This study was approved by the Institutional Review Board of our institution (approval number: 20150128).

All methods described here have been approved by the Institutional Review Board of Keio University School of Medicine.
NOTE: Joint kinematics are measured by reconstructing the motion of a moving bone around a fixed bone. For knee joint kinematics, the femur is defined as the fixed bone and the tibia is defined as the moving bone.
1. CT imaging protocol
<ol>
	<li>Set up the CT machine. Acquire CT examinations with a 320-detector-row CT system to allow for multiple phases of 3D volume data with 160 mm craniocaudal coverage. For example, in the analysis of knee kinematics, the image acquisition consists of 51 volume scans with a rotation time of 0.275 s, and all images are reconstructed using half reconstruction, so that the temporal resolution is approximately 0.16 s.</li>
	<li>Use the following scanning parameters: peak tube voltage = 100 kVp; tube current = 40 mA; scan coverage = 160 mm; matrix size= 512 x 512 pixels; and reconstruction section thickness and section interval = 0.5 mm.</li>
	<li>Place the target joint of the subject inside the CT gantry in the starting position of the 4DCT exam (Figure 1).</li>
	<li>Before the CT exam, rehearse movements of the joint from the start position to the end position within the required examination time. Ask the subject to move the joint during the 10.275 s scan time and obtain a series of volume data. Store the sequential volume data in DICOM format.</li>
	<li>Perform static 3DCT of all the target bones and store the data in DICOM format.</li>
</ol>
2. Surface reconstruction
<ol>
	<li>Perform semiautomatic segmentation of 3DCT data (Figure 2A).
	<ol>
		<li>Load CT DICOM data by selecting all DICOM files of the static 3DCT data.</li>
		<li>Open the label field by clicking Edit New Label Field and check which threshold CT attenuation value is appropriate to extract cortical bone from the source bone. Select materials with CT attenuation values above the threshold. For example, the bone cortex threshold for a young subject is set as 250. Check the label for bone cortex selection and manually modify the demarcation using an editing tool for consistency with the shape of the bone.</li>
		<li>Generate the surface data (triangle meshes) from the labeled bone cortex position data (point cloud in the software). Store the surface data by exporting data in Standard Triangulated Language (STL) format.</li>
		<li>Click Generate Surface|Apply on the label of the cortical bone. Click File|Export Data As|STL Binary Little Endian to save the surface data in STL format.</li>
	</ol>
	</li>
	<li>Perform automatic segmentation of 4DCT volume data (Figure 2B). 
	NOTE: Each frame of the DICOM data includes the distribution of the CT attenuation values in the CT gantry.
	<ol>
		<li>Set the threshold of the bone cortex as in static CT, and extract geometric data showing CT attenuation values above the threshold from all 51 frames of the 4DCT data using the DICOM reading module in the programming software. Adjust the threshold according to the bone density of the source bone. For example, for the osteoporotic bone, set the threshold lower.</li>
		<li>Translate all positional data that have already been obtained in the previous step into a format that can be interpreted by image processing software (e.g., Avizo). In the image processing software, reconstruct all surface data of the point cloud with higher CT attenuation values than the threshold for all 4DCT frames using a batch processing script. The image processing software contains the function to read the script and export the surface data from the DICOM series data automatically. The batch script is shown in the Supplemental Coding File.</li>
	</ol>
	</li>
</ol>
3. Image registration
NOTE: In this step, reconstruct the motions of the moving bone with respect to the fixed bone from the raw 4DCT DICOM data.
<ol>
	<li>Perform surface registration from static 3DCT to the first frame of the 4DCT.
	<ol>
		<li>Trim the bones in a static 3DCT into partial segment data that are included in all frames of 4DCT for use with the iterative closest point (ICP) algorithm11 in the 3D mesh editing software using the Selecting Face function (Figure 3A) by referring 4DCT movie data. The surface data from 4DCT are only partial segments that are included in each volume imagebecause surface registration requires that one surface data point is included in another surface.</li>
		<li>Pick three landmarks in the fixed and moving bones that can be easily identified from the trimmed 3DCT surface and the surface data of the first frame of 4DCT in the 3D mesh editing software using the PickPoints function (Figure 3B).</li>
		<li>Match the partial fixed and moving bones roughly on the first frame of the 4DCT surface data (Figure 3C) according to the picked landmarks in 3.1.2. Next, perform surface registration using the ICP algorithm11 using the open source software (e.g., VTK). 
		NOTE: This process provides homogeneous transformation matrices of the fixed and moving bones from the static 3DCT to the first frame of 4DCT (Figure 3D). These matrices are 4 x 4 matrices consisting of rotation and translation, as shown in Figure 4. The transformation matrix causing the reverse action can also be calculated.</li>
	</ol>
	</li>
	<li>Perform sequential surface registration (Figure 5).
	<ol>
		<li>Match the partial surfaces of the fixed and moving bone in the first 4DCT frame onto the surface data of the second frame. Next, match the partial surfaces of the ith frame onto the (i + 1)th frame of 4DCT sequentially. Repeat this process until the last frame of the 4DCT by programming with use of the ICP module in the open source software.</li>
	</ol>
	</li>
	<li>Calculate transformation matrices from the static 3DCT to all frames in 4DCT according to the results of 3.1 and 3.2.</li>
	<li>Reconstruct moving bone motion with respect to the fixed bone (Figure 6).
	<ol>
		<li>Reconstruct the kinematics of the moving bone with respect to the fixed bone from the matrices that represent the transformation from the static 3DCT to each 4DCT frame. Define the coordinate systems of the fixed and moving bones when the rotation parameters are measured (e.g., flexion angle or rotation angle calculated by the Euler/Cardan angle)12,13,14.</li>
	</ol>
	</li>
</ol>

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The authors have no competing financial interests.

Our method allows visualization and quantification of the motions of whole bones and provides numerical positional data of the moving bone with respect to the fixed bone from 4DCT data. Many tools have been suggested for measuring joint kinematics. Motion skin markers can analyze total body motions over a long time. However, this method contains skin motion errors3. Joint kinematics should be estimated from the motion of adjacent bones. The 2D-3D registration method uses fluoroscopy and infers 3D kinematics from sequential 2D images. Translational errors are still present, although the analysis software has evolved to account for this. Many cadaveric studies have measured joint kinematics by taking CT images in different cadaver positions19. However, these represent passive motions from sequential static 3D images, and thus differ qualitatively from active motions.
There are several critical steps in this protocol. The surface data from 3DCT should be created precisely because this quality affects the accuracy of the initial surface registration to the first frame of 4DCT. Around the joint area, the threshold for the bone cortex may be different from the bone shaft. Threshold adjustment will be needed when the border of the bone cortex is unclear. Once the surface registration of all frames is finished, reconstructed motion should be checked. If surface registration for one frame fails, automated surface registration can be restarted from the next frame by picking the landmarks in the next frame and repeating the protocol.
The 4DCT method provides sequential volume data with accuracy almost as high as static 3DCT because CT DICOM data contain absolute coordinate values of all tissues in the CT gantry. Several studies have used 4DCT for investigations of joint kinematics20,21. However, in most, the observers picked landmarks from several frames and calculated the parameters (e.g., angles, translation). These data analysis processes contain human error that leads to measurement error. Our method of surface registration provides high accuracy image matching. Once plotted, the landmarks for the parameters can be traced according to the shape of the surface in each frame. Theoretically, manual surface segmentation for all 4DCT frames provides the most accurate data, but this process is far too time intensive. Recently, 4DCT has been used for motion analysis for wrist joints because the carpal bones are small and overlapped structures22. There have been several reports about automated bone tracing23,24. Goto et al. analyzed finger motions using normalized correlation coefficients that detect the similarity between two images25. We used surface registration because the position of the bone cortex surface is the most important landmark to describe joint kinematics.
We used an iterative closest point algorithm to trace the motion of the surface data in all frames. An iterative closest point algorithm matches two groups of point clouds or surface data to minimize surface-to-surface distance11 but has several drawbacks. This algorithm is generally used to match two close surfaces. Therefore, when the two surfaces are located distant to each other, registration would occur in the &#39;local minimum&#39; position, not the true matched position26. We overcome this drawback by taking three landmarks in each bone at first. The two surfaces are roughly matched according to these three landmarks. From these two positions, the ICP serves as the closest position. The frame rate of 4DCT is very short (0.2 s), so the surface position in the current frame is close to the surface position in the next frame. In cases of slow joint motion, the rough match step will not be needed for further frame-to-frame sequential surface registration. In addition, the relationship between the entirety of the two bones is reproduced by matching the entire static 3DCT surface data onto the partial 4DCT frame surface data. Generally, the coordinate system of the bone is defined from its entirety12,27. Reconstruction of the whole bone motion thus contributes to the description of joint angles. This accuracy largely depends on surface registration of the partial surface onto the whole surface data. In the representative data, we demonstrated that the availability of over 10% of the segments provides sufficient accuracy for the knee joint.
CT data provide all positional data included within the CT gantry area. The quality of the data depends solely on the quality of the CT machine. This method can thus be applied to small bones or multiple bones such as the carpal bones, which are difficult to trace by 2D-3D registration.
Several limitations must be mentioned. First, ICP depends on the shape of the partial segment. ICP is more accurate when the surface has geometric features such as bone spurs or cortical edges. On the other hand, when the surface shape is symmetrical, such as the radial head or sesamoid, ICP will provide a wrong rotation of the original surface. In addition, ICP also depends on the quality of the surface data. In case of osteoporotic bones, surface reconstruction largely depends on manual segmentation. That may lead to interobserver errors. Recently, computerized tissue segmentation on CT slices has been developed. However, human manual segmentation is still considered more reliable when identifying specific tissues28,29. Although the quality of the CT image cannot be changed, other limitations can be overcome by manual surface segmentation and registration. Second, when the joint motion is too fast, this method cannot trace the bone motions, because the CT images become blurry30. Frame-to-frame surface registration then fails because the two surfaces are too distant. Tolerable velocity depends on the target joint, because the joint morphology affects the success rate of surface registration. Studies of velocity tolerance for each joint will be needed in the future. In addition, joint motion should be performed inside the CT gantry. Therefore, for analysis of loading kinematics, optic sensors or 2D-2D registration are best.

Joint kinematics have been described using a number of methodologies, such as motion capture sensors, 2D-3D registration, and cadaveric studies. Each method has specific advantages and disadvantages. For example, motion capture sensors can measure fast, large-scale motions using infrared cameras with or without sensors on the subject1,2. However, these methods measure skin motion to infer joint kinematics, and therefore contain skin motion errors3.
Cadaveric studies have been used to evaluate ranges of motion, instability, and contact areas4,5,6. This approach can measure small changes in small joints using CT or optical sensors attached directly to bone using pins or screws. Cadaveric models can mainly evaluate passive motions, although multiple actuators have been used to apply external forces to tendons to simulate dynamic motion7. Active joint motion can be measured by 2D-3D registration techniques, matching 3DCT images to 2D fluoroscopy images. Although the accuracy of the registration process remains controversial, the reported accuracy is generally high enough for large joint kinematics8,9. However, this method cannot be applied to small bones or multiple bones in narrow spaces.
In contrast, 4DCT is a dynamic CT method that obtains a series of volumetric data. Active joint motions can be analyzed using this approach10. This technology provides precise 3D positional data of all substances inside the CT gantry. The 3D joint motions are clearly visualized in a viewer. However, describing joint kinematics from such a series of volume data is still difficult, because all the bones are moving and no landmarks can be traced during the active motions in vivo.
We developed a method for 4DCT analysis that provides the in vivo joint kinematics of the whole bones around the joint during active motions. The aim of this article is to present our method, the sequential 3D-3D registration technique for 4DCT analysis, and show representative results obtained using this method.

<table border="1" fo:keep-together.within-page="1" fo:keep-with-next.within-page="always">
	<tbody>
		<tr>
			<td>4DCT scanner</td>
			<td>Canon medical systems (Tochigi, Japan)</td>
			<td>N/A</td>
			<td>4DCT scan, Static 3DCT scan</td>
		</tr>
		<tr>
			<td>AVIZO(9.3.0)*</td>
			<td>Thermo Fisher Scientific (OR, USA)</td>
			<td></td>
			<td>Image processing software. 
			Surface reconstruction from CT DICOM data and point cloud data. 
			* Ryan, T. M. &#38; Walker, A. Trabecular bone structure in the humeral and femoral heads of anthropoid primates. Anat Rec (Hoboken). 293 (4), 719-729, doi:10.1002/ar.21139, (2010).</td>
		</tr>
		<tr>
			<td>Meshlab**</td>
			<td>ISTI (Pisa, Italy)</td>
			<td>N/A</td>
			<td>Surface trimming and landmark picking 
			** MeshLab: an Open-Source Mesh Processing Tool. Sixth Eurographics Italian Chapter Conference, page 129-136, 2008. 
			P. Cignoni, M. Callieri, M. Corsini, M. Dellepiane, F. Ganovelli, G. Ranzuglia</td>
		</tr>
		<tr>
			<td>VTK(6.3.0)***</td>
			<td>Kitware (New York, USA)</td>
			<td>N/A</td>
			<td>Iterative Closest Points algorithm. Used in python language programming. 
			*** https://vtk.org</td>
		</tr>
		<tr>
			<td>Python(3.6.1)</td>
			<td>Python Software Foundation</td>
			<td>N/A</td>
			<td>DICOM file processing to extract the point cloud from the bone cortex (&#39;dicom.py&#39; module). 
			Calculation of the rotation matrices. (Numpy module) 
			Sequential image regestration using ICP algorithm</td>
		</tr>
	</tbody>
</table>

four-dimensional ct analysis using sequential 3d-3d registration

Four-dimensional computed tomography (4DCT) provides a series of volume data and visualizes joint motions. However, numerical analysis of 4DCT data remains difficult because segmentation in all volumetric frames is time-consuming. We aimed to analyze joint kinematics using a sequential 3D-3D registration technique to provide the kinematics of the moving bone with respect to the fixed bone semiautomatically using 4DCT DICOM data and existing software. Surface data of the source bones are reconstructed from 3DCT. The trimmed surface data are respectively matched with surface data from the first frame in 4DCT. These trimmed surfaces are sequentially matched until the last frame. These processes provide positional information for target bones in all frames of the 4DCT. Once the coordinate systems of the target bones are decided, translation and rotation angles between any two bones can be calculated. This 4DCT analysis offers advantages in kinematic analyses of complex structures such as carpal or tarsal bones. However, fast or large-scale motions cannot be traced because of motion artifacts.

We describe the motion of the tibia during knee extension. The knee joint was positioned in the CT gantry. A triangle pillow was used to support the femur at the starting position. The knee was extended to a straight position over the course of 10 s. Radiation exposure was measured. In addition to 4DCT, static 3DCT of the whole femur, tibia, and patella was performed. Surface data of the whole femur and tibia were reconstructed. The threshold for HU numbers of the bone cortex was set as 250 HU and the surface data of all 51 frames were reconstructed.
The femur and tibia were trimmed into partial surface data that were included in all 4DCT framesby visually checking 4DCT movie data, which is created in the preset 4DCT software. In the static 3DCT surfaces and the first frame of the 4DCT, the landmarks of each segment were plotted. On the femur, the medial and lateral epicondyles and intercondylar notch were identified. On the tibia, the medial and lateral ends of the joint surface and tibial tuberosity were also identified as corresponding landmarks. Partial surface data of the femur and tibia were roughly matched with the first frame of the 4DCT data according to these three landmarks. These surfaces were then completely matched using the ICP algorithm.
Partial segments of the femur and tibia of the first frame were matched with the whole surface in the second frame. Partial fragments in the ith frame were thus matched with the whole surface data of the (i + 1)th frame sequentially. In the ICP algorithm, the convergence criteria for mean distance between iterations was set as 0.01 mm.
The femur was defined as the fixed bone and the tibia as the moving bone. The 4 x 4 matrix that describes translation and rotation from the global coordinate system in the original CT DICOM data to the local coordinate system of the fixed bone is calculated. The coordinate system of the femur and tibia were defined in accordance with a previous report15. We calculated the motion of the tibia from the Euler/Cardan angles in &#39;zxy&#39; order, meaning flexion, varus, and internal rotation, in that order14.
Our method depends on the accuracy of image registration from the partial segments onto the whole surface data. We validated the accuracy of partial surface registration by decreasing the length of the femur and tibia incrementally by 1% along the long axis from 20%-1%. Surface registration of partial segments to the whole bones was performed for the entire set of lengths of the femur and tibia, and errors for rotation and translation from the parameters calculated from the whole bones were evaluated.
Results showed that the varus angle of the tibia gradually decreased as the tibia was extended (Figure 7). Tibial external rotation increased at the end of the extension. This external rotation corresponds with the &#34;screw home movement&#34; of the knee in previous reports16,17.
The effective dose estimate for this CT protocol was 0.075 mSv, as determined by the dose length product measurement (187.5 mGy&#8729;cm) and appropriate normalized coefficients (0.0004) as reported in the literature18.
In validation, graphs of the error for translation and rotation show that the error was tolerable for femur lengths longer than 9% of the whole length and tibia lengths longer than 7% of the whole length (Figure 8). At 10% of the length of the femur and 8% of the length of the tibia, errors were 0.02&#176; for varus/valgus rotation, 0.02&#176; for internal/external rotation, 0.01&#176; for extension/flexion rotation, 0.10 mm for anterior/posterior translation, 0.14 mm for proximal/distal translation, and 0.11 mm lateral/medial translation. These translation errors are considered negligible because CT slice thickness is 0.5 mm and exceeds the error size. Internal and external rotation errors tended to fluctuate. This was thought to be caused by local minimum fit for iterative rotation along the long axis due to the symmetrical shape of the tibial joint surface.
As additional data, patellar kinematics were also calculated using the same method. We demonstrated the lateral tilt of the patella by tracking the norm of the patellar surface corresponding to the knee flexion angle as calculated from analysis of the tibia (Supplemental Figure 1).
<img alt="Figure 1" class="xfigimg" src="/files/ftp_upload/59857/59857fig1.jpg" /> 
Figure 1: Acquisition of 4DCT. The 4DCT examination for knee extension. The subject is instructed to lie down and position the knee in the CT gantry. At the starting position, the knee is set in the flexed position and extended within 10 s after the examination starts. In this figure, the subject extends the knee from 60&#176; of flexion to maximum extension in 10 s. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59857/59857fig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 2" class="xfigimg" src="/files/ftp_upload/59857/59857fig2.jpg" /> 
Figure 2: Reconstruction of surface data. (A) Surface data of the whole femur (fixed bone) and whole tibia (moving bone) are reconstructed. (B) Using DICOM data from 4DCT, the positional data of the bone cortex showing CT attenuation values above the threshold are extracted in each frame. These positional data are input into the software and the surface data of all frames are reconstructed. The femur also moves (green arrow) with respect to the tibia (blue arrow). <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59857/59857fig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 3" class="xfigimg" src="/files/ftp_upload/59857/59857fig3.jpg" /> 
Figure 3: Surface registration. (A) Surface data of the fixed and moving bones from 3DCT are trimmed into partial segments that are included in all 4DCT frames because the surface data from 4DCT are only partial segments, which are included in the CT gantry. (B) Three landmarks are picked in the partial segments of the static 3DCT and the first frame of the 4DCT. (C) The partial segments are matched with the first frame according to the landmarks. (D) The iterative closest point (ICP) algorithm is applied to match surface data. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59857/59857fig3large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 4" class="xfigimg" src="/files/ftp_upload/59857/59857fig4.jpg" /> 
Figure 4: Transformation matrix is calculated from surface registration. (A) Translation and rotation of surface data can be described in a 4 x 4 matrix (homogeneous transformation matrix). Mref represents the matrix of the fixed bone and Mobj represents the matrix of the moving bone. The lower right value represents the starting position and the upper left value represents the target position. For example, 1Mrefs translates the fixed bone in the static 3DCT position to the fixed bone in the first frame of 4DCT. (B) The rotation matrix is a 4 x 4 matrix. R3 is a 3 x 3 matrix that defines rotation and d is a 1 x 3 matrix that defines translation. tR3 is a transverse matrix of R3. (C) The upper right &#34;inv&#34; means the reverse action matrix. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59857/59857fig4large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 5" class="xfigimg" src="/files/ftp_upload/59857/59857fig5.jpg" /> 
Figure 5: Steps of sequential surface registration of all frames. The difference between ith and (i + 1)th frames is very small. Partial segments of the ith frame can be matched with whole surface data of the (i + 1)th frame only by the ICP algorithm. Surface registration is repeated sequentially until the last frame. The transformation matrix from the static 3DCT to each frame (iMs) is calculated. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59857/59857fig5large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 6" class="xfigimg" src="/files/ftp_upload/59857/59857fig6.jpg" /> 
Figure 6: Rotation angles are calculated using the defined coordinate systems of the fixed and moving bones. (A) The coordinate system of the fixed bone is defined15. The rotation matrices from static 3DCT to the local coordinate system of the fixed bone (LMrefS) are calculated. (B) The coordinate system of the moving bone is defined and drawn over the fixed bone in its local coordinate system15. Rotation matrices from the local moving bone to the local coordinate system of the fixed bone are calculated (Mi). From these matrices, angles of the moving bone with respect to the fixed bone are calculated using the Euler/Cardan angle. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59857/59857fig6large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 7" class="xfigimg" src="/files/ftp_upload/59857/59857fig7.jpg" /> 
Figure 7: Representative results show kinematics of the tibia during knee extension. (A) Extension of the tibia. From the starting frame, the tibia is extended almost constantly and extension speed increases around the end frame. (B) Tibial internal rotation. The transverse axis is the tibial extension angle. The tibia rotates internally to 10&#176; of flexion and rotates externally until the end frame. (C) The valgus angles increase constantly during all frames of knee extension. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59857/59857fig7large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Figure 8" class="xfigimg" src="/files/ftp_upload/59857/59857fig8.jpg" /> 
Figure 8: Validation of surface registration of the partial segment onto the whole bone. The lengths of the femur and tibia are decreased incrementally by 1% along the long axis from 20%-1%. Surface registration of partial segments to whole bones are performed for all sets of lengths of the femur and tibia, and errors for rotation and translation from the parameters calculated from the whole bones are evaluated. Perturbation analysis is shown in Supplemental Figure 2. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59857/59857fig8large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Supplemental Figure 1" src="/files/ftp_upload/59857/59857suppfig1.jpg" /> 
Supplemental Figure 1: Patellar kinematics during knee extension. Patellar kinematics are also calculated using the same method. (A) A surface was fit on the surface data of the patella. The norm of the surface pointing anteriorly is calculated. The lateral tilt is defined as the lateral tilting angle of the norm in the coordinate system of the femur. (B) The patellar lateral tilt during knee extension is plotted corresponding to knee extension as calculated from tibial kinematics. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59857/59857suppfig1large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
<img alt="Supplemental Figure 2" src="/files/ftp_upload/59857/59857suppfig2.jpg" /> 
Supplemental Figure 2: Perturbation analysis. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59857/59857suppfig2large.jpg" target="_blank">Please click here to view a larger version of this figure.</a>
Supplemental Coding File. <a href="https://www-jove-com.remotexs.ntu.edu.sg/files/ftp_upload/59857/am_list.txt" target="_blank">Please click here to download this file.</a>

Watch this Scientific Journal Video about Four-Dimensional CT Analysis Using Sequential 3D-3D Registration at JoVE.com

Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

We analyzed joint kinematics from four-dimensional computed tomography data. The sequential 3D-3D registration method semiautomatically provides the kinematics of the moving bone with respect to the subject bone from four-dimensional computed tomography data.

Four-dimensional computed tomography (4DCT) provides a series of volume data and visualizes joint motions. However, numerical analysis of 4DCT data ...

four-dimensional-ct-analysis-using-sequential-3d-3d-registration

Nanyang Technological University

Research

JoVE Journal

Bioengineering

7.7K Views.  Keio University School of Medicine. We analyzed joint kinematics from four-dimensional computed tomography data. The sequential 3D-3D registration method semiautomatically provides the kinematics of the moving bone with respect to the subject bone from four-dimensional computed tomography data.

Video: Four-Dimensional CT Analysis Using Sequential 3D-3D Registration

Improved Visualization and Quantitative Analysis of Drug Effects Using Micropatterned Cells

To date, most HCA (High Content Analysis) studies are carried out with adherent cell lines grown on a homogenous substrate in tissue-culture treated micro-plates. Under these conditions, cells spread and divide in all directions resulting in an inherent variability in cell shape, morphology and behavior. The high cell-to-cell variance of the overall population impedes the success of HCA, especially for drug development. The ability of micropatterns to normalize the shape and internal polarity of every individual cell provides a tremendous opportunity for solving this critical bottleneck 1-2.
To facilitate access and use of the micropatterning technology, CYTOO has developed a range of ready to use micropatterns, available in coverslip and microwell formats. In this video article, we provide detailed protocols of all the procedures from cell seeding on CYTOOchip micropatterns, drug treatment, fixation and staining to automated acquisition, automated image processing and final data analysis. With this example, we illustrate how micropatterns can facilitate cell-based assays. Alterations of the cell cytoskeleton are difficult to quantify in cells cultured on homogenous substrates, but culturing cells on micropatterns results in a reproducible organization of the actin meshwork due to systematic positioning of the cell adhesion contacts in every cell. Such normalization of the intracellular architecture allows quantification of even small effects on the actin cytoskeleton as demonstrated in these set of protocols using blebbistatin, an inhibitor of the actin-myosin interaction.

Adhesive micropatterns that normalize cellular architecture can be used to increase sensitivity in the detection of drug effects, improve reproducibility and simplify automated image acquisition and analysis. Such technology will benefit drug/siRNA screening assays, performed on conventional cell culture supports and consequently suffering from excessive cell-to-cell variability.

To date, most HCA (High Content Analysis) studies are carried out with adherent cell lines grown on a homogenous substrate in tissue-culture treated ...

Bioluminescence Imaging for Assessment of Immune Responses Following Implantation of Engineered Heart Tissue (EHT)

Various techniques of cardiac tissue engineering have been pursued in the past decades including scaffolding strategies using either native or bioartificial scaffold materials, entrapment of cardiac myocytes in hydrogels such as fibrin or collagen and stacking of myocyte monolayers 1. These concepts aim at restoration of compromised cardiac function (e.g. after myocardial infarction) or as experimental models (e.g. predictive toxicology and substance screening or disease modelling). Precise monitoring of cell survival after implantation of engineered heart tissue (EHT) has now become possible using in-vivo bioluminescence imaging (BLI) techniques 2. Here we describe the generation of fibrin-based EHT from a transgenic rat strain with ubiquitous expression of firefly luciferase (ROSA/luciferase-LEW Tg; 3). Implantation is performed into the greater omentum of different rat strains to assess immune responses of the recipient organism following EHT implantation. Comparison of results generated by BLI and the Enzyme Linked Immuno Spot Technique (ELISPOT) confirm the usability of BLI for the assessment of immune responses.

Bioluminescence Imaging for Assessment of Immune Responses Following Implantation of Engineered Heart Tissue EHT

This video demonstrates the use of in vivo bioluminescence imaging to study immune responses after implantation of Engineered Heart Tissue (EHT) in rats.

Various techniques of cardiac tissue engineering have been pursued in the past decades including scaffolding strategies using either native or ...

High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT

Non-destructive volume visualization can be achieved only by tomographic techniques, of which the most efficient is the x-ray micro computerized tomography (&mu;CT).
High resolution &mu;CT is a very versatile yet accurate (1-2 microns of resolution) technique for 3D examination of ex-vivo biological samples1, 2. As opposed to electron tomography, the &mu;CT allows the examination of up to 4 cm thick samples. This technique requires only few hours of measurement as compared to weeks in histology. In addition, &mu;CT does not rely on 2D stereologic models, thus it may complement and in some cases can even replace histological methods3, 4, which are both time consuming and destructive. Sample conditioning and positioning in &mu;CT is straightforward and does not require high vacuum or low temperatures, which may adversely affect the structure. The sample is positioned and rotated 180&deg; or 360&deg;between a microfocused x-ray source and a detector, which includes a scintillator and an accurate CCD camera, For each angle a 2D image is taken, and then the entire volume is reconstructed using one of the different available algorithms5-7. The 3D resolution increases with the decrease of the rotation step. The present video protocol shows the main steps in preparation, immobilization and positioning of the sample followed by imaging at high resolution.

High Resolution 3D Imaging of Ex-Vivo Biological Samples by Micro CT

Non-destructive volume visualization can be achieved only by tomographic techniques, of which the most efficient is the x-ray micro computerized tomography ( CT).

Non-destructive volume visualization can be achieved only by tomographic techniques, of which the most efficient is the x-ray micro computerized ...

Polymer Microarrays for High Throughput Discovery of Biomaterials

The discovery of novel biomaterials that are optimized for a specific biological application is readily achieved using polymer microarrays, which allows a combinatorial library of materials to be screened in a parallel, high throughput format1. Herein is described the formation and characterization of a polymer microarray using an on-chip photopolymerization technique 2. This involves mixing monomers at varied ratios to produce a library of monomer solutions, transferring the solution to a glass slide format using a robotic printing device and curing with UV irradiation. This format is readily amenable to many biological assays, including stem cell attachment and proliferation, cell sorting and low bacterial adhesion, allowing the ready identification of 'hit' materials that fulfill a specific biological criterion3-5. Furthermore, the use of high throughput surface characterization (HTSC) allows the biological performance to be correlated with physio-chemical properties, hence elucidating the biological-material interaction6. HTSC makes use of water contact angle (WCA) measurements, atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS) and time-of-flight secondary ion mass spectrometry (ToF-SIMS). In particular, ToF-SIMS provides a chemically rich analysis of the sample that can be used to correlate the cell response with a molecular moiety. In some cases, the biological performance can be predicted from the ToF-SIMS spectra, demonstrating the chemical dependence of a biological-material interaction, and informing the development of hit materials5,3.

A description of the formation of a polymer microarray using an on-chip photopolymerization technique. The high throughput surface characterization using atomic force microscopy, water contact angle measurements, X-ray photoelectron spectroscopy and time of flight secondary ion mass spectrometry and a cell attachment assay is also described.

The discovery of novel biomaterials that are optimized for a specific biological application is readily achieved using polymer microarrays, which allows a ...

Self-reporting Scaffolds for 3-Dimensional Cell Culture

Culturing cells in 3D on appropriate scaffolds is thought to better mimic the in vivo microenvironment and increase cell-cell interactions. The resulting 3D cellular construct can often be more relevant to studying the molecular events and cell-cell interactions than similar experiments studied in 2D. To create effective 3D cultures with high cell viability throughout the scaffold the culture conditions such as oxygen and pH need to be carefully controlled as gradients in analyte concentration can exist throughout the 3D construct. Here we describe the methods of preparing biocompatible pH responsive sol-gel nanosensors and their incorporation into poly(lactic-co-glycolic acid) (PLGA) electrospun scaffolds along with their subsequent preparation for the culture of mammalian cells. The pH responsive scaffolds can be used as tools to determine microenvironmental pH within a 3D cellular construct. Furthermore, we detail the delivery of pH responsive nanosensors to the intracellular environment of mammalian cells whose growth was supported by electrospun PLGA scaffolds. The cytoplasmic location of the pH responsive nanosensors can be utilized to monitor intracellular pH (pHi) during ongoing experimentation.

Biocompatible pH responsive sol-gel nanosensors can be incorporated into poly(lactic-co-glycolic acid) (PLGA) electrospun scaffolds. The produced self-reporting scaffolds can be used for in&#160;situ monitoring of microenvironmental conditions whilst culturing cells upon the scaffold. This is beneficial as the 3D cellular construct can be monitored in real-time without disturbing the experiment.

Culturing cells in 3D on appropriate scaffolds is thought to better mimic the in vivo microenvironment and increase cell-cell interactions. The resulting ...

Systematic Analysis of In Vitro Cell Rolling Using a Multi-well Plate Microfluidic System

A major challenge for cell-based therapy is the inability to systemically target a large quantity of viable cells with high efficiency to tissues of interest following intravenous or intraarterial infusion. Consequently, increasing cell homing is currently studied as a strategy to improve cell therapy. Cell rolling on the vascular endothelium is an important step in the process of cell homing and can be probed in-vitro using a parallel plate flow chamber (PPFC). However, this is an extremely tedious, low throughput assay, with poorly controlled flow conditions. Instead, we used a multi-well plate microfluidic system that enables study of cellular rolling properties in a higher throughput under precisely controlled, physiologically relevant shear flow1,2. In this paper, we show how the rolling properties of HL-60 (human promyelocytic leukemia) cells on P- and E-selectin-coated surfaces as well as on cell monolayer-coated surfaces can be readily examined. To better simulate inflammatory conditions, the microfluidic channel surface was coated with endothelial cells (ECs), which were then activated with tumor necrosis factor-&#945; (TNF-&#945;), significantly increasing interactions with HL-60 cells under dynamic conditions. The enhanced throughput and integrated multi-parameter software analysis platform, that permits rapid analysis of parameters such as rolling velocities and rolling path, are important advantages for assessing cell rolling properties in-vitro. Allowing rapid and accurate analysis of engineering approaches designed to impact cell rolling and homing, this platform may help advance exogenous cell-based therapy.

Systematic Analysis of In Vitro Cell Rolling Using a Multi-well Plate Microfluidic System

This study used a multi-well plate microfluidic system, significantly increasing throughput of cell rolling studies under physiologically relevant shear flow. Given the importance of cell rolling in the multi-step cell homing cascade and the importance of cell homing following systemic delivery of exogenous populations of cells in patients, this system offers potential as a screening platform to improve cell-based therapy.

A major challenge for cell-based therapy is the inability to systemically target a large quantity of viable cells with high efficiency to tissues of ...

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The hierarchical architecture of protective biological materials such as mineralized fish scales, gastropod shells, ram&#8217;s horn, antlers, and turtle shells provides unique design principles with potentials for guiding the design of protective materials and systems in the future. Understanding the structure-property relationships for these material systems at the microscale and nanoscale where failure initiates is essential. Currently, experimental techniques such as nanoindentation, X-ray CT, and SEM provide researchers with a way to correlate the mechanical behavior with hierarchical microstructures of these material systems1-6. However, a well-defined standard procedure for specimen preparation of mineralized biomaterials is not currently available. In this study, the methods for probing spatially correlated chemical, structural, and mechanical properties of the multilayered scale of A. spatula using nanoindentation, FTIR, SEM, with energy-dispersive X-ray (EDX) microanalysis, and X-ray CT are presented.

Characterization Of Multi-layered Fish Scales Atractosteus spatula Using Nanoindentation, X-ray CT, FTIR, and SEM

This paper presents the methods used for probing spatially correlated chemical, structural, and mechanical properties of the multilayered scale of Atractosteus spatula (A. spatula) using nanoindentation, Fourier transform infrared (FTIR) spectroscopy, scanning electron microscopy (SEM), and X-ray computed tomography (X-ray CT). The experimental results have been used to investigate the design principles of protective biological materials.

The hierarchical architecture of protective biological materials such as mineralized fish scales, gastropod shells, ram&#8217;s horn, antlers, and turtle ...

Microscale Vortex-assisted Electroporator for Sequential Molecular Delivery

Electroporation has received increasing attention in the past years, because it is a very powerful technique for physically introducing non-permeant exogenous molecular probes into cells. This work reports a microfluidic electroporation platform capable of performing multiple molecule delivery to mammalian cells with precise and molecular-dependent parameter control. The system&#8217;s ability to isolate cells with uniform size distribution allows for less variation in electroporation efficiency per given electric field strength; hence enhanced sample viability. Moreover, its process visualization feature allows for observation of the fluorescent molecular uptake process in real-time, which permits prompt molecular delivery parameter adjustments in situ for efficiency enhancement. To show the vast capabilities of the reported platform, macromolecules with different sizes and electrical charges (e.g., Dextran with MW of 3,000 and 70,000 Da) were delivered to metastatic breast cancer cells with high delivery efficiencies (&#62;70%) for all tested molecules. The developed platform has proven its potential for use in the expansion of research fields where on-chip electroporation techniques can be beneficial.

A microfluidic vortex assisted electroporation platform was developed for sequential delivery of multiple molecules into identical cell populations with precise and independent dosage control. The system&#8217;s size based target cell purification step preceding electroporation aided to enhance molecular delivery efficiency and processed cell viability.

Electroporation has received increasing attention in the past years, because it is a very powerful technique for physically introducing non-permeant ...

Fabricating Complex Culture Substrates Using Robotic Microcontact Printing (R-&#181;CP) and Sequential Nucleophilic Substitution

In tissue engineering, it is desirable to exhibit spatial control of tissue morphology and cell fate in culture on the micron scale. Culture substrates presenting grafted poly(ethylene glycol) (PEG) brushes can be used to achieve this task by creating microscale, non-fouling and cell adhesion resistant regions as well as regions where cells participate in biospecific interactions with covalently tethered ligands. To engineer complex tissues using such substrates, it will be necessary to sequentially pattern multiple PEG brushes functionalized to confer differential bioactivities and aligned in microscale orientations that mimic in vivo niches. Microcontact printing (&#956;CP) is a versatile technique to pattern such grafted PEG brushes, but manual &#956;CP cannot be performed with microscale precision. Thus, we combined advanced robotics with soft-lithography techniques and emerging surface chemistry reactions to develop a robotic microcontact printing (R-&#956;CP)-assisted method for fabricating culture substrates with complex, microscale, and highly ordered patterns of PEG brushes presenting orthogonal &#8216;click&#8217; chemistries. Here, we describe in detail the workflow to manufacture such substrates.

Fabricating Complex Culture Substrates Using Robotic Microcontact Printing R- &#181;CP and Sequential Nucleophilic Substitution

Cell culture substrates functionalized with microscale patterns of biological ligands have immense utility in the field of tissue engineering. Here, we demonstrate the versatile and automated manufacture of tissue culture substrates with multiple, micropatterned poly(ethylene glycol) brushes presenting orthogonal chemistries that enable spatially precise and site-specific immobilization of biological ligands.

In tissue engineering, it is desirable to exhibit spatial control of tissue morphology and cell fate in culture on the micron scale. Culture substrates ...

Gene Expression Analysis of Endothelial Cells Exposed to Shear Stress Using Multiple Parallel-plate Flow Chambers

We describe a workflow for the analysis of gene expression from endothelial cells subject to a steady laminar flow using multiple monitored parallel-plate flow chambers. Endothelial cells form the inner cellular lining of blood vessels and are chronically exposed to the frictional force of blood flow called shear stress. Under physiological conditions, endothelial cells function in the presence of various shear stress conditions. Thus, the application of shear stress conditions in in vitro models can provide greater insight into endothelial responses in vivo. The parallel-plate flow chamber previously published by Lane et al.9 is adapted to study endothelial gene regulation in the presence and absence of steady (non-pulsatile) laminar flow. Key adaptations in the set-up for laminar flow as presented here include a large, dedicated environment to house concurrent flow circuits, the monitoring of flow rates in real-time, and the inclusion of an exogenous reference RNA for the normalization of quantitative real-time PCR data. To assess multiple treatments/conditions with the application of shear stress, multiple flow circuits and pumps are used simultaneously within the same heated and humidified incubator. The flow rate of each flow circuit is measured continuously in real-time to standardize shear stress conditions throughout the experiments. Because these experiments have multiple conditions, we also use an exogenous reference RNA that is spiked-in at the time of RNA extraction for the normalization of RNA extraction and first-strand cDNA synthesis efficiencies. These steps minimize the variability between samples. This strategy is employed in our pipeline for the gene expression analysis with shear stress experiments using the parallel-plate flow chamber, but parts of this strategy, such as the exogenous reference RNA spike-in, can easily and cost-effectively be used for other applications.

Here, a workflow for the culture and gene expression analysis of endothelial cells under fluid shear stress is presented. Included is a physical arrangement for simultaneously housing and monitoring multiple flow chambers in a controlled environment and the use of an exogenous reference RNA for quantitative PCR.

We describe a workflow for the analysis of gene expression from endothelial cells subject to a steady laminar flow using multiple monitored parallel-plate ...