我们感谢马雷克韦勒进行幻像实验。这项工作是由欧洲研究委员会（ERC启动格兰特309495:NeoNaNo）的支持下，北莱茵 - 威斯特法伦州的德国联邦州（NRW; High-Tech.NRW/EU-Ziel 2 PROGRAMM（EFRE）; ForSaTum），德国教育部和研究部（BMBF）（资助方案虚拟肝（0315743），LungSys（0315415C），LungSys2（0316042F），Photonik Forschung德国（13N13355）），亚琛工业大学（我TM 3种子基金），以及飞利浦研究（亚琛，德国）。

该协议包含以下步骤的详细描述:首先，幻影或小鼠和多峰鼠标床是用于成像制备。然后一个全身扫描中的μCT获得。随后鼠标床转移到两个扫描采集（和倒置）的FMT。这可以重复多鼠标在多个时间点。数据采集​​完成后，需要将数据导出和排序，以使自动分割（需要Definiens的软件许可证），以及图像融合和荧光重建（需要一个Imalytics临床前软件许可）。最后，示出了如何将多峰数据集可视化，以及如何器官交互式分割来量化荧光探针的生物分布。 1.准备幻影注:幻影是有用测试的成像系统，还确定校准事实上r代表一个新的探头。 <ol><li>制备200毫升水，2％琼脂糖，1.8 1g的TiO 2粉末，50微升台盼蓝的溶液。煮沸后，填充溶液为矩形形式，约8厘米长，3个厘米宽和1.5厘米的高度。 </li><li>准备几个荧光夹杂使用吸头的幻象，含有荧光和μCT造影剂的混合。要创建夹杂物，切断枪头，并用打火机密封他们。 </li><li>后的溶液凝固，插入夹杂物进入幻象。切掉了幻影的某些部分，以实现一个不规则的形状，并使其适合于多模式鼠标座。 </li><li>为确定校准系数为新的探头，一些幻象扫描需要。为此，默认FMT幻象被组合使用已知量的探针。对于增加的精度，添加4％的脂质乳剂的溶液以接收包含内相同的散射系数作为其余的幻影。还添加μCT造影剂的少量（2％），以方便进行图像分析。 </li></ol> 2.鼠标准备注:μCT-FMT成像需要特别的准备，包括麻醉和脱毛。 <ol><li>成像前7天把鼠标放在叶绿素食品。这将减少背景信号，并且是低于750纳米的FMT频道特别重要。 </li><li>所有动物实验均在麻醉状态下进行。以引发麻醉，将鼠标在充满2％异氟烷在空气（流速为5升/分钟），直至鼠标入睡的腔室。确认正确麻醉轻轻脚趾或皮肤捏并通过检查肌紧张松弛（ 例如 ，下巴肌肉）。以维持麻醉，继续使用被放置在鼠标的鼻管的异氟烷应用（在空气中2％异氟烷，流速1升/分钟）。为了防止眼睛drynesS，使用兽医软膏麻醉小鼠。 </li><li>注入造影剂，修复麻醉鼠标使用磁带加热垫。放置一个导管（连接于管注射器针头）至尾静脉注入的荧光对比剂（ 如 ，2纳摩尔，用5毫升最大注射体积/ kg体重， 例如 ，150微升的30克小鼠）。 </li><li>对于扫描毛茸茸的鼠标，扫描区域有提前脱毛。为此，使用剃须刀或脱毛霜。小鼠的一些菌株可以开发从脱毛膏皮疹。因此，监测小鼠的皮肤的变化，如果需要护理与兽医人员。还使用新的小鼠品系，当对少数动物测试公差。 </li><li>保持小鼠的μCT与FMT成像期间麻醉（在空气中2％异氟烷，流速1升/分钟）。 </li></ol> 3.鼠标床准备注:对于μCT-FMT扫描，使用多式联运鼠标床，既适合的μCT和FMT。 <ol><li>成像之前，请清洁鼠标床上湿纸巾。不要使用酒精，因为这可能会损坏亚克力玻璃。确保该标记是不含水的，因为这可能会损害自动标记检测。 </li><li>打开鼠标多式联运床的螺丝，取下上部。 </li><li>装上麻醉气体管上的鼠标床上，用胶带固定它。 </li><li>将鼠标麻醉到鼠标床，把鼻子插入气管。确保小鼠头部是在鼠标床的前指示器（ 图1）。 </li><li>确保鼠标在鼠标床中间为了优化使用的角度FMT领域。 </li><li>关闭鼠标床并拧紧螺丝，直到鼠标紧紧举行。确保鼠标可以通过可视化监控的胸部呼吸运动平稳呼吸。 </li></ol> 4.我μCTmaging 注意:使用μCT被执行的全身扫描。所产生的解剖数据所需的图像融合，对改进的荧光重建和图像分析。 <ol><li>将鼠标床用鼠标进入μCT。确保鼠标放置在它去"尾先"进入μCT的方式。这一点对于自动熔接重要。 </li><li>为了维持麻醉时μCT盖被关闭时，重新连接管通过μCT的情况下，以引导所述气体。首先拆下长管从鼠标床上，并将其连接到连接器的μCT外。那么剩下的自由端连接到内部μCT连接器。 </li><li>驱动鼠标床到μCT。确保燃气管不松，不能抓到旋转支架。如果必要的话，固定用胶带。把试管插入切口鼠标床的持有人。 </l我&gt; <li>关闭μCT并获得topogram。选择的至少两个副扫描以覆盖鼠标和鼠标床，这是融合和重建重要的主要部分。 </li><li>选择名为HQD-6565-360-90的μCT扫描协议，获得720预测与1032×1012像素在一个完整的旋转需要每副扫描90秒的扫描时间。管在电压65千伏，电流1.0毫安操作。另外，为了减少辐射剂量和扫描时间，选择扫描协议SQD-6565-360-29，其获取720预测与516 x 506像素的扫描时间每副扫描29秒。 </li><li>启动μCT扫描。蓝条表示进度。的副扫描将随后获得的。低温和流体损失是因为只有几分钟的短暂扫描持续时间的不存在的问题。扫描过程中不要打开μCT的盖子，因为这将中断自动扫描，以防止用户辐射。 </li><li>当扫描完成后，打开盖子，重新麻醉管和分离鼠标从床上持有人将其运到FMT。 </li></ol> 5. FMT成像注意:直接μCT扫描后，鼠标被扫描在FMT两种配置（向上和颠倒），它们一起用于一种改进的荧光重建。 <ol><li>打开麻醉气体供给（2％异氟烷在空气，流速1升/分钟）将鼠标床进FMT之前为FMT。使用FMT控制软件，创建一个研究小组用适当数量的科目（ 即只）。选择将用于成像（使用OsteoSense对于未列出的新颖探针）的探针。 </li><li>开展多式联运鼠标床用鼠标的FMT。长柔性麻醉气体管维持气体流。插入鼠标床到FMT之前，小心地取出管，因为它不需要的FMT内部。避免转动鼠标床的螺丝。 </li><li>将鼠标床与红色指示灯的FMT第一（"头先"）。此为图像融合成与μCT一致是重要的。 </li><li>关闭FMT。 </li><li>选择正确的研究组和课题。选择FMT所需频道（为OsteoSense 750EX，使用750纳米通道）。 </li><li>添加说明， 例如 ，"向上"或"向下"，然后按"捕捉"获得一个概述扫描。此捕获整个视野的反射率图像。确保不会有"反射图像只有"选中，否则它是不可能获得的3D扫描之后。 </li><li>调整为三维扫描的摄像参数。放大的视场，以包括尽可能鼠标。通常，头部和尾部将不会完全然而适合的视场。点击"高级221;并检查成像设置。设置采样密度为3毫米，灵敏度为正常，光照最小/最大为5000和50000，分别。 </li><li>点击"添加到队列中的重建"，然后单击"扫描"开始扫描格式化。这将需要大约5至15分钟，这取决于尺寸和鼠标的厚度，因为较长的曝光时间都需要较厚的对象。该器件包含一个热成像室，避免低温。 </li><li>扫描后，翻转鼠标床包括鼠标倒置，并获得另一次扫描。这提供了荧光重建附加数据。 </li><li>当μCT和FMT-扫描完成，鼠标从麻醉中醒来，不要离开它无人看管，直到它已经恢复了神志充足， 例如 ，走动或保持胸骨斜卧。 </li></ol> 6.图像融合与重构注:后的μCT-FMT扫描， 例如 ，在研究结束时完成，需要将所获得的数据进行排序，以使自动图像融合和荧光重建。 <ol><li>要扫描作进一步处理排序，创建文件夹为研究对象。对于每个μCT-FMT扫描，创建一个子文件夹名称中包含了鼠标的ID和时间点， 例如 ，M01_02h。 </li><li>对于每个μCT-FMT扫描，出口FMT扫描（上下）作为.FMT文件并将其保存到文件名使用任何与"_up.fmt"或"_down.fmt"结尾的子文件夹。每个.FMT文件包含所获取的原始数据， 即 ，由摄像机中，元数据，获取的激发和发射图像，例如曝光时间，并且由FMT所产生的荧光重建。 </li><li>使用μCT软件，创建重建各向同性体素尺寸35微米。选择一个光滑的重建内核（T10）。调整视场，以便整个鼠标床包括标记被覆盖。选择MIFX / RAW输出格式，并开始重建。改造完成后，将μCT重建文件到μCT-FMT扫描的子文件夹。 </li><li>出口μCT和FMT数据所有扫描。确保每个子文件夹包含两个文件的.fmt（上下）和μCT重建的MIFX / RAW格式。检查完整性选择菜单 - &gt; CT-FMT-&gt;检查完整性使用Imalytics临床前软件。错误的列表可能会出现，如缺少的.fmt文件或μCT重建。修正错误，并检查完整性，直到所有错误都解决。 </li><li>使用菜单 - &gt; CT-FMT-&gt;设置，检查Definiens公司软件的服务器的名称，必要时调整。默认是http://本地主机:8184，假设Definiens公司软件安装在同一台计算机上。 Definiens的软件是必需在下一步​​骤执行盟鼠标床和标记tomated分割。 </li><li>点击菜单 - &gt; CT-FMT-&gt;熔断器组在临床前Imalytics软件进行自动μCT-FMT融合整个研究。这需要几分钟每μCT-FMT和扫描结果与后缀"包"平行研究文件夹中的文件夹中。这包含文件（μCT数据和熔融供应商提供的FMT重建）有关的进一步分析的较小子集。 </li><li>单击Imalytics临床前软件菜单- &gt; CT-FMT-&gt;重建组（FMT）来进行荧光重建，包括吸收的产生和散射贴图13。尽管该处理是GPU加速20，每个重建需要1至4个小时，这取决于鼠标的大小。结果将存储在包的文件夹。注:为了使更高的吞吐量，我们目前有56个GPU的GPU的集群上执行这些重建。 </li&gt; </ol> 7.图像分析注意:要提取的图像数据的定量测量，病变和器官的分割是必需的。 <ol><li>一旦套融合和重建的所有数据，扫描使用Imalytics临床前软件创建一个分割为每个μCT-FMT。 </li><li>加载μCT文件作为底图和荧光文件覆盖。按"三维"时开启体积透视和检查数据集。 </li><li>细分肺，点击菜单 - &gt;类 - &gt;添加类，创建一个名为"TMP"级。这也可以通过上下文菜单完成。创建一个新的类会自动将其设置为输出类为后续的分割操作。 <ol><li>执行阈值操作与段中的μCT数据集低强度所有地区（点击菜单 - &gt; Segmentation-&gt; Thresholding-&gt;下面，然后输入600）。现在，TMP类包含莫外的空气使用，但也肺组织。 </li><li>创建一个"龙"级。执行"填充区域"操作（右键单击进入肺部，然后选择菜单 - &gt;填写Region-&gt;填充区域无限制），肺与外界空气隔开。 </li><li>删除tmp的类，因为它不再需要。 </li></ol></li><li>细分凸区域， 例如 ，膀胱，使用涂鸦模式。首先创建一个类"膀胱"。 <ol><li>按F1键删除所有涂鸦。 </li><li>使用电脑鼠标，绘制涂鸦划定膀胱的边界。 </li><li>按F3以填补与显示为红色覆盖临时面具涂鸦包围的区域。迭代地添加更多的涂鸦（在任何切片方向），然后按F3，直到有足够的精度来实现的。通常情况下，在10片的涂鸦是足够的。 </li><li>按F4临时存储面膜为"膀胱"。 </li><li>这样进行分割其他凸地区，如心脏和肾脏。许多地区， 例如 ，胃或肝脏，可以由几个凸区域近似。 </li></ol></li><li>为了段脊柱，首先创建一个"骨"级。 <ol><li>选择ContextMenu-&gt; Thresholding-&gt;上面执行阈值操作，全亮像素分类（ 例如 ，上述1600）为"骨"。 </li><li>的区域填充操作将无法段脊柱，因为它是与骨架的许多其他部分， 例如 ，所述肋连接。通过反复绘制的涂鸦，并按下F2脊柱从头骨，肋骨和骶骨分开执行一些切割操作。 </li><li>最后，创建一个类"脊柱"，并进行区域填充操作以获取脊柱（右键点击进入脊椎和选择ContextMenu-&gt;填写Region-&gt;填充区域无限制）。 </li></ol></li><li>保存分割为第内部文件ê子文件夹的μCT-FMT扫描。使用一致的名称， 例如 ，organs.seg，使批量处理。 </li><li>选择菜单 - &gt; Statistics-&gt;类统计（覆盖），以产生一个电子表格，其中包含了平均荧光强度，体积和总量（平均和体积的乘积）为每个类。 </li><li>要生成包含所有μCT-FMT扫描各地区的值一个电子表格，点击菜单 - &gt;分批&gt;设置批次设置，然后点击菜单 - &gt;分批&gt;批量的统计数据。这避免了在创建和合并许多电子表格文件， 即 ，一个用于每个μCT-FMT扫描的努力。 </li></ol> 8.探头校准<ol><li>来计算的探针校准因子，多μCT-FMT幻象扫描用不同的已知量的探针是必须的（参见步骤1.4）， 例如 ，用100皮摩尔，50皮摩尔，25皮摩尔和0皮摩尔。 </li><li>如上述扫描幻影第4及5同样的幻象扫描，上下扫描的FMT是必需的。 </li><li>导出数据并作为第6进行描述和融合重建。 </li><li>通过分段阈值使用μCT数据，每次扫描列入（1200以上）和区域填充。 </li><li>产生与测量荧光量的电子表格并画出它们作为已知量的功能。计算的线性回归拟合的斜率。这是为探针的校准因子。 </li></ol>

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菲利克斯Gremse是创始人Gremse-IT，一个初创公司，提供软件和服务的合作，与飞利浦和部亚琛工业大学实验分子影像医学图像分析和所有者。

我们描述和应用协议多式联运μCT-FMT成像。我们用市售的和广泛使用的裂变材料条约和μCT设备3,11,15 - 17,21。而该协议需要特定的格式化，所述μCT可以被另一个μCT具有类似功能和可比扫描参数，例如被取代，视场应足够大，以覆盖鼠标床包括标记。 该FMT是用于生物分布分析，而没有用μCT或MRI 21结合，但是，所述解剖数据是有益的，以增加可重复性，因为该分割可以基于器官边界;这些都是μCT数据10可见。而集成的μCT-FMT设备已经开发2,7，这些都不是市售呢。此外，使用两个单独的设备允许管道， 即 ，下一个鼠标钙N为成像在μCT而第一鼠标仍然在格式化，以增加吞吐量。 为了减少人工工作量，我们执行自动标记检测和融合。此外，鼠标的形状被自动分段并且此信息显著改善了荧光重建11,13,22。用于定量荧光的重建，需要吸收和散射映射13,23。我们推导散射地图由μCT数据的自动分段和分配几个组织类型的已知散射系数（肺，骨，皮肤，脂肪，和剩余的软组织）24。随后，我们重构吸收图从光的原始数据是用于良好灌注器官，如心脏和肝脏13,20尤为重要。 在多个时间点在扫描几个小鼠很快导致大量的数据集进行分析。对于biodistribution研究，几种器官需要被分割为每个μCT-FMT扫描。不幸的是，分割不能重复使用，因为鼠标新反复定位到小鼠床。我们使用交互式分割工具，在我们研究所开发的，然而，其它的工具也可能是适当的25。我们生成素明智的分割，因为这些搭配好复杂的器官不是简单的形状，如椭圆和立方体26。自动全动物分割将是有用的，以进一步减少手工工作量27，但是交互式分割工具仍需要以校正分割误差。此外，自动分割工具很难预料的特殊情况下，如病理正确。由于我们使用本地μCT扫描，一些器官如脾脏是非常困难的，甚至细分手动。造影剂会有所帮助，但有问题的耐受性，这是难以maintai纵贯全境成像呐稳定造影剂分布。 我们的幻像的研究表明，该信号定位是使用形状信息为荧光重建时改善。 在体内 ，有类似的改进是显而易见的早期时间点（注射后15分钟），当大量的探针在已经是膀胱。羟基磷灰石结合探头积聚在骨骼和关节。值得注意的是如何快速发生这种情况， 即 ，该信号已经在注射后脊柱15分钟清晰可见。这大概是由探头，它可以快速外渗并扩散到目标区域的低分子量导致的。该探针结合共价与其靶羟基磷灰石和未结合的探针被排出体外。对于稍后的时间点，6小时和注射后24小时之间，在脊柱的信号强度保持相对稳定，很可能是因为几乎没有任何重光酸痛深入到鼠标漂白的荧光。对于我们的研究中，我们使用的750纳米通道，这导致在低背景荧光的明显对于注射前获得的扫描。在较低的波长，更多的背景信号可以预期28。 总之，我们描述了用于市售FMT和μCT装置多模式成像协议。我们表明，该组合提供了荧光重建的好处。我们说明如何的生物分布曲线是从大量的图像数据的由交互式器官分割和批量处理装置提取。我们认为，这一标准化工作流可以是用于药物开发和使用荧光标记的探针等影像学很有帮助。

荧光介导的断层扫描，也称为荧光分子断层摄影术（FMT），是一种有前途的技术，以定量评估弥漫组织，如麻醉小鼠甚至人体组织， 例如 ，乳房或指关节的荧光分布。与此相反，以非侵入性的显微技术，其允许浅表目标成像在亚细胞分辨率1，格式化允许荧光光源在深度几厘米，尽管三维重建在较低的分辨率2。许多靶向荧光探针可用来图像的血管生成，细胞凋亡，炎症，和其他2 - 5。有些探头激活， 例如通过特定酶切割导致荧光的unquenching。此外，表达荧光蛋白的报告基因可以被成像， 例如 ，以跟踪肿瘤细胞迁移6。 FMT强烈受益于结合解剖形态， 例如 ，μCT2,7或MRI 8。而独立的FMT装置是市售装置9中，荧光图像是困难而不解剖参考的信息以解译。最近，我们能够显示，该熔融解剖图像数据能够更可靠的分析10。解剖数据还可以用于提供先验知识，例如鼠标，这对精确的光学建模和荧光重建11重要的外部形状。此外，光散射和吸收的地图可使用组织类型的分割来估计并通过分配类特定系数12,13。对于近红外光，血红蛋白的主要吸收在小鼠中，除了黑色素和毛皮14。由于相对血容量数量级区域而变化，一个吸收图是用于全特别重要titative荧光重建13。 使用非侵入性的成像装置的一个优点是，可使小鼠纵向进行成像， 即，在多个时间点。这是重要的，以评估探针的动态特性， 即，它们的目标积累，生物分布和排泄10,15，或评估疾病进展16。当在多个时间点成像几个小鼠中，大量的图像数据集产生。启用可比性，这些应以系统的方式， 即获得，具有良好定义和记录协议。大量扫描构成图像分析，这是需要提取的图象数据的定量测量是一个挑战。 我们研究的目的是要提供使用了的和优化的μCT-FMT成像协议的详细描述，在整个几个研究10,13,15,17,18。我们描述如何生成数据集，处理，可视化，并进行分析。此使用证明已建立的分子探针，OsteoSense，结合羟基磷灰石19，并可以用于图像骨骼疾病和重塑2。所有涉及动物的程序批准了关于动物保护的政府审查委员会。

<table border="0" cellpadding="0" cellspacing="0">
	<tbody>
		<tr>
			<td>FMT (Fluorescence molecular tomography) FMT2500 LX</td>
			<td>PerkinElmer</td>
			<td>FMT2000</td>
			<td>Device for fluorescence molecular tomography</td>
		</tr>
		<tr>
			<td>&#181;CT (micro computed tomography) Tomoscope Duo</td>
			<td>CT Imaging GmbH</td>
			<td>Tomoscope Duo</td>
			<td>Device for micro computed tomography</td>
		</tr>
		<tr>
			<td>Multimodal Mouse Bed</td>
			<td>CT Imaging GmbH</td>
			<td>Experimental builder</td>
			<td>Partially transparent animal holder</td>
		</tr>
		<tr>
			<td>IsoFlo (isoflurane, USP)</td>
			<td>Abbott</td>
			<td>05260-05</td>
			<td>Isoflurane Inhalation anesthesia</td>
		</tr>
		<tr>
			<td>Small animal anesthesia system</td>
			<td>Harvard apparatus</td>
			<td>726419</td>
			<td>Complete Isoflurane Table-Top System</td>
		</tr>
		<tr>
			<td>Chlorophyll-free mouse food</td>
			<td>Ssniff</td>
			<td>E15051</td>
			<td>low chlorophyll / low fluorescence food</td>
		</tr>
		<tr>
			<td>OsteoSense 750EX</td>
			<td>PerkinElmer</td>
			<td>NEV10053EX</td>
			<td>Animal FMT contrast agent</td>
		</tr>
		<tr>
			<td>Portex Fine Bore Polythene Tubing</td>
			<td>Smith medical</td>
			<td>800/100/120</td>
			<td>Tube for injection catheter</td>
		</tr>
		<tr>
			<td>Sterican 30g</td>
			<td>BBraun</td>
			<td>4656300</td>
			<td>Hypodermic needle for catheter</td>
		</tr>
		<tr>
			<td>Imeron</td>
			<td>Altana pharma</td>
			<td>INLA F.1/0203/3.5337.69</td>
			<td>CT contrast agent for the phantom inclusions</td>
		</tr>
		<tr>
			<td>Agarose</td>
			<td>Sigma</td>
			<td>90-12-36-6</td>
			<td>Agarose for phantom production</td>
		</tr>
		<tr>
			<td>TiO2</td>
			<td>Applichem</td>
			<td>A1900,1000</td>
			<td>Titanium oxyde as phantom scattering agent</td>
		</tr>
		<tr>
			<td>Trypan blue</td>
			<td>Fluka</td>
			<td>93595</td>
			<td>Trypan blue to adjust phantom light propagation</td>
		</tr>
		<tr>
			<td>Cy7</td>
			<td>Lumiprobe</td>
			<td>15020</td>
			<td>Fluorochrome for the phantom inclusions</td>
		</tr>
		<tr>
			<td>Lipovenoes 20%</td>
			<td>Fresenius Kabi</td>
			<td>3094740</td>
			<td>Lipid emulsion, scattering agent for FMT contrast agents</td>
		</tr>
		<tr>
			<td>Definiens Developer XD Server</td>
			<td>Definiens AG</td>
			<td>Server XD</td>
			<td>Software platform for automated segmentation</td>
		</tr>
		<tr>
			<td>Imalytics Preclinical</td>
			<td>ExMI/Gremse-IT</td>
			<td>Version 2.0.1</td>
			<td>Software for image fusion, reconstruction and analysis</td>
		</tr>
		<tr>
			<td>NVIDIA Geforce Titan</td>
			<td>Asus</td>
			<td>GTXTITAN6GD5</td>
			<td>High end computer graphics card, 6GB Memory</td>
		</tr>
	</tbody>
</table>

hybrid &#181;ct-fmt imaging and image analysis

荧光介导的断层摄影（FMT）使纵向和定量测定体内的荧光分布的，并且可以用来评估的新颖的探针的生物分布和使用已建立的分子探针或报道基因，以评估疾病进展。用解剖形态， 例如所述的组合，微计算机断层扫描（μCT），有利于图像分析和荧光重建。我们描述了一个协议，用于多μCT-FMT成像，包括必要的提取定量测量的图像处理步骤。制备小鼠和执行成像后，多峰数据集被注册。随后，改进荧光重建被执行时，它考虑到鼠标的形状。对于定量分析，器官分割是基于使用我们的交互式分割工具的解剖数据生成。最后，将生物分布立方米使用间歇处理特征生成rves。我们表明了该方法的适用性通过评估一个众所周知的探针结合于骨和关节的生物分布。

我们应用所描述的协议，以评估靶向探针，OsteoSense，其结合羟基磷灰石的生物分布。 3只小鼠（C57BL / 6 APOE - / - AHSG - / - 双敲除小鼠10周龄）进行成像之前和15分钟，2小时，4小时，6小时，和iv注射2纳摩尔OsteoSense后24小时。我们的软件自动检测内置于多峰鼠标床的标记物（ 图1，图2A，B），这使融合的解剖μCT数据与由FMT（ 图2C，D）执行的荧光重建。因为OsteoSense是具有低分子量，快速肾排泄，因此高的信号中的膀胱的探针的预期。该FMT的荧光重建的融合显示问题，如膀胱（ 图2C，D）的外错位的信号。发生，因为FMT并不知道鼠标的真实形状，并且假定一个块的形状，这些问题。欧ř重建确定准确形状从μCT数据并产生散射和吸收，以使更精确的荧光重建具有更好信号定位，这是膀胱（ 图2E，F）尤其明显映射13。 分配重建荧光适当的区域，我们交互分段使用我们的软件（ 图3）多个器官。对于每个18扫描，7个地区的基础上的μCT数据， 即 ，心脏，肺，肝，肾，脊柱，肠和膀胱进行分割。随后，该软件用于计算对于每个126区域的平均荧光浓度。幸运的是，该软件提供了一个批处理模式，它计算所有的值，并将其保存在一个单一的电子表格。 为了显现荧光分布，对于每个时间点生成3D效果图，使用可比窗口设置（ 图4A-F）。使用量化的器官值，该生物分布计算通过平均三个小鼠（ 图4G）的器官值。预扫描，注射前获得的，可以忽略不计显示出背景信号。 15分钟注射后，最强的信号出现，因为快速肾排泄在泌尿膀胱。在随后的时间点，其余的探针已经积累在骨骼和关节。 <img alt="图1" src="/files/ftp_upload/52770/52770fig1.jpg" /> 图1.多式联运鼠标床。（A）多模式鼠标床包含两个有机玻璃板指紧紧握住鼠标。紧缩是用两个螺丝调整。鼠标床包含标记（空洞）的图像融合。麻醉气体被使用柔性管，其被固定用叔供给猿。 （B）中的小鼠床附着到金属支架和在旋转的μCT机架的中心举行。 （℃）避免鼠标床和金属支架之间的间隙，因为否则，标记可能被错误地分配，导致不正确的融合。麻醉气体管应连接到管连接器。 （D）中的小鼠床应插入FMT与前先以使得正确的自动融合。 （E）的标志是可见的FMT摄像头，用于自动标记检测和融合。 <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/52770/52770fig1large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图2" src="/files/ftp_upload/52770/52770fig2.jpg" /> 图2.图像融合与重构。（A，B）标记和谅解备忘录的外形本身是由自动分割算法来确定。 （C，D）注射OsteoSense的15分钟后，有相当数量的探针已被排泄到尿膀胱。用μCT数据融合的供应商提供的重建后，问题变得更明显。大部分的信号的周围出现膀胱但膀胱不内侧和一些信号甚至会出现在空气中。这是因为FMT假定一个块形鼠标。 （E，F）我们改进的荧光重建，使用从μCT数据得出鼠标的形状，产生更好的本地化膀胱内的荧光。 <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/52770/52770fig2large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> <img alt="图3" src="/files/ftp_upload/52770/52770fig3.jpg" /> 图3.互动器官分割，。离子（ 一 ）量化荧光分布，多个器官被分割:心脏（红色），肺（粉红色），肝（棕色），胃癌（米色），脊柱（紫色），肾脏（黄色），肠（绿色）和膀胱（金）。 （B）中的肺，其强烈的对比与周围组织相比，使用阈值和区域填充分段。 （C）的圆形的器官，如膀胱，肾脏和心脏都用"涂鸦"分段。 （ 四）机关处理更复杂的形状， 例如 ，肝，胃是分段逐步使用涂鸦。为了段脊椎，一个高门槛被应用到段的所有骨骼。然后一些骨头， 比如 ，肋骨，被切掉，直到脊柱仍然存在。 <a href="https://jove.unicartagenaproxy.elogim.com/files/ftp_upload/52770/52770fig3large.jpg" target="_blank">请点击此处查看该图的放大版本。</a> 图4.生物分布。为了评估的生物分布，小鼠被扫描在几个时间点（AF）。 （ 一 ）预扫描，注射前，显示在750纳米通道点点的背景信号。注射后（B）的 15分钟，有相当数量的探针已经是在泌尿膀胱。 （C）在2小时的时间点，鼠标撒了尿，这导致在小鼠以外的一些荧光。在稍后的时间点（DF），信号主要出现在骨骼和关节， 即 ，在脊椎和膝盖。 （G）的量化荧光浓度显示为选定的器官。

Watch this Scientific Journal Video about Hybrid Ã‚ÂµCT-FMT imaging and image analysis at JoVE.com

Hybrid  &#181;CT-FMT imaging and image analysis

We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (&#181;CT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.

混合μCT-FMT成像和图像分析

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to ...

Universidad de Cartagena UDEC

Research

JoVE Journal

Bioengineering

Hybrid &#181;CT-FMT imaging and image analysis

13.1K 次观看.  RWTH Aachen University. We describe a protocol for hybrid imaging, combining fluorescence-mediated tomography (FMT) with micro computed tomography (µCT). After fusion and reconstruction, we perform interactive organ segmentation to extract quantitative measurements of the fluorescence distribution.

视频: Hybrid  µCT-FMT imaging and image analysis

Specimen Preparation, Imaging, and Analysis Protocols for Knife-edge Scanning Microscopy

Major advances in high-throughput, high-resolution, 3D microscopy techniques have enabled the acquisition of large volumes of neuroanatomical data at submicrometer resolution. One of the first such instruments producing whole-brain-scale data is the Knife-Edge Scanning Microscope (KESM)7, 5, 9, developed and hosted in the authors' lab. KESM has been used to section and image whole mouse brains at submicrometer resolution, revealing the intricate details of the neuronal networks (Golgi)1, 4, 8, vascular networks (India ink)1, 4, and cell body distribution (Nissl)3. The use of KESM is not restricted to the mouse nor the brain. We have successfully imaged the octopus brain6, mouse lung, and rat brain. We are currently working on whole zebra fish embryos. Data like these can greatly contribute to connectomics research10; to microcirculation and hemodynamic research; and to stereology research by providing an exact ground-truth. 
In this article, we will describe the pipeline, including specimen preparation (fixing, staining, and embedding), KESM configuration and setup, sectioning and imaging with the KESM, image processing, data preparation, and data visualization and analysis. The emphasis will be on specimen preparation and visualization/analysis of obtained KESM data. We expect the detailed protocol presented in this article to help broaden the access to KESM and increase its utilization.

The full process from brain specimen preparation to serial sectioning imaging using the Knife-Edge Scanning Microscope, to data visualization and analysis is described. This technique is currently used to acquire mouse brain data, but it is applicable to other organs, other species.

刀口扫描显微镜的样品制备，图像和分析协议

Major advances in high-throughput, high-resolution, 3D microscopy techniques have enabled the acquisition of large volumes of neuroanatomical data at ...

科研

生物工程

Optical Frequency Domain Imaging of Ex vivo Pulmonary Resection Specimens: Obtaining One to One Image to Histopathology Correlation

Lung cancer is the leading cause of cancer-related deaths1. Squamous cell and small cell cancers typically arise in association with the conducting airways, whereas adenocarcinomas are typically more peripheral in location. Lung malignancy detection early in the disease process may be difficult due to several limitations: radiological resolution, bronchoscopic limitations in evaluating tissue underlying the airway mucosa and identifying early pathologic changes, and small sample size and/or incomplete sampling in histology biopsies. High resolution imaging modalities, such as optical frequency domain imaging (OFDI), provide non-destructive, large area 3-dimensional views of tissue microstructure to depths approaching 2 mm in real time (Figure 1)2-6. OFDI has been utilized in a variety of applications, including evaluation of coronary artery atherosclerosis6,7 and esophageal intestinal metaplasia and dysplasia6,8-10.
Bronchoscopic OCT/OFDI has been demonstrated as a safe in vivo imaging tool for evaluating the pulmonary airways11-23 (Animation). OCT has been assessed in pulmonary airways16,23 and parenchyma17,22 of animal models and in vivo human airway14,15. OCT imaging of normal airway has demonstrated visualization of airway layering and alveolar attachments, and evaluation of dysplastic lesions has been found useful in distinguishing grades of dysplasia in the bronchial mucosa11,12,20,21. OFDI imaging of bronchial mucosa has been demonstrated in a short bronchial segment (0.8 cm)18. Additionally, volumetric OFDI spanning multiple airway generations in swine and human pulmonary airways in vivo has been described19. Endobronchial OCT/OFDI is typically performed using thin, flexible catheters, which are compatible with standard bronchoscopic access ports. Additionally, OCT and OFDI needle-based probes have recently been developed, which may be used to image regions of the lung beyond the airway wall or pleural surface17.
While OCT/OFDI has been utilized and demonstrated as feasible for in vivo pulmonary imaging, no studies with precisely matched one-to-one OFDI:histology have been performed. Therefore, specific imaging criteria for various pulmonary pathologies have yet to be developed. Histopathological counterparts obtained in vivo consist of only small biopsy fragments, which are difficult to correlate with large OFDI datasets. Additionally, they do not provide the comprehensive histology needed for registration with large volume OFDI. As a result, specific imaging features of pulmonary pathology cannot be developed in the in vivo setting. Precisely matched, one-to-one OFDI and histology correlation is vital to accurately evaluate features seen in OFDI against histology as a gold standard in order to derive specific image interpretation criteria for pulmonary neoplasms and other pulmonary pathologies. Once specific imaging criteria have been developed and validated ex vivo with matched one-to-one histology, the criteria may then be applied to in vivo imaging studies. Here, we present a method for precise, one to one correlation between high resolution optical imaging and histology in ex vivo lung resection specimens. Throughout this manuscript, we describe the techniques used to match OFDI images to histology. However, this method is not specific to OFDI and can be used to obtain histology-registered images for any optical imaging technique. We performed airway centered OFDI with a specialized custom built bronchoscopic 2.4 French (0.8 mm diameter) catheter. Tissue samples were marked with tissue dye, visible in both OFDI and histology. Careful orientation procedures were used to precisely correlate imaging and histological sampling locations. The techniques outlined in this manuscript were used to conduct the first demonstration of volumetric OFDI with precise correlation to tissue-based diagnosis for evaluating pulmonary pathology24. This straightforward, effective technique may be extended to other tissue types to provide precise imaging to histology correlation needed to determine fine imaging features of both normal and diseased tissues.

Optical Frequency Domain Imaging of Ex vivo Pulmonary Resection Specimens: Obtaining One to One Image to Histopathology Correlation

A method to image ex vivo pulmonary resection specimens with optical frequency domain imaging (OFDI) and obtain precise correlation to histology is described, which is essential to developing specific OFDI interpretation criteria for pulmonary pathology. This method is applicable to other tissue types and imaging techniques to obtain precise imaging to histology correlation for accurate image interpretation and assessment. Imaging criteria established with this technique would then be applicable to image assessment in future in vivo studies.

光频域成像<em&gt;体外</em肺切除标本:获得一对一的图像病理组织学的相关性

Lung cancer is the leading cause of cancer-related deaths1. Squamous cell and small cell cancers typically arise in association with the conducting ...

High-resolution Spatiotemporal Analysis of Receptor Dynamics by Single-molecule Fluorescence Microscopy

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., kinetics, coexistence of different states and populations, transient interactions), which are typically hidden in ensemble measurements, such as those obtained with standard biochemical or microscopy methods. Thus, dynamic events, such as receptor-receptor interactions, can be followed in real time in a living cell with high spatiotemporal resolution. This protocol describes a method based on labeling with small and bright organic fluorophores and total internal reflection fluorescence (TIRF) microscopy to directly visualize single receptors on the surface of living cells. This approach allows one to precisely localize receptors, measure the size of receptor complexes, and capture dynamic events such as transient receptor-receptor interactions. The protocol provides a detailed description of how to perform a single-molecule experiment, including sample preparation, image acquisition and image analysis. As an example, the application of this method to analyze two G-protein-coupled receptors, i.e., &#946;2-adrenergic and &#947;-aminobutyric acid type B (GABAB) receptor, is reported. The protocol can be adapted to other membrane proteins and different cell models, transfection methods and labeling strategies.

This protocol describes how to use total internal reflection fluorescence microscopy to visualize and track single receptors on the surface of living cells and thereby analyze receptor lateral mobility, size of receptor complexes as well as to visualize transient receptor-receptor interactions. This protocol can be extended to other membrane proteins.

受体动力学通过单分子荧光显微镜的高分辨率时空分析

Single-molecule microscopy is emerging as a powerful approach to analyze the behavior of signaling molecules, in particular concerning those aspect (e.g., ...

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

Recent fiber-bundle microendoscopy techniques enable non-invasive analysis of in vivo tissue using either imaging techniques or a combination of spectroscopy techniques. Combining imaging and spectroscopy techniques into a single optical probe may provide a more complete analysis of tissue health. In this article, two dissimilar modalities are combined, high-resolution fluorescence microendoscopy imaging and diffuse reflectance spectroscopy, into a single optical probe. High-resolution fluorescence microendoscopy imaging is a technique used to visualize apical tissue micro-architecture and, although mostly a qualitative technique, has demonstrated effective real-time differentiation between neoplastic and non-neoplastic tissue. Diffuse reflectance spectroscopy is a technique which can extract tissue physiological parameters including local hemoglobin concentration, melanin concentration, and oxygen saturation. This article describes the specifications required to construct the fiber-optic probe, how to build the instrumentation, and then demonstrates the technique on in vivo human skin. This work revealed that tissue micro-architecture, specifically apical skin keratinocytes, can be co-registered with its associated physiological parameters. The instrumentation and fiber-bundle probe presented here can be optimized as either a handheld or endoscopically-compatible device for use in a variety of organ systems. Additional clinical research is needed to test the viability of this technique for different epithelial disease states.

Multimodal Imaging and Spectroscopy Fiber-bundle Microendoscopy Platform for Non-invasive, In Vivo Tissue Analysis

The assembly and use of a multimodal microendoscope is described which can co-register superficial tissue image data with tissue physiological parameters including hemoglobin concentration, melanin concentration, and oxygen saturation. This technique can be useful for evaluating tissue structure and perfusion, and can be optimized for individual needs of the investigator.

多峰成像和光谱学纤维束显微内窥镜纲要非侵入性，<em&gt;在体内</em&gt;组织分析

Recent fiber-bundle microendoscopy techniques enable non-invasive analysis of in vivo tissue using either imaging techniques or a combination of ...

Magnetic Levitation Coupled with Portable Imaging and Analysis for Disease Diagnostics

Currently, many clinical diagnostic procedures are complex, costly, inefficient, and inaccessible to a large population in the world. The requirements for specialized equipment and trained personnel require that many diagnostic tests be performed at remote, centralized clinical laboratories. Magnetic levitation is a simple yet powerful technique and can be applied to levitate cells, which are suspended in a paramagnetic solution and placed in a magnetic field, at a position determined by equilibrium between a magnetic force and a buoyancy force. Here, we present a versatile platform technology designed for point-of-care diagnostics which uses magnetic levitation coupled to microscopic imaging and automated analysis to determine the density distribution of a patient's cells as a useful diagnostic indicator. We present two platforms operating on this principle: (i) a smartphone-compatible version of the technology, where the built-in smartphone camera is used to image cells in the magnetic field and a smartphone application processes the images and to measures the density distribution of the cells and (ii) a self-contained version where a camera board is used to capture images and an embedded processing unit with attached thin-film-transistor (TFT) screen measures and displays the results. Demonstrated applications include: (i) measuring the altered distribution of a cell population with a disease phenotype compared to a healthy phenotype, which is applied to sickle cell disease diagnosis, and (ii) separation of different cell types based on their characteristic densities, which is applied to separate white blood cells from red blood cells for white blood cell cytometry. These applications, as well as future extensions of the essential density-based measurements enabled by this portable, user-friendly platform technology, will significantly enhance disease diagnostic capabilities at the point of care.

We present a magnetic levitation technique coupled with automated imaging and analysis in both a smartphone-compatible device and a device with embedded imaging and processing. This is applied to measure the density distribution of cells with two demonstrated biomedical applications: sickle cell disease diagnosis and separating white and red blood cells.

磁悬浮加上便携式成像与分析诊断疾病

Currently, many clinical diagnostic procedures are complex, costly, inefficient, and inaccessible to a large population in the world. The requirements for ...

Image-guided, Laser-based Fabrication of Vascular-derived Microfluidic Networks

This detailed protocol outlines the implementation of image-guided, laser-based hydrogel degradation for the fabrication of vascular-derived microfluidic networks embedded in PEGDA hydrogels. Here, we describe the creation of virtual masks that allow for image-guided laser control; the photopolymerization of a micromolded PEGDA hydrogel, suitable for microfluidic network fabrication and pressure head-driven flow; the setup and use of a commercially available laser scanning confocal microscope paired with a femtosecond pulsed Ti:S laser to induce hydrogel degradation; and the imaging of fabricated microfluidic networks using fluorescent species and confocal microscopy. Much of the protocol is focused on the proper setup and implementation of the microscope software and microscope macro, as these are crucial steps in using a commercial microscope for microfluidic fabrication purposes that contain a number of intricacies. The image-guided component of this technique allows for the implementation of 3D image stacks or user-generated 3D models, thereby allowing for creative microfluidic design and for the fabrication of complex microfluidic systems of virtually any configuration. With an expected impact in tissue engineering, the methods outlined in this protocol could aid in the fabrication of advanced biomimetic microtissue constructs for organ- and human-on-a-chip devices. By mimicking the complex architecture, tortuosity, size, and density of in vivo vasculature, essential biological transport processes can be replicated in these constructs, leading to more accurate in vitro modeling of drug pharmacokinetics and disease.

This protocol outlines the implementation of image-guided, laser-based hydrogel degradation to fabricate vascular-derived, biomimetic microfluidic networks embedded in poly(ethylene glycol) diacrylate (PEGDA) hydrogels. These biomimetic microfluidic systems may be useful for tissue engineering applications, generation of in vitro disease models, and fabrication of advanced "on-a-chip" devices.

影像引导下，血管源性微流控网络的基于激光的研制

This detailed protocol outlines the implementation of image-guided, laser-based hydrogel degradation for the fabrication of vascular-derived microfluidic ...

A Rapid Image-based Bacterial Virulence Assay Using Amoeba

Traditional bacterial virulence assays involve prolonged exposure of bacteria over the course of several hours to host cells. During this time, bacteria can undergo changes in the physiology due to the exposure to host growth environment and the presence of the host cells. We developed an assay to rapidly measure the virulence state of bacteria that minimize the extent to which bacteria grow in the presence of host cells. Bacteria and amoebae are mixed together and immobilized on a single imaging plane using an agar pad. The procedure uses single-cell fluorescence imaging with calcein-acetoxymethyl ester (calcein-AM) as an indicator of host cell health. The fluorescence of host cells is analyzed after 1 h of exposure of host cells to bacteria using epifluorescence microscopy. Image analysis software is used to compute a host killing index. This method has been used to measure virulence within planktonic and surface-attached Pseudomonas aeruginosa sub-populations during the initial stage of biofilm formation and may be adapted to other bacteria and other stages of biofilm growth. This protocol provides a rapid and robust method of measuring virulence and avoids many of the complexities associated with the growth and maintenance of mammalian cell lines. Virulence phenotypes measured here using amoebae have also been validated using mouse macrophages. In particular, this assay was used to establish that surface attachment upregulates virulence in P. aeruginosa.

Here, we present a protocol to measure the virulence of planktonic or surface-attached bacteria using D. discoideum (amoeba) as a host. Virulence is measured over a period of 1 h and host killing is quantified using fluorescence microscopy and image analysis. We demonstrate this protocol using the bacterium P. aeruginosa.

一种基于变形虫的快速图像细菌毒力检测方法

Traditional bacterial virulence assays involve prolonged exposure of bacteria over the course of several hours to host cells. During this time, bacteria ...

Quantifying Intermembrane Distances with Serial Image Dilations

A recently-described extracellular nanodomain, termed the perinexus, has been implicated in ephaptic coupling, which is an alternative mechanism for electrical conduction between cardiomyocytes. The current method for quantifying this space by manual segmentation is slow and has low spatial resolution.We developed an algorithm that uses serial image dilations of a binary outline to count the number of pixels between two opposing 2 dimensional edges.This algorithm requires fewer man hours and has a higher spatial resolution than the manual method while preserving the reproducibility of the manual process.In fact, experienced and novice investigators were able to recapitulate the results of a previous study with this new algorithm.The algorithm is limited by the human input needed to manually outline the perinexus and computational power mainly encumbered by a pre-existing pathfinding algorithm.However, the algorithm&#39;s high-throughput capabilities, high spatial resolution and reproducibility make it a versatile and robust measurement tool for use across a variety of applications requiring the measurement of the distance between any 2-dimensional (2D) edges.

The purpose of this algorithm is to continuously measure the distance between two 2-dimensional edges using serial image dilations and pathfinding. This algorithm can be applied to a variety of fields such as cardiac structural biology, vascular biology, and civil engineering.

用序列图像量化膜间隙距离伸缩

A recently-described extracellular nanodomain, termed the perinexus, has been implicated in ephaptic coupling, which is an alternative mechanism for ...

Substructure Analyzer: A User-Friendly Workflow for Rapid Exploration and Accurate Analysis of Cellular Bodies in Fluorescence Microscopy Images

The last decade has been characterized by breakthroughs in fluorescence microscopy techniques illustrated by spatial resolution improvement but also in live-cell imaging and high-throughput microscopy techniques. This led to a constant increase in the amount and complexity of the microscopy data for a single experiment. Because manual analysis of microscopy data is very time consuming, subjective, and prohibits quantitative analyses, automation of bioimage analysis is becoming almost unavoidable. We built an informatics workflow called Substructure Analyzer to fully automate signal analysis in bioimages from fluorescent microscopy. This workflow is developed on the user-friendly open-source platform Icy and is completed by functionalities from ImageJ. It includes the pre-processing of images to improve the signal to noise ratio, the individual segmentation of cells (detection of cell boundaries) and the detection/quantification of cell bodies enriched in specific cell compartments. The main advantage of this workflow is to propose complex bio-imaging functionalities to users without image analysis expertise through a user-friendly interface. Moreover, it is highly modular and adapted to several issues from the characterization of nuclear/cytoplasmic translocation to the comparative analysis of different cell bodies in different cellular sub-structures. The functionality of this workflow is illustrated through the study of the Cajal (coiled) Bodies under oxidative stress (OS) conditions. Data from fluorescence microscopy show that their integrity in human cells is impacted a few hours after the induction of OS. This effect is characterized by a decrease of coilin nucleation into characteristic Cajal Bodies, associated with a nucleoplasmic redistribution of coilin into an increased number of smaller foci. The central role of coilin in the exchange between CB components and the surrounding nucleoplasm suggests that OS induced redistribution of coilin could affect the composition and the functionality of Cajal Bodies.

We present a freely available workflow built for rapid exploration and accurate analysis of cellular bodies in specific cell compartments in fluorescence microscopy images. This user-friendly workflow is designed on the open-source software Icy and also uses ImageJ functionalities. The pipeline is affordable without knowledge in image analysis.

子结构分析仪:用于荧光显微镜图像中细胞体快速探索和精确分析的用户友好型工作流程

The last decade has been characterized by breakthroughs in fluorescence microscopy techniques illustrated by spatial resolution improvement but also in ...

Imaging and Quantification of the Area of Fast-Moving Microbubbles Using a High-Speed Camera and Image Analysis

An experimental and image analysis technique is presented for imaging cavitation bubbles and calculating their area. The high-speed imaging experimental technique and image analysis protocol presented here can also be applied for imaging microscopic bubbles in other fields of research; therefore, it has a wide range of applications. We apply this to image cavitation around dental ultrasonic scalers. It is important to image cavitation to characterize it and to understand how it can be exploited for various applications. Cavitation occurring around dental ultrasonic scalers can be used as a novel method of dental plaque removal, which would be more effective and cause less damage than current periodontal therapy techniques. We present a method for imaging the cavitation bubble clouds occurring around dental ultrasonic scaler tips using a high-speed camera and a zoom lens. We also calculate the area of cavitation using machine learning image analysis. Open source software is used for image analysis. The image analysis presented is easy to replicate, does not require programming experience, and can be modified easily to suit the application of the user.

Cavitation microbubbles are imaged using a high-speed camera attached to a zoom lens. The experimental setup is explained, and image analysis is used to calculate the area of the cavitation. Image analysis is done using ImageJ.

使用高速摄像机和图像分析对快速移动微泡区域进行成像和定量

An experimental and image analysis technique is presented for imaging cavitation bubbles and calculating their area. The high-speed imaging experimental ...

混合μCT-FMT成像和图像分析

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