瑞秋是由罗莎莉 b ·海特研究生奖学金和美国军团辅助奖学金资助的。卡洛斯卡德纳斯已由伊尔克什坦博士在生物医学科学研究金的资助。野生山羊的发展由 R03 CA178495 的资助。

1. 安装山羊
注意: 要安装源代码版本, 请转到步骤1.1。或者, 要安装 stand-alone 版本, 请转到步骤1.2。
<ol><li>
源代码版本
	<ol>
<li>转到野山羊源代码版本网站3。下载 "IBEX_Source. zip" 和 "How_to_use pdf" 文件。在 "How_to_use pdf" 文件中查找必备使用最新的野山羊版本。 注意: 野山羊只在32位和64位 Matlab 版本中分别工作2011a 和2014b。</li>
		<li>下载完成后, 解压 "IBEX_Source. zip", 并将 \n IBEX_Source 文件夹添加到本地路径。 注意: 有关如何添加路径的说明, 可在 "添加路径"26的 "软件引用" 页上找到。</li>
		<li>在命令窗口中键入 "IBEXMain" 以启动野山羊。</li>
	</ol>
</li>
	<li>
独立版本
	<ol>
<li>去 stand-alone 版本网站4。如果第一次安装野山羊, 请转到 "依赖性" 文件夹并下载/安装 "MCRInstaller.exe"、"vc2005redist_x86_new" 和 "vc2012redist_x86 .exe" 文件。从主页, 下载 "IBEX.exe", "野山羊", "野山羊" 和 "How_to_use. pdf" 文件到同一个文件夹。查看 "How_to_use pdf" 文件, 必备使用最新的野山羊版本。</li>
		<li>双击 "IBEX.exe" 以启动野山羊。</li>
	</ol>
</li>
</ol>2. 设置位置
注意: 图像被导入到这个集合位置, 并且这项研究的数据也存储在这里。在重新时, 野山羊使用最以前应用的位置作为默认位置。
<ol><li>单击 "位置" 图标 (图 1)。</li>
	<li>选择用户或双击 "新用户" 以创建新的用户文件夹。如果创建新的用户文件夹, 请在新窗口中输入名称, 然后单击 "确定" 按钮。</li>
	<li>选择所选用户的站点或双击 "新建站点" 以创建新的站点文件夹。如果创建新的用户文件夹, 请在新窗口中输入名称, 然后单击 "确定" 按钮。</li>
	<li>单击底部的 "确定" 按钮。</li>
</ol>3. 导入图像
<ol><li>单击 "导入" 图标 (图 1)。</li>
	<li>通过单击格式的名称, 选择 DICOM 或 Pinnacle9 (顶峰文件版本 9) 格式。要导入 DICOM 图像, 可进入步骤3.3.1。导入 Pinnacle9 文件转到步骤3.3.2。</li>
	<li>单击 "下一步" 按钮。<ol>
<li>导入 DICOM 图像 注意: 要单独导入患者, 请到步骤3.3.1.1。同时导入多个患者去3.3.1.2。<ol>
<li>通过目录选择病人文件夹, 逐个导入患者。<ol>
<li>单击列出的目录旁边的 "..." 按钮, 选择包含 dicom 图像和辐射结构的患者文件夹 (可选)。 注意: 尽管使用患者文件导入结构集只是可选的, 但强烈建议您这样做。野山羊在它里面有一个轮廓平台, 但是对于大型数据集来说这有点不切实际。野山羊可以容纳从商业软件平台的结构集, 这是熟悉的轮廓工具的辐射肿瘤学家, 这使得工作流程更容易。这通常是如何在研究中使用野山羊。</li>
				<li>单击左下角的 "导入" 按钮一旦野山羊完成了 DICOM 文件的搜索, 患者和详细信息框将被填充。</li>
				<li>对每个病人重复步骤 3.3. 1.1. 1-2。</li>
				<li>在所有患者导入后, 单击 "退出" 按钮。</li>
			</ol>
</li>
			<li>通过检查右上角的 "批处理文件夹" 框, 立即导入所有患者。 注: 所有患者必须有一个单独的文件夹, 其中包含各自的 DICOM 文件。<ol>
<li>单击列出的目录旁边的 "..." 按钮以选择包含所有患者文件夹的文件夹, 其相应的 dicom 图像和辐射结构设置了 dicom 文件 (可选)。</li>
				<li>单击 "导入" 按钮。患者将按顺序导入。在所有患者导入后, 单击 "退出" 按钮。</li>
			</ol>
</li>
		</ol>
</li>
		<li>顶点文件<ol>
<li>在 "\n 进出口 \n ImportModule \ Pinnacle9 \ Pinnacle9ImportMain. INI" 文件中配置 "顶级主机和数据库"。在 "导入模块" 框中选择 "顶点" 之前, 单击 "配置" 按钮。</li>
			<li>选择合适的顶点主机和数据库。</li>
			<li>输入患者的林、姓氏或名字, 然后单击窗口中的其他位置。</li>
			<li>单击 "查询" 按钮。</li>
			<li>在新窗口中选择病人, 然后单击 "转移" 按钮。</li>
			<li>单击新窗口中该患者所需图像集的框, 然后单击 "确定" 按钮。</li>
			<li>在标记为 "成功导入数据" 的新窗口中单击 "确定" 按钮。</li>
			<li>在所有患者导入后, 单击 "退出" 按钮。</li>
		</ol>
</li>
	</ol>
</li>
</ol>4. 观看图片和感兴趣的区域 (roi)
<ol><li>单击 "数据" 图标 (图 1)。点击所需的病人文件打开在 "选择病人" 部分的 GUI (上半)。</li>
	<li>单击所需病人文件的图像集, 在 GUI 的 "选择图像集 (s)" 部分打开 (下半部)。单击 "打开" 按钮。</li>
	<li>使用图像下方的箭头滚动图像, 这是显示在轴向, 冠状, 和矢状视图 (从左到右)。</li>
	<li>单击 "缩放" 按钮 (图 2) 可放大图像。在图像平面上绘制一个框以进行缩放。右键单击可缩小。</li>
	<li>通过选择 "交集" 按钮 (图 2) 打开本地化。 注意: 显示的线条显示该切片在每个平面中的位置。使用箭头滚动时, 这些线条会移动。</li>
	<li>单击 "标尺" 按钮 (图 2) 以测量距离。点击并按住鼠标左键测量两个点之间的兴趣。松开鼠标以从显示区域中移除测量值。</li>
	<li>单击 "CT Num" 按钮 (图 2) 以测量体素的值。单击并按住鼠标左键对感兴趣的体素, 该值将弹出在 GUI 的左侧, 在颜色刻度下面。松开鼠标以从显示区域中移除测量值。</li>
	<li>单击 "W/L" 按钮 (图 2) 以窗口/级别显示图像。另一个窗口会弹出。将左侧和右侧拖动到所需区域, 或在新窗口底部的窗口和级别框中手动输入窗口/级别。使用左侧的第一个下拉菜单选择不同的窗口/级别 (图 2): 有8的 CT 和1的 PET。</li>
	<li>使用左侧的第二个下拉菜单选择不同的颜色刻度 (图 2): 有9选项可供选择。</li>
	<li>通过单击 ROI 旁边的框以使其在图像上可见, 或者单击 "所有 roi" 按钮打开所有患者 roi, 可以直观显示 roi。</li>
</ol>5. 编辑 ROI
<ol><li>通过单击左上角或底部中心的 "编辑 roi" 按钮来编辑 roi。通过单击其名称旁边的框来选择要编辑的轮廓。</li>
	<li>使用 "微移轮廓" 按钮 (图 3) 可按已绘制的轮廓来按下或出。调整在 "微移轮廓" 按钮旁边标记为 "D" 的框中使用的圆的大小。</li>
	<li>使用 "剪切轮廓" 按钮 (图 3) 删除整个轮廓切片。单击并按住鼠标左键并在要移除的 ROI 周围绘制一个框。</li>
	<li>绘制轮廓 注意: 转到步骤 5.4.1, 通过连接点绘制轮廓。通过自由手去5.4.2 画轮廓。<ol>
<li>连接点: 使用 "绘制轮廓 (1)" 按钮 (图 3) 在每个切片上绘制轮廓, 通过单击轮廓线周围的点以直线连接。单击所绘制的第一个点, 完成该切片的 ROI。</li>
		<li>自由手: 使用 "绘制轮廓 (2)" 按钮 (图 3) 以自由手绘制轮廓。按住鼠标左键在切片上绘制轮廓。通过返回到绘制 ROI 的开始处, 然后松开鼠标左键, 完成该切片上的轮廓。</li>
	</ol>
</li>
	<li>单击 "插补轮廓" 按钮 (图 3) 可在切片之间插入 roi。</li>
	<li>点击 "复制 roi" 按钮来复制 roi。</li>
	<li>通过选择多个 roi, 然后单击 "合并 ROI" 按钮, 合并 roi。</li>
	<li>单击 "删除 roi" 按钮以删除当前选定的 roi。</li>
	<li>单击 "创建 roi" 以创建新的 roi, 这将导致弹出窗口。输入 ROI 的名称, 然后单击 "确定" 按钮。单击 "保存" 按钮。单击 "退出" 按钮返回患者查看器。</li>
</ol>6. 数据集中的轮廓
<ol><li>查看数据集中的轮廓<ol>
<li>单击 "显示数据集" 按钮以查看数据集中的轮廓。单击要查看的数据集的名称。单击 "打开" 按钮。使用其名称旁边的框选择患者轮廓。单击 "删除"、"移动" 或 "复制" 按钮, 具体取决于该轮廓所需的功能。完成查看数据集后, 关闭数据集窗口。</li>
	</ol>
</li>
	<li>向数据集添加轮廓<ol>
<li>选择要添加到数据集的等高线, 请单击其名称旁边的复选框, 然后单击 "添加到数据集" 按钮。单击数据集的名称以添加轮廓或单击新数据集的 "新建" 按钮。如果选择了 "新建", 请在新窗口中输入数据集的名称。单击 "确定" 按钮。</li>
	</ol>
</li>
	<li>单击 "导入 roi" 按钮导入一个顶点版本 9. 如果在3节中未加载等高线, roi 文件。<ol>
<li>单击 "..." 按钮以选择该文件。单击 "导入" 按钮。</li>
	</ol>
</li>
	<li>单击 "导出 roi" 按钮导出 roi。<ol>
<li>单击 "..." 按钮以选择将在其中放置导出的 roi 的目录。单击为导出的 roi 首选的格式的名称。如果首选, 请输入匿名信息。单击 "导出" 按钮。 注意: 预设不匿名执行。</li>
	</ol>
</li>
	<li>单击 "退出" 按钮返回患者和扫描列表。</li>
</ol>7. 创建功能集
<ol><li>点击 "功能" 图标。</li>
	<li>如果需要, 添加预处理。<ol>
<li>单击 "Step1: 预处理" 下的 "添加" 按钮。从新窗口中的下拉菜单中选择 "预处理" 选项。</li>
		<li>点击 "i" 下的 "段" 来选择预处理参数。单击参数新窗口中 "值" 列下的数字以更改它。键入新参数值, 然后单击 "确定" 按钮。</li>
		<li>单击右上角的 "问号" 按钮, 以描述预处理方法和特定参数的预处理。</li>
		<li>单击 "添加" 按钮。选择预处理步骤, 然后单击 "删除" 按钮以删除不需要的预处理步骤。</li>
	</ol>
</li>
	<li>添加所需的功能。<ol>
<li>从 "步骤 2: 功能类别:" 下拉菜单中选择功能类别。对多个类别重复步骤。 注意: 一次只能添加一种功能类别。如果需要, 可以将预处理和特征类别的不同组合添加到同一功能集。<ol>
<li>选取 "GrayLevelCooccurenceMatrix25" 和 "NeighborIntensityDifference25" 特征类别分别计算2.5D 中的灰度共生矩阵和邻域强度差矩阵, 并通过计算矩阵分别在每个切片上, 然后汇总所有矩阵。</li>
			<li>选取 "GrayLevelCooccurenceMatrix3" 和 "NeighborIntensityDifference3" 特征类别分别计算3D 中的灰度共生矩阵和邻域强度差分矩阵。</li>
		</ol>
</li>
		<li>单击 "i" 在 "段落" 下, 为该预处理选择的参数。单击右上角的 "问号" 按钮, 以描述预处理方法和参数。<ol>
<li>键入新参数值, 然后单击 "确定" 按钮。</li>
		</ol>
</li>
		<li>单击 "测试" 按钮以查看功能类别或特定功能。单击所需功能或类别旁边的按钮。<ol>
<li>选择要查看测试的数据集, 然后单击 "打开" 按钮。选中所选数据集中的患者旁边的复选框, 查看测试, 然后单击 "测试" 按钮。</li>
		</ol>
</li>
		<li>取消选中所选类别的不需要的功能;选择类别后, 将选择所有功能。单击 "功能:" 下的 "Step2: 功能" 下的单词, 取消选中所有功能。</li>
		<li>单击 "添加到功能集" 按钮, 添加所有选定的预处理功能。<ol>
<li>选择要添加功能的功能集, 然后单击 "打开" 按钮, 或单击 "新建" 按钮将功能添加到新功能集。如果创建新的功能集, 请在新窗口中输入功能集的名称, 然后单击 "确定" 按钮。</li>
		</ol>
</li>
	</ol>
</li>
	<li>单击 "显示功能集" 按钮以查看具有相应预处理技术的功能, 以查看功能集中的功能。<ol>
<li>选择要查看的功能集, 然后单击 "打开" 按钮, 或单击 "新建" 按钮以创建新的功能集。如果创建新的功能集, 请在新窗口中输入功能集的名称, 然后单击 "确定" 按钮。</li>
	</ol>
</li>
	<li>单击 "显示数据集" 按钮以查看当前数据集。<ol>
<li>选择要查看的数据集, 然后单击 "打开" 按钮, 或单击 "新建" 按钮以创建新的数据集。如果创建新的数据集, 请在新窗口中输入数据集的名称, 然后单击 "确定" 按钮。</li>
	</ol>
</li>
	<li>单击 "退出" 按钮。</li>
</ol>8. 输出特性
<ol><li>单击 "结果" 图标。单击数据集以运行 "步骤 1: 数据集" 下的功能。单击功能集以在 "步骤 2: 功能集" 下的选定数据上运行。</li>
	<li>单击 "查看数据" 按钮以查看选定的数据集。单击 "查看功能" 按钮可查看所选功能集。</li>
	<li>单击 "计算 & 保存结果" 按钮。输入结果的文件名, 然后单击 "保存" 按钮。</li>
</ol>9. 统计模型的建立
<ol><li>打开保存的结果文件。 注意: 除非在步骤8.4 中另行指定, 否则通常在 C: \n DataIbex \ [步骤2.2 中的选定用户] \ [步骤2.3 中的选定站点] \n 1FeatureResultSet_Result 下。</li>
	<li>使用软件中所需的统计测试或模型构建中的不同选项卡中的数据。 注意: 协议可以在任何时候暂停。但是, 在完成特定图标所需的所有步骤后, 最方便的方法是例如, 完成功能图标下的所有7节。</li>
</ol>

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V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Stage+III+Non-Small+Cell+Lung+Cancer:+Prognostic+Value+of+FDG+PET+Quantitative+Imaging+Features+Combined+with+Clinical+Prognostic+Factors.">Stage III Non-Small Cell Lung Cancer: Prognostic Value of FDG PET Quantitative Imaging Features Combined with Clinical Prognostic Factors.</a> Radiology. 278, 214-222 (2016).</li><li>Fried, D. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Potential+Use+of+(18)F-fluorodeoxyglucose+Positron+Emission+Tomography-Based+Quantitative+Imaging+Features+for+Guiding+Dose+Escalation+in+Stage+III+Non-Small+Cell+Lung+Cancer.">Potential Use of (18)F-fluorodeoxyglucose Positron Emission Tomography-Based Quantitative Imaging Features for Guiding Dose Escalation in Stage III Non-Small Cell Lung Cancer.</a> IJROBP. 94, 368-376 (2016).</li><li>Mackin, D., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Measuring+Computed+Tomography+Scanner+Variability+of+Radiomics+Features.">Measuring Computed Tomography Scanner Variability of Radiomics Features.</a> Invest radiol. 50, 757-765 (2015).</li><li>Yang, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Uncertainty+analysis+of+quantitative+imaging+features+extracted+from+contrast-enhanced+CT+in+lung+tumors.">Uncertainty analysis of quantitative imaging features extracted from contrast-enhanced CT in lung tumors.</a> Comp Med Imaging Graph. 48, 1-8 (2016).</li><li>van Rossum, P. S., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=The+incremental+value+of+subjective+and+quantitative+assessment+of+18F-FDG+PET+for+the+prediction+of+pathologic+complete+response+to+preoperative+chemoradiotherapy+in+esophageal+cancer.">The incremental value of subjective and quantitative assessment of 18F-FDG PET for the prediction of pathologic complete response to preoperative chemoradiotherapy in esophageal cancer.</a> JNM. 57, 691-700 (2016).</li><li>Hunter, L. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=NSCLC+tumor+shrinkage+prediction+using+quantitative+image+features.">NSCLC tumor shrinkage prediction using quantitative image features.</a> Comp Med Imaging Graph. 49, 29-36 (2016).</li><li>Hunter, L. A., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=High+quality+machine-robust+image+features:+identification+in+nonsmall+cell+lung+cancer+computed+tomography+images.">High quality machine-robust image features: identification in nonsmall cell lung cancer computed tomography images.</a> Med Phys. 40, 121916 (2013).</li><li>Fried, D. V., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prognostic+value+and+reproducibility+of+pretreatment+CT+texture+features+in+stage+III+non-small+cell+lung+cancer.">Prognostic value and reproducibility of pretreatment CT texture features in stage III non-small cell lung cancer.</a> IJROBP. 90, 834-842 (2014).</li><li>Gan, J., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=MO-DE-207B-09:+A+Consistent+Test+for+Radiomics+Softwares.">MO-DE-207B-09: A Consistent Test for Radiomics Softwares.</a> Med Phys. 43, 3706-3706 (2016).</li><li>Klawikowski, S., Christian, J., Schott, D., Zhang, M., Li, X. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SU-D-207B-07:+Development+of+a+CT-Radiomics+Based+Early+Response+Prediction+Model+During+Delivery+of+Chemoradiation+Therapy+for+Pancreatic+Cancer.">SU-D-207B-07: Development of a CT-Radiomics Based Early Response Prediction Model During Delivery of Chemoradiation Therapy for Pancreatic Cancer.</a> Med Phys. 43, 3350-3350 (2016).</li><li>Huang, W., Tu, S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=SU-F-R-22:+Malignancy+Classification+for+Small+Pulmonary+Nodules+with+Radiomics+and+Logistic+Regression.">SU-F-R-22: Malignancy Classification for Small Pulmonary Nodules with Radiomics and Logistic Regression.</a> Med Phys. 43, 3377-3378 (2016).</li><li>Hanania, A. N., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Quantitative+imaging+to+evaluate+malignant+potential+of+IPMNs.">Quantitative imaging to evaluate malignant potential of IPMNs.</a> Oncotarget. 7, 85776-85784 (2016).</li><li>Court, L. E., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Computational+resources+for+radiomics.">Computational resources for radiomics.</a> Trans Cancer Res. 5, 340-348 (2016).</li><li>. Matlab Add path Available from: <a target="_blank" href="https://www.mathworks.com/help/matlab/ref/addpath.html">https://www.mathworks.com/help/matlab/ref/addpath.html</a> (2017)</li><li>Zhao, B., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reproducibility+of+radiomics+for+deciphering+tumor+phenotype+with+imaging.">Reproducibility of radiomics for deciphering tumor phenotype with imaging.</a> Sci Rep. 6, 23428 (2016).</li><li>Owens, C., et al. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Reproducibility+and+Robustness+of+Radiomic+Features+Extracted+with+Semi-Automatic+Segmentation+Tools.">Reproducibility and Robustness of Radiomic Features Extracted with Semi-Automatic Segmentation Tools.</a> Med Phys. , (2017).</li><li>Kassner, A., Liu, F., Thornhill, R. E., Tomlinson, G., Mikulis, D. J. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Prediction+of+hemorrhagic+transformation+in+acute+ischemic+stroke+using+texture+analysis+of+postcontrast+T1-weighted+MR+images.">Prediction of hemorrhagic transformation in acute ischemic stroke using texture analysis of postcontrast T1-weighted MR images.</a> JMRI. 30, 933-941 (2009).</li><li>. IBEX Google Forum Available from: <a target="_blank" href="https://groups.google.com/forum/#!forum/ibex_users">https://groups.google.com/forum/#!forum/ibex_users</a> (2017)</li></ol>

作者没有竞争的财务利益透露。

野山羊是医学影像 radiomics 研究的有力工具。迄今为止, 它主要用于放射肿瘤学的目的, 由 MD 安德森 radiomics 组进行的研究。野山羊允许操作的 roi 和计算功能在5主要特征类别。源代码版本的野山羊允许用户设计的应用程序, 是不是已经部分的野山羊, 如灰度级区域矩阵的功能。
roi 的主要步骤包括图像的导入、轮廓的绘制、数据集的 roi 选择以及特征集的创建。精确的轮廓是必要的, 因为特征仅在这些区域中计算, 因此不准确的轮廓将提供不准确的特征值。因此, 在这些特征之间发现的任何关系不准确的轮廓和结果将是虚假的。特征参数的选择也是一个重要的步骤。例如, 更改灰度级共生矩阵的步长大小可能会影响从矩阵计算出的特征。这可能取决于图像类型 (即、MRI、CT 或 PET)、用于调查的站点 (如、NSCLC 与头颈部) 以及研究的目的 (例如, 创建生存模型与将图像特征与基因组连接起来)。应根据物理或生物推理选择特征参数,例如, 是否有一个原因, 即步骤大小为4在共生矩阵中具有生物学相关性？特征参数也可以根据以前的研究结果选择, 它们已经发现某些特征参数与结局或生物表达相关联。野山羊有27预处理模块和132功能可供选择, 同时允许更改每个预处理模块和特征类别的参数, 这使得它成为许多类型的 radiomics 研究的一种适应性工具。
在使用任何软件时, radiomics 的研究有几个普遍的限制。例如, 图像特征已被显示为依赖于图像采集参数, 如像素大小和扫描仪15,27。所有软件的一个限制是, 有许多参数可以为每个功能修改, 默认值可能不适合特定的研究。用户必须保持警惕, 并研究以前使用的参数设置进行类似的研究, 并评估设置的适用性。轮廓的质量和固有的和 intra-observer 的变异性也会影响到特征的计算。欧文斯et al.显示, 使用 auto-contouring 工具28时, 计算出的功能更加健壮。为 radiomics 研究计算的特征是人的工程学特征, 并且可能不充分地传达视觉知觉系统观察的特征。此外, 这些特性在分析结果时可能会与另一个人产生很大的关联。野山羊的一个具体限制是, 当前版本缺乏计算小波特征的可用性;但是, 我们的小组打算在将来的版本中包括这些功能。
有几个可供选择的软件平台可用于计算图像功能25。野山羊的一些优点包括, 它是免费提供的事实, 是很好的文件2, 并允许用户详细控制图像功能的计算。野山羊还会显示经过处理的患者图像 (例如, 在平滑后), 因此用户可以可视化任何图像处理的影响-这很有用, 例如, 当确认预处理未 over-smoothed 图像时。同样, 野山羊也可以导出实际共生矩阵和强度直方图;这在深入了解图像特征时很有用。
野山羊只被用于癌症研究, 主要集中在放射治疗上。然而, 未来的研究可以分支到其他癌症治疗, 甚至在癌症领域之外。例如, Kassner et al.使用 radiomics 来预测急性缺血性中风患者的出血转化29。野山羊也可以用于这种类型的 radiomics 研究。
野山羊也有持续的维护。例如, 由最喜爱的et al.的一项研究发现, 5 特性 (繁忙、粗糙、灰度级不均匀、运行长度不均匀和能量) 强烈依赖于卷, 并更正了它们的公式11。这些更新的公式已经包括在更新的野山羊版本中。此外, 还有一个 google 组30 , 用户会发布其他用户回答的问题。这种持续改进的山羊除了目前的能力, 山羊和它的可用性, 使它成为 radiomics 研究的主要来源。

在医学上, 患者疾病诊断通常包含大量的诊断检查, 如 x 光、超声、计算机断层扫描 (CT)、磁共振成像 (MRI) 和正电子发射断层成像 (PET), 以协助确定病人护理的过程。虽然医生使用这些图像定性地评估病人的诊断, 可能有额外的数量特征, 可以提取, 以指导病人的护理。其基本原理是, 这些特征可能代表了在宏观尺度1上表达的蛋白质和基因组模式。将这些量化信息与当前的临床信息 (如、患者的人口统计数据) 相结合, 可能会允许更多的个性化病人护理。这是 radiomics 背后的理论: 体素水平上的图像特征分析。特征通常分为5大类: 灰度共生矩阵、灰度级运行长度矩阵、邻域强度差分矩阵、直方图和形状。
成像生物标记资源管理器 (野山羊) 是一个开源工具, 用于 radiomics 工作2。图形用户界面 (GUI) 是在 MD 安德森癌症中心开发的, 目的是促进定量特征的提取和计算, 以帮助癌症治疗中的决策。在线可以使用源代码3和 stand-alone4版本。野山羊计算医疗 radiomics 中使用的5种最常见的功能类别, 并可为每个功能类别设置参数。类别有: 灰度共生矩阵5、灰度级运行长度矩阵6、7、强度、邻域强度差矩阵8和形状。由于野山羊是开源的, 它允许跨机构统一的特征提取结果, 以方便地比较不同的 radiomics 研究。在张et al.的初始文件中描述了野山羊中的所有特征2
这份手稿的目的是提供指导, 如何使用野山羊和演示其应用通过同行评议发表的研究从 MD 安德森 radiomics 集团。自2015年向公众发布以来, 野山羊已被用来计算 CT、PET 和 MRI 扫描图像的特征, MD 安德森 radiomics 组, 典型的调查功能, 以改善临床生存模式9,10, 11,12,13,14,15,16,17,18,19,20并由外部机构21、22、23、24。在法院et al.中可以找到可用于 radiomics 研究中未包括在野山羊中的步骤的其他软件工具指南25
对山羊的工作流程的一般介绍将帮助组织数据正确之前开始 radiomics 项目利用野山羊。如果导入 dicom 图像, 则要求每个患者都有自己的 dicom 图像文件夹。DICOM 辐射结构集是可选的, 包括在病人的文件夹, 但建议, 而不是使用的轮廓平台在山羊。为了帮助所有病人导入一个特定的研究, 所有的病人文件夹都可以放在一个文件夹中, 以便所有的数据都可以只用一个步骤导入到野山羊中。如果从顶点导入病人, 最好是将患者计划的结构设置好。由于患者可能有多个图像集和计划在顶峰, 最好是知道哪个图像集和计划是正确的, 然后再导入。如果计算时间是一个问题, 减少病人的图像切片数量可以大大缩短时间。例如, 如果只有肝脏是研究的兴趣, 但病人有全身 CT 扫描, 减少 dicom 切片只在感兴趣的区域范围内可以缩短计算时间 (例如, 将 dicom 从300切片减少到50切片可以采取 1/6 t时间)。有不同的工具可用于执行此切片减少, 从手动到半自动。

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			<td height="42" style="height:42px;width:216px;">Excel</td>
			<td style="width:71px;">Microsoft Office</td>
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			<td style="width:167px;">Any version of excel should work.</td>
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			<td height="42" style="height:42px;width:216px;">Matlab</td>
			<td style="width:71px;">MathWorks</td>
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			<td>Only use IBEX on 32 bit Matlab 2011a or 64 bit Matlab 2014b.</td>
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guidelines and experience using imaging biomarker explorer (ibex) for radiomics

成像生物标志物探险家 (野山羊) 是一个开源工具的医学成像 radiomics 工作。本文的目的是描述如何使用山羊的图形用户界面 (GUI), 并说明如何在临床研究中使用了野山羊的计算功能。野山羊允许输入 dicom 图像与 dicom 辐射治疗结构文件或顶点文件。一旦图像被导入, 在数据选择 GUI 中, 野山羊就有工具来操纵图像的查看, 测量体素值和距离, 并创建和编辑轮廓。野山羊有27预处理和132功能选择来设计特征集。每个预处理和特征类别都有可以更改的参数。从野山羊的输出是一个电子表格, 包含: 1) 每一个功能, 从功能集计算每个轮廓的数据集, 2) 的图像信息的每个轮廓在一个数据集, 和 3) 的预处理和功能与他们所选择的使用摘要参数.从野山羊身上计算的特征已经用于研究不同成像条件和生存模式下的特征的变异性, 以改善目前的临床模型。

来自野山羊的输出是一个电子表格 (参见图 4), 其中包含3选项卡。"结果" 选项卡包含数据集中每个 ROI 的功能值 (图 4A)。"数据信息" 选项卡包含有关从数据集中的每个 ROI 中获取的图像的信息 (图 4B)。"功能信息" 选项卡包含与为特征类别选择的参数和用于该功能类别的预处理 (图 4C) 一起使用的功能的综合列表。
从医学图像中计算出的野山羊特征已经在几个方面得到了应用。亨特et al.使用早期版本的野山羊来识别健壮的图像功能19。最喜爱的et al.调查了在不同呼吸阶段、峰值管电压和管流9中收集的 4D CT 胸部扫描的 radiomics 特征的不确定性。本研究发现, 当管电压和电流变化时, intra-patient 变异小于 inter-patient 变化, 使得这些因素微不足道。然后用野山羊10评价了锥束 CT (总站) 图像的再现性。在这项研究中, 从肺部总站图像计算的特征被发现是可重现的, 当相同的协议和制造商只有当呼吸运动是小的使用。随后对图像预处理对特征值的影响进行了评估。研究表明, 39 的55特性中, 有至少一个预处理技术, 通过 Cox 比例风险模型, 对整体生存造成了显著的分层, 表明每个功能11。用野山羊对灌注 CT 图像特征的不确定度也进行了评价。杨et al.显示, radiomics 特征不依赖于对比度管理和 CT 扫描之间的时间, 并且86.9% 的特征是可重现的, 闭会稳定性一致性相关系数大于或等于为 0.916。最后, 设计了一个幻像来测试功能子集15的 inter-scanner 可变性。质地强度被发现是最一致的特点, 而忙碌被发现变化最。
radiomics 的特点, 也经常用于模型建设, 通常是看整体生存, 局部地区控制, 并从远处转移的自由。油炸et al.确定 8 radiomics 功能从非小细胞肺癌 (NSCLC) 患者的 CT 扫描, 当实施成 Cox 比例风险模型的整体生存, 区域控制, 远转移明显改进的卡普兰-Meier 分层时, 与只使用临床数据的模型相比,20。同样, 最喜爱的et al.发现了在生存曲线上改善患者分层的 radiomics 特征12。他们的研究使用每周的 CT 图像和计算的肺部 radiomics 特征的变化。模型中的计算特征有四种不同的预处理方法: (1) 阈值、(2) 门限和位深度, (3) 阈值和平滑, (4) 阈值, 位深度, 平滑;在 Cox 比例风险模型测试之前, 分别选择了最佳的预处理方法。亨特et al.还表明, radiomics 功能可以预测肺癌患者的肿瘤萎缩, 同时探索不同阈值和位深度重新价值18。
利用野山羊对 PET 图像的 radiomics 特征及其预测价值进行了研究。油炸的et al.通过将 suv 舍入到最接近的整数来缩放标准化的摄取值 (suv), 然后从其余的13中减去该 ROI 的最小 SUV。在仅包括常规临床因素的条件下, 与模型相比, 能量和坚固性在统计学上改善了整体生存模式。这两个 radiomics 特征也被发现能够识别那些从剂量提升中受益或受到损害的患者的子群14。同样, van 罗森et al.发现, 当将 radiomics 特征纳入临床模型17时, 病理完全反应的临床预测模型的 c 索引增加。
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/57132/57132fig1.jpg"> 图 1: 山羊主主页.与每个部分的图标的野山羊的主页。其中的每一节都在 2-6 节中介绍。<a href="//cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57132/57132fig1large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/57132/57132fig2.jpg"> 图 2: 数据选择窗口.GUI 窗口用于数据选择操作。该窗口带有按钮来改变图像的外观, 如步骤 4.4-4.10 所述。<a href="//cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57132/57132fig2large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/57132/57132fig3.jpg"> 图 3: ROI 编辑器窗口. GUI 窗口用于 ROI 操作。该窗口带有相同的按钮, 用于更改数据选择中的外观以及更改 roi 的按钮。5节描述了 ROI 操作。<a href="//cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57132/57132fig3large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/57132/57132fig4.jpg"> 图 4: 野山羊结果工作表野山羊在工作表中输出三页信息。第一页 (A) 包含每个 ROI 的功能值, 第二页 (B) 包含有关绘制 roi 的图像的信息, 第三页 (C) 包含有关所使用的功能和预处理的信息。这个数字的输出是从一个幻影研究, 其中的特点是使用北海平滑和8位深度缩放, 显示在面板 C, 列 E, 行5和6。<a href="//cloudflare-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/57132/57132fig4large.jpg" target="_blank">请单击此处查看此图的较大版本.</a>

Watch this Scientific Journal Video about Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics at JoVE.com

Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

我们描述了一种用于医学影像 radiomics 研究的开源工具, 以及如何使用这个工具。此外, 一些已出版的作品, 已使用野山羊的不确定性分析和模型建设展示。

使用成像生物标志物探险家 (野山羊) Radiomics 的指南和经验

Imaging Biomarker Explorer (IBEX) is an open-source tool for medical imaging radiomics work. The purpose of this paper is to describe how to use IBEX's ...

guidelines-experience-using-imaging-biomarker-explorer-ibex-for

Nanyang Technological University

Research

JoVE Journal

Engineering

Guidelines and Experience Using Imaging Biomarker Explorer (IBEX) for Radiomics

13.1K 次观看.  The University of Texas MD Anderson Cancer Center. 我们描述了一种用于医学影像 radiomics 研究的开源工具, 以及如何使用这个工具。此外, 一些已出版的作品, 已使用野山羊的不确定性分析和模型建设展示。

视频: Guidelines and Experience Using Imaging Biomarker Explorer IBEX for Radiomics

Compact Quantum Dots for Single-molecule Imaging

Single-molecule imaging is an important tool for understanding the mechanisms of biomolecular function and for visualizing the spatial and temporal heterogeneity of molecular behaviors that underlie cellular biology 1-4. To image an individual molecule of interest, it is typically conjugated to a fluorescent tag (dye, protein, bead, or quantum dot) and observed with epifluorescence or total internal reflection fluorescence (TIRF) microscopy. While dyes and fluorescent proteins have been the mainstay of fluorescence imaging for decades, their fluorescence is unstable under high photon fluxes necessary to observe individual molecules, yielding only a few seconds of observation before complete loss of signal. Latex beads and dye-labeled beads provide improved signal stability but at the expense of drastically larger hydrodynamic size, which can deleteriously alter the diffusion and behavior of the molecule under study.
Quantum dots (QDs) offer a balance between these two problematic regimes. These nanoparticles are composed of semiconductor materials and can be engineered with a hydrodynamically compact size with exceptional resistance to photodegradation 5. Thus in recent years QDs have been instrumental in enabling long-term observation of complex macromolecular behavior on the single molecule level. However these particles have still been found to exhibit impaired diffusion in crowded molecular environments such as the cellular cytoplasm and the neuronal synaptic cleft, where their sizes are still too large 4,6,7.
Recently we have engineered the cores and surface coatings of QDs for minimized hydrodynamic size, while balancing offsets to colloidal stability, photostability, brightness, and nonspecific binding that have hindered the utility of compact QDs in the past 8,9. The goal of this article is to demonstrate the synthesis, modification, and characterization of these optimized nanocrystals, composed of an alloyed HgxCd1-xSe core coated with an insulating CdyZn1-yS shell, further coated with a multidentate polymer ligand modified with short polyethylene glycol (PEG) chains (Figure 1). Compared with conventional CdSe nanocrystals, HgxCd1-xSe alloys offer greater quantum yields of fluorescence, fluorescence at red and near-infrared wavelengths for enhanced signal-to-noise in cells, and excitation at non-cytotoxic visible wavelengths. Multidentate polymer coatings bind to the nanocrystal surface in a closed and flat conformation to minimize hydrodynamic size, and PEG neutralizes the surface charge to minimize nonspecific binding to cells and biomolecules. The end result is a brightly fluorescent nanocrystal with emission between 550-800 nm and a total hydrodynamic size near 12 nm. This is in the same size range as many soluble globular proteins in cells, and substantially smaller than conventional PEGylated QDs (25-35 nm).

We describe the preparation of colloidal quantum dots with minimized hydrodynamic size for single-molecule fluorescence imaging. Compared to conventional quantum dots, these nanoparticles are similar in size to globular proteins and are optimized for single-molecule brightness, stability against photodegradation, and resistance to nonspecific binding to proteins and cells.

紧凑型量子点的单分子成像

Single-molecule imaging is an important tool for  understanding the mechanisms of biomolecular function and for visualizing the  spatial and temporal ...

科研

工程学

Spatial Separation of Molecular Conformers and Clusters

Gas-phase molecular physics and physical chemistry experiments commonly use supersonic expansions through pulsed valves for the production of cold molecular beams. However, these beams often contain multiple conformers and clusters, even at low rotational temperatures. We present an experimental methodology that allows the spatial separation of these constituent parts of a molecular beam expansion. Using an electric deflector the beam is separated by its mass-to-dipole moment ratio, analogous to a bender or an electric sector mass spectrometer spatially dispersing charged molecules on the basis of their mass-to-charge ratio. This deflector exploits the Stark effect in an inhomogeneous electric field and allows the separation of individual species of polar neutral molecules and clusters. It furthermore allows the selection of the coldest part of a molecular beam, as low-energy rotational quantum states generally experience the largest deflection. Different structural isomers (conformers) of a species can be separated due to the different arrangement of functional groups, which leads to distinct dipole moments. These are exploited by the electrostatic deflector for the production of a conformationally pure sample from a molecular beam. Similarly, specific cluster stoichiometries can be selected, as the mass and dipole moment of a given cluster depends on the degree of solvation around the parent molecule. This allows experiments on specific cluster sizes and structures, enabling the systematic study of solvation of neutral molecules.

We present a technique that allows the spatial separation of different conformers or clusters present in a molecular beam. An electrostatic deflector is used to separate species by their mass-to-dipole moment ratio, leading to the production of gas-phase ensembles of a single conformer or cluster stoichiometry.

分子构象器和星团的空间分离

Gas-phase molecular physics and physical chemistry experiments commonly use supersonic expansions through pulsed valves for the production of cold ...

Reservoir Condition Pore-scale Imaging of Multiple Fluid Phases Using X-ray Microtomography

X-ray microtomography was used to image, at a resolution of 6.6 &#181;m, the pore-scale arrangement of residual carbon dioxide ganglia in the pore-space of a carbonate rock at pressures and temperatures representative of typical formations used for CO2 storage. Chemical equilibrium between the CO2, brine and rock phases was maintained using a high pressure high temperature reactor, replicating conditions far away from the injection site. Fluid flow was controlled using high pressure high temperature syringe pumps. To maintain representative in-situ conditions within the micro-CT scanner a carbon fiber high pressure micro-CT coreholder was used. Diffusive CO2 exchange across the confining sleeve from the pore-space of the rock to the confining fluid was prevented by surrounding the core with a triple wrap of aluminum foil. Reconstructed brine contrast was modeled using a polychromatic x-ray source, and brine composition was chosen to maximize the three phase contrast between the two fluids and the rock. Flexible flow lines were used to reduce forces on the sample during image acquisition, potentially causing unwanted sample motion, a major shortcoming in previous techniques. An internal thermocouple, placed directly adjacent to the rock core, coupled with an external flexible heating wrap and a PID controller was used to maintain a constant temperature within the flow cell. Substantial amounts of CO2 were trapped, with a residual saturation of 0.203 &#177; 0.013, and the sizes of larger volume ganglia obey power law distributions, consistent with percolation theory.

We present a methodology for the imaging of multiple fluid phases at reservoir conditions by the use of x-ray microtomography. We show some representative results of capillary trapping in a carbonate rock sample.

多重流体相储层孔隙条件级成像使用X射线微断层

X-ray microtomography was used to image, at a resolution of 6.6 &#181;m, the pore-scale arrangement of residual carbon dioxide ganglia in the pore-space ...

Electron Channeling Contrast Imaging for Rapid III-V Heteroepitaxial Characterization

Misfit dislocations in heteroepitaxial layers of GaP grown on Si(001) substrates are characterized through use of electron channeling contrast imaging (ECCI) in a scanning electron microscope (SEM). ECCI allows for imaging of defects and crystallographic features under specific diffraction conditions, similar to that possible via plan-view transmission electron microscopy (PV-TEM). A particular advantage of the ECCI technique is that it requires little to no sample preparation, and indeed can use large area, as-produced samples, making it a considerably higher throughput characterization method than TEM. Similar to TEM, different diffraction conditions can be obtained with ECCI by tilting and rotating the sample in the SEM. This capability enables the selective imaging of specific defects, such as misfit dislocations at the GaP/Si interface, with high contrast levels, which are determined by the standard invisibility criteria. An example application of this technique is described wherein ECCI imaging is used to determine the critical thickness for dislocation nucleation for GaP-on-Si by imaging a range of samples with various GaP epilayer thicknesses. Examples of ECCI micrographs of additional defect types, including threading dislocations and a stacking fault, are provided as demonstration of its broad, TEM-like applicability. Ultimately, the combination of TEM-like capabilities &#8211; high spatial resolution and richness of microstructural data &#8211; with the convenience and speed of SEM, position ECCI as a powerful tool for the rapid characterization of crystalline materials.

The use of electron channeling contrast imaging in a scanning electron microscope to characterize defects in III-V/Si heteroexpitaxial thin films is described. This method yields similar results to plan-view transmission electron microscopy, but in significantly less time due to lack of required sample preparation.

电子通道造影的快速III-V异质特征

Misfit dislocations in heteroepitaxial layers of GaP grown on Si(001) substrates are characterized through use of electron channeling contrast imaging ...

Use of a Multi-compartment Dynamic Single Enzyme Phantom for Studies of Hyperpolarized Magnetic Resonance Agents

Imaging of hyperpolarized substrates by magnetic resonance shows great clinical promise for assessment of critical biochemical processes in real time. Due to fundamental constraints imposed by the hyperpolarized state, exotic imaging and reconstruction techniques are commonly used. A practical system for characterization of dynamic, multi-spectral imaging methods is critically needed. Such a system must reproducibly recapitulate the relevant chemical dynamics of normal and pathological tissues. The most widely utilized substrate to date is hyperpolarized [1-13C]-pyruvate for assessment of cancer metabolism. We describe an enzyme-based phantom system that mediates the conversion of pyruvate to lactate. The reaction is initiated by injection of the hyperpolarized agent into multiple chambers within the phantom, each of which contains varying concentrations of reagents that control the reaction rate. Multiple compartments are necessary to ensure that imaging sequences faithfully capture the spatial and metabolic heterogeneity of tissue. This system will aid the development and validation of advanced imaging strategies by providing chemical dynamics that are not available from conventional phantoms, as well as control and reproducibility that is not possible in vivo.

A multi-compartment dynamic phantom is used to simulate some biology of interest for metabolic studies using hyperpolarized magnet resonance agents.

一个多腔动态单一酶幻影超极化磁共振剂研究的用途

Imaging of hyperpolarized substrates by magnetic resonance shows great clinical promise for assessment of critical biochemical processes in real time. Due ...

Dynamic Pore-scale Reservoir-condition Imaging of Reaction in Carbonates Using Synchrotron Fast Tomography

Underground storage permanence is a major concern for carbon capture and storage. Pumping CO2 into carbonate reservoirs has the potential to dissolve geologic seals and allow CO2 to escape. However, the dissolution processes at reservoir conditions are poorly understood. Thus, time-resolved experiments are needed to observe and predict the nature and rate of dissolution at the pore scale. Synchrotron fast tomography is a method of taking high-resolution time-resolved images of complex pore structures much more quickly than traditional &#181;-CT. The Diamond Lightsource Pink Beam was used to dynamically image dissolution of limestone in the presence of CO2-saturated brine at reservoir conditions. 100 scans were taken at a 6.1 &#181;m resolution over a period of 2 hours. The images were segmented and the porosity and permeability were measured using image analysis and network extraction. Porosity increased uniformly along the length of the sample; however, the rate of increase of both porosity and permeability slowed at later times.

Synchrotron fast tomography was used to dynamically image dissolution of limestone in the presence of CO2-saturated brine at reservoir conditions. 100 scans were taken at a 6.1 &#181;m resolution over a period of 2 h.

用同步快速反应层析成像动态孔隙规模油藏条件下成像碳酸盐

Underground storage permanence is a major concern for carbon capture and storage. Pumping CO2 into carbonate reservoirs has the potential to dissolve ...

High-resolution Thermal Micro-imaging Using Europium Chelate Luminescent Coatings

Micro-electronic devices often undergo significant self-heating when biased to their typical operating conditions. This paper describes a convenient optical micro-imaging technique which can be used to map and quantify such behavior. Europium thenoyltrifluoroacetonate (EuTFC) has a 612 nm luminescence line whose activation efficiency drops strongly with increasing temperature, due to T-dependent interactions between the Eu3+ ion and the organic chelating compound. This material may be readily coated on to a sample surface by thermal sublimation in vacuum. When the coating is excited with ultraviolet light (337 nm) an optical micro-image of the 612 nm luminescent response can be converted directly into a map of the sample surface temperature. This technique offers spatial resolution limited only by the microscope optics (about 1 micron) and time resolution limited by the speed of the camera employed. It offers the additional advantages of only requiring comparatively simple and non-specialized equipment, and giving a quantitative probe of sample temperature.

Europium thenoyltrifluoroacetonate (EuTFC) has an optical luminescence line at 612 nm, whose activation efficiency decreases strongly with temperature. If a sample coated with a thin film of this material is micro-imaged, the 612 nm luminescent response intensity may be converted into a direct map of sample surface temperature.

高分辨率热显微成像采用铕螯合物的荧光涂料

Micro-electronic devices often undergo significant self-heating when biased to their typical operating conditions. This paper describes a convenient ...

Advanced Compositional Analysis of Nanoparticle-polymer Composites Using Direct Fluorescence Imaging

The fabrication of polymer-nanoparticle composites is extremely important in the development of many functional materials. Identifying the precise composition of these materials is essential, especially in the design of surface catalysts, where the surface concentration of the active component determines the activity of the material. Antimicrobial materials which utilize nanoparticles are a particular focus of this technology. Recently swell encapsulation has emerged as a technique for inserting antimicrobial nanoparticles into a host polymer matrix. Swell encapsulation provides the advantage of localizing the incorporation to the external surfaces of materials, which act as the active sites of these materials. However, quantification of this nanoparticle uptake is challenging. Previous studies explore the link between antimicrobial activity and surface concentration of the active component, but this is not directly visualized. Here we show a reliable method to monitor the incorporation of nanoparticles into a polymer host matrix via swell encapsulation. We show that the surface concentration of CdSe/ZnS nanoparticles can be accurately visualized through cross-sectional fluorescence imaging. Using this method, we can quantify the uptake of nanoparticles via swell encapsulation and measure the surface concentration of encapsulated particles, which is key in optimizing the activity of functional materials.

Here we present a reliable method to monitor the incorporation of nanoparticles into a polymer host matrix via swell encapsulation. We show that the surface concentration of cadmium selenide quantum dots can be accurately visualized through cross-sectional fluorescence imaging.

纳米粒子 - 聚合物复合材料使用直接荧光成像的先进成分分析

The fabrication of polymer-nanoparticle composites is extremely important in the development of many functional materials. Identifying the precise ...

Nanostructured Ag-zeolite Composites as Luminescence-based Humidity Sensors

Small silver clusters confined inside zeolite matrices have recently emerged as a novel type of highly luminescent materials. Their emission has high external quantum efficiencies (EQE) and spans the whole visible spectrum. It has been recently reported that the UV excited luminescence of partially Li-exchanged sodium Linde type A zeolites [LTA(Na)] containing luminescent silver clusters can be controlled by adjusting the water content of the zeolite. These samples showed a dynamic change in their emission color from blue to green and yellow upon an increase of the hydration level of the zeolite, showing the great potential that these materials can have as luminescence-based humidity sensors at the macro and micro scale. Here, we describe the detailed procedure to fabricate a humidity sensor prototype using silver-exchanged zeolite composites. The sensor is produced by suspending the luminescent Ag-zeolites in an aqueous solution of polyethylenimine (PEI) to subsequently deposit a film of the material onto a quartz plate. The coated plate is subjected to several hydration/dehydration cycles to show the functionality of the sensing film.

A protocol for the synthesis of moisture-responsive luminescent Ag-zeolite composites is described in this report.

纳米银沸石复合材料作为基于发光的湿度传感器

Small silver clusters confined inside zeolite matrices have recently emerged as a novel type of highly luminescent materials. Their emission has high ...

Simultaneous Measurement of Turbulence and Particle Kinematics Using Flow Imaging Techniques

Numerous problems in scientific and engineering fields involve understanding the kinematics of particles in turbulent flows, such as contaminants, marine micro-organisms, and/or sediments in the ocean, or fluidized bed reactors and combustion processes in engineered systems. In order to study the effect of turbulence on the kinematics of particles in such flows, simultaneous measurements of both the flow and particle kinematics are required. Non-intrusive, optical flow measurement techniques for measuring turbulence, or for tracking particles, exist but measuring both simultaneously can be challenging due to interference between the techniques. The method presented herein provides a low cost and relatively simple method to make simultaneous measurements of the flow and particle kinematics. A cross section of the flow is measured using a particle image velocimetry (PIV) technique, which provides two components of velocity in the measurement plane. This technique utilizes a pulsed-laser for illumination of the seeded flow field that is imaged by a digital camera. The particle kinematics are simultaneously imaged using a light emitting diode (LED) line light that illuminates a planar cross section of the flow that overlaps with the PIV field-of-view (FOV). The line light is of low enough power that it does not affect the PIV measurements, but powerful enough to illuminate the larger particles of interest imaged using the high-speed camera. High-speed images that contain the laser pulses from the PIV technique are easily filtered by examining the summed intensity level of each high-speed image. By making the frame rate of the high-speed camera incommensurate with that of the PIV camera frame rate, the number of contaminated frames in the high-speed time series can be minimized. The technique is suitable for mean flows that are predominantly two-dimensional, contain particles that are at least 5 times the mean diameter of the PIV seeding tracers, and are low in concentration.

The technique described herein offers a low cost and relatively simple method to simultaneously measure particle kinematics and turbulence in flows with low particle concentrations. The turbulence is measured using particle image velocimetry (PIV), and particle kinematics are calculated from images obtained with a high-speed camera in an overlapping field-of-view.

流成像技术同时测量湍流和粒子运动学

Numerous problems in scientific and engineering fields involve understanding the kinematics of particles in turbulent flows, such as contaminants, marine ...