度分级与Goutallier分级、Thomazeau分级之间呈显著正相关(r_(s)分别为0.757、0.737,P均<0.001),且冈上肌FF值在Goutallier和Thomazeau的分级中差异具有统计学意义(P均<0.001)。结论3.0 T MR IDEAL-IQ序列能量化和客观评估肩袖肌群脂肪浸润程度,肩袖肌群脂肪浸润程度与冈上肌腱损伤分级呈正相关,与年龄、症状持续时间呈正相关。
文摘Instant Digital Express iDEAL-CIO The “Magic Lamp” for Cloud Intelligence Outlet, which has been recommended, combines innovations to satisfy modern users’ needs and efficiently sift through the ever-expanding amount of intelligent content stored in the cloud. One such innovation introduces a ground-breaking concept to remove superfluous and outdated sequential search patterns that overwhelm the user and computer in order to better serve the user in an eclectic & elastic and multidimensional approach to finding, grouping, assimilation, organizing, and delivering archival content. The cloud intelligence outlet (CIO) is presented in this article as the perfect magic lamp option for quick digital express advocacy. The grouping, indexing, folding, and targeting (GIFT) method of multidimensional online synthetic/analytical intelligent content (MOSAIC) for adaptive intelligence is the fundamental intelligent aggregation and automated process of the Magic Lamp. Three perspectives above this new ideal framework are available to observe: The Magic Lamp proposes contextual and multiple analytical tracks to improve cloud intelligence services conceptually. Technically speaking, MOSAIC combines domain-specific services for a wide range of international users, and through the usage of Cloud Intelligence Outlet, GIFT operationally activates grouping, indexing, folding, and targeting to promote decent experience and in-depth research on target for users’ wants. Because of this, iDEAL-CIO works in tandem with cloud extraction, digital transformation, and archival loading to provide improved service through the readily accessible cloud intelligence outlet.
文摘目的 :对比研究IDEAL序列和频率选择预饱和脂肪抑制方法的信噪比(signal to noise ratio,SNR)、对比噪声比(contrast to noise ratio,CNR)和脂肪抑制的均匀性,以明确IDEAL序列在乳腺MR成像中的优缺点。方法:选取19名行乳腺MR检查的患者,选择病变相同的层面测量SNR和CNR;对2种脂肪抑制方法所得图像选择相同层面的20个相同感兴趣区,测量皮下脂肪的信号值,并绘图进行比较。结果:IDEAL序列的SNR=7.159±0.279,频率选择预饱和脂肪抑制的SNR=5.012±0.243(P=0.000);IDEAL序列的CNR=24.643±1.598,频率选择预饱和脂肪抑制的CNR=21.832±2.096(P=0.031)。IDEAL序列的信号空间分布曲线形态平缓,频率选择预饱和脂肪抑制的信号空间分布曲线形态陡峭。结论:IDEAL序列具有良好的信噪比、对比噪声比和脂肪抑制均匀性,可作为乳腺MRI检查的首选序列。
