We thank Marek Weiler for performing the phantom experiments. This work was supported by the European Research Council (ERC Starting Grant 309495: NeoNaNo), the German Federal State of North Rhine Westphalia (NRW; High-Tech.NRW/EU-Ziel 2-Programm (EFRE); ForSaTum), the German Ministry for Education and Research (BMBF) (funding programs Virtual Liver (0315743), LungSys (0315415C), LungSys2(0316042F), Photonik Forschung Deutschland (13N13355)), the RWTH Aachen University (I3TM Seed Fund), and Philips Research (Aachen, Germany).

The protocol contains a detailed description of the following steps: At first, phantoms or mice and the multimodal mouse bed are prepared for imaging. Then a whole-body scan is acquired in the &#181;CT. Subsequently the mouse bed is transferred to the FMT where two scans are acquired (up and upside down). This can be repeated for multiple mice at multiple time points. After completion of the data acquisition, the data needs to be exported and sorted to enable automated segmentation (requiring a Definiens software license), as well as image fusion and fluorescence reconstruction (requiring an Imalytics Preclinical software license). Finally it is shown how the multimodal data sets are visualized and how organs are interactively segmented to quantify the biodistribution of fluorescent probes.
1. Phantom Preparation
NOTE: Phantoms are useful to test the imaging system, but also to determine the calibration factor for a new probe.
<ol>
	<li>Prepare a solution of 200 ml water, 2 % agarose, 1.8 g TiO2 powder, 50 &#181;l trypan blue. After boiling, fill the solution into a rectangular form, approximately of 8 cm length, 3 cm width, and 1.5 cm height.</li>
	<li>Prepare several fluorescent inclusions in the phantom using pipette tips, containing a mix of fluorescence and &#181;CT contrast agent. To create the inclusions, cut the pipette tips and seal them with a lighter.</li>
	<li>After the solution has solidified, insert the inclusions into the phantom. Cut away some parts of the phantom to achieve an irregular shape and to fit it to the multimodal mouse holder.</li>
	<li>To determine the calibration factor for a new probe, some phantom scans are required. For this, the default FMT phantom is used in combination with known amounts of the probe. For an increased accuracy, add 4 % lipid emulsion to the solution to receive the same scattering coefficient inside the inclusion as in the rest of the phantom. Also add a small amount (2 %) of &#181;CT contrast agent for easier image analysis.</li>
</ol>
2. Mouse Preparation
NOTE: &#181;CT-FMT imaging requires special preparation including anesthetization and hair removal.
<ol>
	<li>Place the mouse on chlorophyll-free food 7 days before imaging. This will reduce the background signal and is particularly important for FMT channels below 750 nm.</li>
	<li>All animal experiments are performed under anesthesia. To initiate the anesthesia, place the mouse in a chamber filled with 2 % isoflurane in air (flow rate 5 L/min) until the mouse is falling asleep. Confirm proper anesthetization by gentle toe or skin pinching and by checking relaxation of muscle tonus (e.g., jaw muscle). To sustain the anesthesia, continue the isoflurane application using a tube that is placed on the nose of the mouse (2 % isoflurane in air, flow rate 1 L/min). In order to prevent eye dryness, use vet ointment on anesthetized mice.</li>
	<li>To inject contrast agent, fix the anesthetized mouse on a heating pad using tape. Place a catheter (syringe needle attached to a tube) to the tail vein and inject the fluorescent contrast agent (e.g., 2 nmol, with maximal injection volume of 5 ml/kg body weight, i.e., 150 &#181;l for a 30 g mouse).</li>
	<li>For scanning a hairy mouse, the scanning area has to be depilated in advance. For this, use a shaver or hair removal cream. Some strains of mice can develop rashes from the hair removal cream. Therefore, monitor the mice for skin changes and contact veterinary staff for care if needed. Also test the tolerance on a small number of animals when using new mouse strains.</li>
	<li>Keep the mouse anesthetized during &#181;CT and FMT imaging (2 % isoflurane in air, flow rate 1 L/min).</li>
</ol>
3. Mouse Bed Preparation
NOTE: For &#181;CT-FMT scanning, use a multimodal mouse bed, which fits both into the &#181;CT and the FMT.
<ol>
	<li>Before imaging, clean the mouse bed with wet tissues. Do not use ethanol because this may damage the acrylic glass. Make sure that the markers are free of water, because this may impair the automated marker detection.</li>
	<li>Open the screws of the multimodal mouse bed and remove the upper part.</li>
	<li>Attach the anesthetic gas tube on the mouse bed and fixate it with tape.</li>
	<li>Place the anesthetized mouse into the mouse bed and put the nose into the gas tube. Make sure the head of the mouse is at the front indicator of the mouse bed (Figure&#160;1).</li>
	<li>Make sure that the mouse is in the middle of the mouse bed to optimally use the field of view of the FMT.</li>
	<li>Close the mouse bed and tighten the screws until the mouse is tightly held. Make sure the mouse can breathe steadily by visual monitoring of thoracic breathing movements.</li>
</ol>
4. &#181;CT Imaging
NOTE: A whole-body scan is performed using the &#181;CT. The generated anatomical data is required for image fusion, for an improved fluorescence reconstruction and for image analysis.
<ol>
	<li>Place the mouse bed with the mouse into the &#181;CT. Make sure that the mouse is placed in a way that it goes &#8220;tail-first&#8221; into the &#181;CT. This is important for the automated fusion.</li>
	<li>In order to sustain anesthesia when the &#181;CT-lid is closed, reconnect the tubes to channel the gas through the case of the &#181;CT. First detach the long tube from the mouse bed and attach it to the connector at the outside of the &#181;CT. Then attach the remaining free end to the connector inside the &#181;CT.</li>
	<li>Drive the mouse bed into the &#181;CT. Make sure that the gas tube is not loose and cannot get caught by the rotating gantry. If necessary, fixate it with tape. Insert the tube into the cut-out of the holder of the mouse bed.</li>
	<li>Close the &#181;CT and acquire a topogram. Select at least two subscans to cover a substantial part of the mouse and the mouse bed, which is important for the fusion and reconstruction.</li>
	<li>Select the &#181;CT scan protocol named HQD-6565-360-90, which acquires 720 projections with 1032 x 1012 pixels during one full rotation requiring a scanning time of 90 s per subscan. Tubes are operated at voltage 65 kV and current 1.0 mA. Alternatively, to reduce the radiation dose and scanning duration, select the scanning protocol SQD-6565-360-29 which acquires 720 projections with 516 x 506 pixels with scanning time 29 s per subscan.</li>
	<li>Start the &#181;CT scan. The blue bar indicates the progress. The subscans will be acquired subsequently. Hypothermia and fluid loss are not a problem because of the short scanning duration of only a few minutes. Do not open the lid of the &#181;CT during scanning because this will automatically interrupt the scanning to protect the user from radiation.</li>
	<li>When the scanning is completed, open the lid, reconnect the anesthetic tube and detach the mouse bed from the holder to transport it to the FMT.</li>
</ol>
5. FMT Imaging
NOTE: Directly after &#181;CT scanning, the mouse is scanned in the FMT in two configurations (up and upside down) which are used together for an improved fluorescence reconstruction.
<ol>
	<li>Turn on the anesthetic gas supply (2 % isoflurane in air, flow rate 1 L/min) for the FMT before placing the mouse bed into the FMT. Using the FMT control software, create a study group with an appropriate number of subjects (i.e., mice). Select the probes that will be used for imaging (use OsteoSense for novel probes that are not listed).</li>
	<li>Carry the multimodal mouse bed with the mouse to the FMT. The long flexible anesthetic gas tube maintains the gas flow. Before inserting the mouse bed into the FMT, carefully remove the tube since it is not needed inside the FMT. Avoid turning the screws of the mouse bed.</li>
	<li>Place the mouse bed into the FMT with the red indicator first (&#8220;head-first&#8221;). This is important for the image fusion to be consistent with the &#181;CT.</li>
	<li>Close the FMT.</li>
	<li>Select the correct study group and subject. Select the required channel of the FMT (for OsteoSense 750EX, use the 750 nm channel).</li>
	<li>Add a description, e.g., &#8220;up&#8221; or &#8220;down&#8221; and acquire an overview scan by pressing &#8220;Capture&#8221;. This captures a reflectance image of the entire field of view. Make sure to not have &#8220;Reflectance Images Only&#8221; selected, because otherwise it is not possible to acquire 3D scans subsequently.</li>
	<li>Adjust the imaging parameters for the 3D scan. Enlarge the field of view to include as much as possible of the mouse. Usually, the head and tail will not entirely fit into the field of view, however. Click &#8220;Advanced&#8221; and check the imaging settings. Set sampling density to 3 mm, sensitivity to normal and illumination min/max to 5000 and 50000, respectively.</li>
	<li>Click &#8220;add to reconstruction queue&#8221; and then click &#8220;Scan&#8221; to start the FMT scan. This will take around 5 to 15 min, depending on the size and thickness of the mouse, because longer exposure times are required for thicker objects. The device contains a heated imaging chamber to avoid hypothermia.</li>
	<li>After the scan, flip the mouse bed including the mouse upside down and acquire another scan. This provides additional data for the fluorescence reconstruction.</li>
	<li>When the &#181;CT and the FMT-scans are completed and the mouse wakes up from the anesthesia, do not leave it unattended until it has regained sufficient consciousness, e.g., to walk around or to maintain sternal recumbency.</li>
</ol>
6. Image Fusion and Reconstruction
NOTE: After completion of the &#181;CT-FMT scanning, e.g., at the end of the study, the acquired data needs to be sorted to enable the automated image fusion and fluorescence reconstruction.
<ol>
	<li>To sort the scans for further processing, create a folder for the study. For each &#181;CT-FMT scan, create one subfolder whose name contains the mouse ID and the time point, e.g., M01_02h.</li>
	<li>For each &#181;CT-FMT scan, export the FMT scans (up and down) as .fmt files and save them into the subfolder using filenames ending with either &#8220;_up.fmt&#8221; or &#8220;_down.fmt&#8221;. Each .fmt file contains the acquired raw data, i.e., the excitation and emission images acquired by the camera, the metadata, such as the exposure times, and the fluorescence reconstruction generated by the FMT.</li>
	<li>Using the &#181;CT software, create a reconstruction with isotropic voxel size 35 &#181;m. Select a smooth reconstruction kernel (T10). Adjust the field of view so that the entire mouse bed including the markers is covered. Select MIFX/RAW as output format and start the reconstruction. After the reconstruction is done, move the &#181;CT reconstruction files into the subfolder of the &#181;CT-FMT scan.</li>
	<li>Export the &#181;CT and FMT data for all scans. Make sure that each subfolder contains two .fmt files (up and down) and the &#181;CT reconstruction in the MIFX/RAW format. To check for completeness select Menu-&#62;CT-FMT-&#62;Check Completeness using the Imalytics Preclinical software. A list of errors might appear, such as missing .fmt files or &#181;CT reconstructions. Fix the errors and check for completeness until all errors are resolved.</li>
	<li>Using Menu-&#62;CT-FMT-&#62;Settings, check the server name of the Definiens software and adjust if necessary. The default is http://localhost:8184, assuming that the Definiens software is installed on the same computer. The Definiens software is required in the next step to perform the automated segmentation of the mouse bed and markers.</li>
	<li>Click Menu-&#62;CT-FMT-&#62;Fuse group in the Imalytics Preclinical software to perform automated &#181;CT-FMT fusion for the entire study. This takes a few minutes per &#181;CT-FMT scan and results in a folder with suffix &#8220;Package&#8221; parallel to the study folder. This contains a smaller subset of files (the &#181;CT data and the fused vendor-provided FMT reconstruction) that are relevant for further analysis.</li>
	<li>Click Menu-&#62;CT-FMT-&#62;Reconstruct group (FMT) in the Imalytics Preclinical software to perform the fluorescence reconstruction including the generation of absorption and scattering maps13. Even though the processing is GPU-accelerated20, each reconstruction requires 1 to 4 hr depending on the size of the mouse. The results will be stored in the package folder. Note: To enable a higher throughput, we currently perform these reconstructions on a GPU cluster with 56 GPUs.</li>
</ol>
7. Image Analysis
NOTE: To extract quantitative measurements from the image data, segmentation of lesions and organs is required.
<ol>
	<li>Once all data sets are fused and reconstructed, create a segmentation for each &#181;CT-FMT scan using the Imalytics Preclinical software.</li>
	<li>Load a &#181;CT file as underlay and the fluorescence file as overlay. Press &#8220;3D&#8221; to turn on the volume rendering and inspect the data set.</li>
	<li>To segment the lung, click Menu-&#62;Classes-&#62;Add class and create a class named &#8220;tmp&#8221;. This can also be done through the context menu. Creating a new class automatically sets it as output class for subsequent segmentation operations.
	<ol>
		<li>Perform a thresholding operation to segment all regions with low intensity in the &#181;CT data set (click Menu-&#62;Segmentation-&#62;Thresholding-&#62;Below and enter 600). Now the tmp class contains the air outside the mouse but also the lung tissue.</li>
		<li>Create a &#8220;Lung&#8221; class. Perform a &#8220;Fill Region&#8221; operation (right-click into the lung and select Menu-&#62;Fill Region-&#62;Fill region unlimited), to separate the lung from the outer air.</li>
		<li>Delete the tmp class because it is not needed anymore.</li>
	</ol>
	</li>
	<li>To segment convex regions, e.g., the bladder, use the scribble mode. First create a class &#8220;Bladder&#8221;.
	<ol>
		<li>Press F1 to delete all scribbles.</li>
		<li>Using the computer mouse, draw scribbles to delineate the boundaries of the bladder.</li>
		<li>Press F3 to fill the region enclosed by the scribbles with a temporary mask which appears as red overlay. Iteratively add more scribbles (in any slicing orientation) and press F3 until a sufficient accuracy is achieved. Typically, scribbles in 10 slices are sufficient.</li>
		<li>Press F4 to store the temporary mask as &#8220;Bladder&#8221;.</li>
		<li>Proceed like this to segment other convex regions such as the heart and kidneys. Many regions, e.g., the stomach or the liver, can be approximated by a few convex regions.</li>
	</ol>
	</li>
	<li>To segment the spine, first create a &#8220;Bone&#8221; class.
	<ol>
		<li>Select ContextMenu-&#62;Thresholding-&#62;Above to perform a thresholding operation to classify all bright voxels (e.g., above 1,600) as &#8220;Bone&#8221;.</li>
		<li>A region filling operation would fail to segment the spine because it is connected with many other parts of the skeleton, e.g., the ribs. Perform a few cutting operations by iteratively drawing scribbles and pressing F2 to separate the spine from the skull, the ribs, and the sacral bone.</li>
		<li>Finally, create a class &#8220;Spine&#8221; and perform a region filling operation to get the spine (right-click into the spine and select ContextMenu-&#62;Fill Region-&#62;Fill region unlimited).</li>
	</ol>
	</li>
	<li>Save the segmentation as a file inside the subfolder of the &#181;CT-FMT scan. Use a consistent name, e.g.,&#160;organs.seg, to enable the batch processing.</li>
	<li>Select Menu-&#62;Statistics-&#62;Class statistics (overlay), to generate a spreadsheet which contains the mean fluorescence intensity, the volume and the total amount (product of mean and volume) for each class.</li>
	<li>To generate a single spreadsheet containing the values for all regions of all &#181;CT-FMT scans, click Menu-&#62;Batch-&#62;Set batch settings and then click Menu-&#62;Batch-&#62;Batch statistics. This avoids the effort of creating and merging many spreadsheet files, i.e., one for each &#181;CT-FMT scan.</li>
</ol>
8. Probe Calibration
<ol>
	<li>To calculate the calibration factor for a probe, multiple &#181;CT-FMT phantom scans with different known amounts of the probe are required (see step 1.4), e.g., with 100 pmol, 50 pmol, 25 pmol and 0 pmol.</li>
	<li>Scan the phantoms as described in sections 4 and 5. Also for phantom scans, up and down scans in the FMT are required.</li>
	<li>Export the data and perform fusion and reconstruction as described in section 6.</li>
	<li>Segment the inclusion using the &#181;CT data for each scan by thresholding (above 1,200) and region filling.</li>
	<li>Generate a spreadsheet with the measured fluorescence amounts and plot them as function of the known amounts. Compute the slope of a linear regression fit. This is the calibration factor for the probe.</li>
</ol>

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S. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=AMIDE:+a+free+software+tool+for+multimodality+medical+image+analysis.">AMIDE: a free software tool for multimodality medical image analysis.</a> Molecular Imaging. 2 (3), 131-137 (2003).</li><li>Gremse, F., Schulz, V. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Qualitative+and+Quantitative+Data+Analysis.">Qualitative and Quantitative Data Analysis.</a> Small Animal Imaging. , 363-378 (2011).</li><li>Baiker, M., Milles, 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=Atlas-based+whole-body+segmentation+of+mice+from+low-contrast+Micro-CT+data.">Atlas-based whole-body segmentation of mice from low-contrast Micro-CT data.</a> Medical Image Analysis. 14 (6), 723-737 (2010).</li><li>Sevick-Muraca, E. M., Rasmussen, J. C. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=Molecular+imaging+with+optics:+primer+and+case+for+near-infrared+fluorescence+techniques+in+personalized+medicine.">Molecular imaging with optics: primer and case for near-infrared fluorescence techniques in personalized medicine.</a> Journal of Biomedical Optics. 13 (4), 041303 (2008).</li></ol>

Felix Gremse is founder and owner of Gremse-IT, a startup company that offers software and services for medical image analysis in co-operation with Philips and the Department for Experimental Molecular Imaging of the RWTH Aachen University.

We describe and apply a protocol for multimodal &#181;CT-FMT imaging. We use commercially available and widely used FMT and &#181;CT devices3,11,15&#8211;17,21. While the protocol requires a specific FMT, the &#181;CT can be replaced by another &#181;CT with similar functionality and comparable scanning parameters, e. g., the field of view should be large enough to cover the mouse bed including the markers.
The FMT has been used for biodistribution analysis without combining it with &#181;CT or MRI21, however, the anatomical data is beneficial to increase the reproducibility because the segmentation can be based on the organ boundaries which are visible in the &#181;CT data10. While integrated &#181;CT-FMT devices have been developed2,7, these are not commercially available yet. Furthermore, the use of two separate devices allows piping, i.e., the next mouse can be imaged in the &#181;CT while the first mouse is still in the FMT, to increase the throughput.
To reduce the manual workload, we perform automated marker detection and fusion. Furthermore, the mouse shape is automatically segmented and this information significantly improves the fluorescence reconstruction11,13,22. For quantitative fluorescence reconstruction, absorption and scattering maps are needed13,23. We derive the scattering map by automated segmentation of the &#181;CT data and assigning known scattering coefficients of several tissue types (lung, bone, skin, fat, and remaining soft tissue)24. Subsequently, we reconstruct an absorption map from the optical raw data which is particularly important for well-perfused organs such as the heart and the liver13,20.
Scanning several mice at multiple time points quickly results in a large number of data sets to be analyzed. For biodistribution studies, several organs need to be segmented for each &#181;CT-FMT scan. Unfortunately, the segmentations cannot be reused, because the mouse is newly positioned into the mouse bed repeatedly. We use a tool for interactive segmentation, developed at our institute, however, other tools might also be appropriate25. We generate voxel-wise segmentations, because these match better to complex organs than simple shapes such as ellipses and cubes26. Automated whole-animal segmentation would be useful to further reduce the manual workload27, but an interactive segmentation tool would still be required to correct for segmentation errors. Furthermore, automated segmentation tools can hardly anticipate special cases such as pathologies correctly. Since we use native &#181;CT scans, some organs such as the spleen are very difficult to segment even manually. Contrast agents would help, but there are problems with tolerability and it is difficult to maintain a steady contrast agent distribution throughout the longitudinal imaging.
Our phantom study shows that the signal localization is improved when using the shape information for fluorescence reconstruction. In vivo, a similar improvement is evident for the early time point (15 min&#160;after injection), when a large amount of the probe is already in the urinary bladder. The hydroxyapatite-binding probe accumulates at bones and joints. It is remarkable how fast this occurs, i.e., the signal is already clearly visible at the spine 15 min&#160;after injection. This probably is caused by the low molecular weight of the probe, which enables fast extravasation and diffusion to the target regions. The probe binds covalently to its target hydroxyapatite and the unbound probe is excreted. For the later time points, between 6 h and 24 h after injection, the signal intensity in the spine remains relatively stable, probably, because hardly any light reaches deep into the mouse to bleach the fluorescence. For our study, we used the 750 nm channel, which results in low background fluorescence as evident for the scans acquired before injection. At lower wavelengths, more background signal can be expected28.
In summary, we describe a multimodal imaging protocol for commercially available FMT and &#181;CT devices. We show that the combination provides benefits for fluorescence reconstruction. We illustrate how the biodistribution curves are extracted from the large amount of image data by means of interactive organ segmentation and batch processing. We believe that this standardized workflow can be helpful for drug development and other imaging studies using fluorescently labeled probes.

Fluorescence-mediated tomography, also called fluorescence molecular tomography (FMT), is a promising technique to quantitatively assess the fluorescence distribution in diffuse tissues, such as anesthetized mice or even human body tissues, e.g., breasts or finger joints. In contrast to non-invasive microscopy techniques, which allow imaging of superficial targets at subcellular resolution1, FMT allows three-dimensional reconstruction of fluorescent sources in depths of several centimeters, albeit at lower resolution2. Many targeted fluorescent probes are available to image angiogenesis, apoptosis, inflammation, and others2&#8211;5. Some probes are activatable, e.g., by specific enzyme cleavage leading to unquenching of fluorochromes. Moreover, reporter genes expressing fluorescent proteins can be imaged, e.g., to track tumor cell migration6.
FMT strongly benefits from the combination with an anatomical modality, e.g., &#181;CT2,7 or MRI8. While stand-alone FMT devices are commercially available9, the fluorescence images are difficult to interpret without anatomical reference information. Recently we were able to show, that the fused anatomical image data enables a more robust analysis10. The anatomical data can also be used to provide prior knowledge, such as the outer shape of the mouse, which is important for accurate optical modeling and fluorescence reconstruction11. Furthermore, optical scattering and absorption maps can be estimated using segmentation of tissue types and by assigning class specific coefficients12,13. For near-infrared light, hemoglobin is the main absorber in mice, besides melanin and fur14. Since the relative blood volume varies regionally by orders of magnitude, an absorption map is particularly important for quantitative fluorescence reconstruction13.
One advantage of using non-invasive imaging devices is that the mice can be imaged longitudinally, i.e., at multiple time points. This is important to assess the dynamic behavior of probes, i.e., their target accumulation, biodistribution and excretion10,15, or to assess the disease progression16. When imaging several mice at multiple time points, a large amount of image data sets arises. To enable comparability, these should be acquired in a systematic way, i.e., with a well-defined and documented protocol. The large number of scans poses a challenge for image analysis, which is required to extract quantitative measurements from the image data.
The purpose of our study is to provide a detailed description of a &#181;CT-FMT imaging protocol which we used and optimized throughout several studies10,13,15,17,18. We describe how the data sets are generated, processed, visualized, and analyzed. This is demonstrated using an established molecular probe, OsteoSense, that binds to hydroxyapatite19, and can be used to image bone diseases and remodeling2. All procedures involving animals were approved by the governmental review committee on animal care.

<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

Fluorescence-mediated tomography (FMT) enables longitudinal and quantitative determination of the fluorescence distribution in vivo and can be used to assess the biodistribution of novel probes and to assess disease progression using established molecular probes or reporter genes. The combination with an anatomical modality, e.g., micro computed tomography (&#181;CT), is beneficial for image analysis and for fluorescence reconstruction. We describe a protocol for multimodal &#181;CT-FMT imaging including the image processing steps necessary to extract quantitative measurements. After preparing the mice and performing the imaging, the multimodal data sets are registered. Subsequently, an improved fluorescence reconstruction is performed, which takes into account the shape of the mouse. For quantitative analysis, organ segmentations are generated based on the anatomical data using our interactive segmentation tool. Finally, the biodistribution curves are generated using a batch-processing feature. We show the applicability of the method by assessing the biodistribution of a well-known probe that binds to bones and joints.

We applied the described protocol to assess the biodistribution of a targeted probe, OsteoSense, which binds to hydroxyapatite. 3 mice (C57BL/6 Apoe-/- Ahsg-/- double knockout mice, 10 weeks old) were imaged before and 15 min, 2 h, 4 h, 6 h, and 24 h after i.v. injection of 2 nmol OsteoSense. Our software automatically detected the markers built into the multimodal mouse bed (Figure&#160;1, Figure&#160;2A,B), which enabled fusion of the anatomical &#181;CT data with the fluorescence reconstruction performed by the FMT (Figure&#160;2C,D). Since OsteoSense is a probe with a low molecular weight, a fast renal excretion and therefore high signal in the urinary bladder is expected. Fusion of the fluorescence reconstruction of the FMT revealed problems such as misplaced signal outside the bladder (Figure 2C,D). These problems occur because the FMT does not know the true shape of the mouse and assumes a block shape. Our reconstruction determines the accurate shape from the &#181;CT data and generates scattering and absorption maps13 in order to enable a more accurate fluorescence reconstruction with better signal localization, which is particularly evident for the bladder (Figure&#160;2E,F).
To assign the reconstructed fluorescence to appropriate regions, we interactively segmented several organs using our software (Figure&#160;3). For each of the 18 scans, 7 regions were segmented based on the &#181;CT data, i.e., heart, lung, liver, kidneys, spine, intestine and bladder. Subsequently, the software was used to compute the mean fluorescence concentration for each of the 126 regions. Fortunately, the software provides a batch mode, which computes all the values and saves them in a single spreadsheet.
To visualize the fluorescence distribution, 3D renderings were generated for each time point, using comparable windowing setting&#160;(Figure&#160;4A-F). Using the quantified organ values, the biodistribution was computed by averaging the organ values over the three mice (Figure&#160;4G). The pre scans, acquired before injection, showed negligible background signal. 15 min&#160;after injection, the strongest signal appeared in the urinary bladder, because of the fast renal excretion. At the subsequent time points, the remaining probe had accumulated at bones and joints.
<img alt="Figure 1" src="/files/ftp_upload/52770/52770fig1.jpg" /> 
Figure 1.&#160;Multimodal Mouse Bed. (A) The multimodal mouse bed contains two acrylic glass plates that tightly hold the mouse. The tightening is adjusted using two screws. The mouse bed contains markers (empty holes) for image fusion. Anesthetic gas is supplied using a flexible tube which is fixated with tape. (B) The mouse bed is attached to a metal holder and held in the center of the rotating &#181;CT gantry. (C) Avoid a gap between mouse bed and the metal holder, because otherwise, the markers may be incorrectly assigned leading to incorrect fusion. The anesthetic gas tube should be attached to the tube connector. (D) The mouse bed should be inserted into the FMT with the front first to enable a correct automated fusion. (E) The markers are visible to the FMT camera, which is used for the automated marker detection and fusion. <a href="https://www.jove.com/files/ftp_upload/52770/52770fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" src="/files/ftp_upload/52770/52770fig2.jpg" /> 
Figure 2. Image Fusion and Reconstruction. (A, B) Markers and the outer shape of the mouse are determined by the automated segmentation algorithm. (C, D) 15 min&#160;after injection of OsteoSense, a considerable amount of the probe has already been excreted into the urinary bladder. After fusing the vendor-provided reconstruction with the &#181;CT data, problems become visible. Most of the signal appears around the bladder but not inside the bladder and some signal even appears in the air. This happens because the FMT assumes a block-shaped mouse. (E, F) Our improved fluorescence reconstruction, using the shape of the mouse derived from the &#181;CT data, results in better localization of the fluorescence inside the bladder. <a href="https://www.jove.com/files/ftp_upload/52770/52770fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" src="/files/ftp_upload/52770/52770fig3.jpg" /> 
Figure 3. Interactive Organ Segmentation. (A) To quantify the fluorescence distribution, several organs are segmented: heart (red), lung (pink), liver (brown), stomach (beige), spine (purple), kidneys (yellow), intestine (green), and urinary bladder (gold). (B) The lung, which is strongly contrasted compared with the surrounding tissue, is segmented using thresholding and region filling. (C) Round organs, such as the bladder, kidneys, and heart are segmented using &#8220;scribbles&#8221;. (D) Organs with a more complex shape, e.g., liver and stomach are segmented incrementally using scribbles. To segment the spine, a high threshold is applied to segment all bones. Then some bones, e.g., the ribs, are cut away, until the spine remains. <a href="https://www.jove.com/files/ftp_upload/52770/52770fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" src="/files/ftp_upload/52770/52770fig4.jpg" /> 
Figure 4. Biodistribution. To assess the biodistribution, the mice are scanned at several time points (A-F). (A) The pre scan, before injection, shows little background signal in the 750 nm channel. (B) 15 min&#160;after injection, a considerable amount of the probe is already in the urinary bladder. (C) At the 2 h time point, the mouse had urinated, which results in some fluorescence outside the mouse. At later time points (D-F), the signal appears predominantly at the bones and joints, i.e., at the spine and the knees. (G) The quantified fluorescence concentration is shown for selected organs.

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

Hybrid  &amp;#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.

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

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

Research

JoVE Journal

Bioengineering

13.1K visualizações.  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.

Vídeo: Hybrid  &#181;CT-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.

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

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.

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.

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

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.

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

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.

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.

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.

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.

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

High-Dimensionality Flow Cytometry for Immune Function Analysis of Dissected Implant Tissues

The success of implanting laboratory-grown tissue or a medical device in an individual is subject to the immune response of the recipient host. Considering an implant as a foreign body, a hostile and dysregulated immune response may result in the rejection of the implant, while a regulated response and regaining of homeostasis can lead to its acceptance. Analyzing the microenvironments of implants dissected out under in vivo or ex vivo&#160;settings can help in understanding the pattern of immune response, which can ultimately help in developing new generations of biomaterials. Flow cytometry is a well-known technique for characterizing immune cells and their subsets based on their cell surface markers. This review describes a protocol based on manual dicing, enzymatic digestion, and filtration through a cell strainer for the isolation of uniform cell suspensions from dissected implant tissue. Further, a multicolor flow cytometry staining protocol has been explained, along with steps for initial cytometer settings to characterize and quantify these isolated cells by flow cytometry.

Isolation of cells from dissected implants and their characterization by flow cytometry can significantly contribute to understanding the pattern of immune response against implants. This paper describes a precise method for the isolation of cells from dissected implants and their staining for flow cytometric analysis.

The success of implanting laboratory-grown tissue or a medical device in an individual is subject to the immune response of the recipient host. ...

Area-based Image Analysis Algorithm for Quantification of Macrophage-fibroblast Cocultures

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either fluorescence detection approaches, such as immunofluorescent staining or transfection with fluorescent proteins or edge detection techniques, which are often error-prone due to noise and other non-idealities in the image background. 
We designed a new algorithm that could accurately count and distinguish macrophages and fibroblasts, cells of different phenotypes that often colocalize during tissue regeneration. MATLAB was used to implement the algorithm, which differentiated distinct cell types based on differences in height from the background. A primary algorithm was developed using an area-based method to account for variations in cell size/structure and high-density seeding conditions. 
Non-idealities in cell structures were accounted for with a secondary, iterative algorithm utilizing internal parameters such as cell coverage computed using experimental data for a given cell type. Finally, an analysis of coculture environments was carried out using an isolation algorithm in which various cell types were selectively excluded based on the evaluation of relative height differences within the image. This approach was found to accurately count cells within a 5% error margin for monocultured cells and within a 10% error margin for cocultured cells.

We present a method, which utilizes a generalizable area-based image analysis approach to identify cell counts. Analysis of different cell populations exploited the significant cell height and structure differences between distinct cell types within an adaptive algorithm.

Quantification of cells is necessary for a wide range of biological and biochemical studies. Conventional image analysis of cells typically employs either ...