目的基于1.5 T MRI,比较常规T2液体衰减反转恢复(T2-fluid attenuated inversion recovery,T2-FLAIR)序列传统重建和快速T2-FLAIR深度学习(deep learning,DL)重建在颅内占位性病变患者的图像质量与临床价值,并探究最佳DL重建参数。材料...目的基于1.5 T MRI,比较常规T2液体衰减反转恢复(T2-fluid attenuated inversion recovery,T2-FLAIR)序列传统重建和快速T2-FLAIR深度学习(deep learning,DL)重建在颅内占位性病变患者的图像质量与临床价值,并探究最佳DL重建参数。材料与方法前瞻性纳入颅内占位性病变患者104例,分别采集常规T2-FLAIR和快速T2-FLAIR[并行采集(parallel imaging,PI)加速因子2]。常规T2-FLAIR采用传统重建,记为NDL组;快速T2-FLAIR选择DL重建等级2、3和4,记为PI-DL2、PI-DL3和PI-DL4组。由两位医师采用盲法对四组图像进行定量评价和定性评价,并记录病变大小和数量。定量评价包括信噪比(signal-to-noise ratio,SNR)和对比噪声比(contrast-to-noise ratio,CNR);定性评价包括图像锐利度、噪声、灰白质对比度、伪影、病变显示、诊断信心和整体图像质量。结果常规T2-FLAIR扫描时间为2 min 8 s,快速T2-FLAIR扫描时间为1 min 20 s,时间缩短约37.5%。定量分析显示,与NDL组相比,各DL重建组(等级2、3、4)的SNR均有提高,且随DL等级增加而提高(P<0.05);PI-DL4组的CNR高于其他三组(P<0.05),而PI-DL2组在胼胝体压部、脑桥和小脑区域的CNR与NDL组差异无统计学意义(P>0.05)。定性评价方面,两位诊断医师评价一致性良好;PI-DL4组在图像锐利度、噪声控制和整体图像质量方面表现最佳(P<0.05);PI-DL4与PI-DL3组灰白质对比度、病变显示和诊断信心差异无统计学意义(P>0.05);PI-DL2组与NDL组在各项定性评价指标上差异均无统计学意义(P>0.05)。在病变检出方面,DL组检出率高于NDL组,病变大小测量差异无统计学意义(P>0.05)。结论在1.5 T MRI中,将DL重建算法与PI加速技术联合使用,可显著提高T2-FLAIR序列的图像质量和病变显示能力,并有效缩短扫描时间。由于DL等级4可能会导致部分病变边缘对比度降低,因此颅内占位性病变推荐使用DL等级3作为T2-FLAIR序列的最佳重建参数。展开更多
Objective： To study the expression of osteosarcoma metastasis associated gene using a cDNA microarray, and screen new candidate genes related＇to the development, progress and osteosarcoma metastasis. Methods： Total...Objective： To study the expression of osteosarcoma metastasis associated gene using a cDNA microarray, and screen new candidate genes related＇to the development, progress and osteosarcoma metastasis. Methods： Total RNA of a low metastatic osteosarcoma and a high metastatic osteosarcoma （M6 and M8 cell lines, respectively） was extracted, purified to mRNA and then reverse transcribed to cDNA. M6 was used as the experimental group and M8 as the control group, and the gene expression of cells from both of these two sublines was investigated using cDNA microarrays containig 8064 cDNA clones. The cDNA of M6 was labeled with cy3 and the cDNA of M8 was labeled with cyS. The two sublines were hybridized with the cDNA microarray. The hybridization signals were scanned with a Generation HI array scanner and analyzed by Imagequant 5.0 software. Results： There were 330 differentially expressed genes between M6 and M8. In the M6 subline,152 genes were up-regulated and 178 genes were down-regulated compared to the M8 subline. These genes could be classified according to their function. Cell growth-related genes that were down-regulated included CCNG1, CDC2, APC10,and RPA3, while expression of the tumor suppressor genes, CDKN1A and CDKN2D, was up-regulated. Other genes that were differentially expressed included those that have been implicated in the regulation of signal transduction, metabolism and apoptosis. Conclusion： This study exploits a cDNA microarray approach to identifying genes that may be associated with metastasis. The gene expression profiles of osteosarcoma cell lines is a potentially important index in the search of new candidate genes related to tumor occurrence, development and metastasis.展开更多
文摘目的基于1.5 T MRI,比较常规T2液体衰减反转恢复(T2-fluid attenuated inversion recovery,T2-FLAIR)序列传统重建和快速T2-FLAIR深度学习(deep learning,DL)重建在颅内占位性病变患者的图像质量与临床价值,并探究最佳DL重建参数。材料与方法前瞻性纳入颅内占位性病变患者104例,分别采集常规T2-FLAIR和快速T2-FLAIR[并行采集(parallel imaging,PI)加速因子2]。常规T2-FLAIR采用传统重建,记为NDL组;快速T2-FLAIR选择DL重建等级2、3和4,记为PI-DL2、PI-DL3和PI-DL4组。由两位医师采用盲法对四组图像进行定量评价和定性评价,并记录病变大小和数量。定量评价包括信噪比(signal-to-noise ratio,SNR)和对比噪声比(contrast-to-noise ratio,CNR);定性评价包括图像锐利度、噪声、灰白质对比度、伪影、病变显示、诊断信心和整体图像质量。结果常规T2-FLAIR扫描时间为2 min 8 s,快速T2-FLAIR扫描时间为1 min 20 s,时间缩短约37.5%。定量分析显示,与NDL组相比,各DL重建组(等级2、3、4)的SNR均有提高,且随DL等级增加而提高(P<0.05);PI-DL4组的CNR高于其他三组(P<0.05),而PI-DL2组在胼胝体压部、脑桥和小脑区域的CNR与NDL组差异无统计学意义(P>0.05)。定性评价方面,两位诊断医师评价一致性良好;PI-DL4组在图像锐利度、噪声控制和整体图像质量方面表现最佳(P<0.05);PI-DL4与PI-DL3组灰白质对比度、病变显示和诊断信心差异无统计学意义(P>0.05);PI-DL2组与NDL组在各项定性评价指标上差异均无统计学意义(P>0.05)。在病变检出方面,DL组检出率高于NDL组,病变大小测量差异无统计学意义(P>0.05)。结论在1.5 T MRI中,将DL重建算法与PI加速技术联合使用,可显著提高T2-FLAIR序列的图像质量和病变显示能力,并有效缩短扫描时间。由于DL等级4可能会导致部分病变边缘对比度降低,因此颅内占位性病变推荐使用DL等级3作为T2-FLAIR序列的最佳重建参数。
文摘Objective： To study the expression of osteosarcoma metastasis associated gene using a cDNA microarray, and screen new candidate genes related＇to the development, progress and osteosarcoma metastasis. Methods： Total RNA of a low metastatic osteosarcoma and a high metastatic osteosarcoma （M6 and M8 cell lines, respectively） was extracted, purified to mRNA and then reverse transcribed to cDNA. M6 was used as the experimental group and M8 as the control group, and the gene expression of cells from both of these two sublines was investigated using cDNA microarrays containig 8064 cDNA clones. The cDNA of M6 was labeled with cy3 and the cDNA of M8 was labeled with cyS. The two sublines were hybridized with the cDNA microarray. The hybridization signals were scanned with a Generation HI array scanner and analyzed by Imagequant 5.0 software. Results： There were 330 differentially expressed genes between M6 and M8. In the M6 subline,152 genes were up-regulated and 178 genes were down-regulated compared to the M8 subline. These genes could be classified according to their function. Cell growth-related genes that were down-regulated included CCNG1, CDC2, APC10,and RPA3, while expression of the tumor suppressor genes, CDKN1A and CDKN2D, was up-regulated. Other genes that were differentially expressed included those that have been implicated in the regulation of signal transduction, metabolism and apoptosis. Conclusion： This study exploits a cDNA microarray approach to identifying genes that may be associated with metastasis. The gene expression profiles of osteosarcoma cell lines is a potentially important index in the search of new candidate genes related to tumor occurrence, development and metastasis.
