Name: four-dimensional ct analysis using sequential 3d-3d registration
Uploaded: 2019-11-23T13:00:00
Description: Watch this Scientific Journal Video about Four-Dimensional CT Analysis Using Sequential 3D-3D Registration at JoVE.com

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...

<ol>
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D. <a target="_blank" href="http://www.ncbi.nlm.nih.gov/entrez/query.fcgi?db=PubMed&cmd=Search&doptcmdl=Citation&defaultField=Title+Word&term=A+method+for+registration+of+3-D+shapes.">A method for registration of 3-D shapes.</a> IEEE Transactions on Pattern Analysis and Machine Intelligence. 14 (2), 239-256 (1992).</li><li>Wu, G., 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=ISB+recommendation+on+definitions+of+joint+coordinate+system+of+various+joints+for+the+reporting+of+human+joint+motion--part+I:+ankle,+hip,+and+spine.">ISB recommendation on definitions of joint coordinate system of various joints for the reporting of human joint motion--part I: ankle, hip, and spine.</a> Journal of Biomechanics. 35 (4), 543-548 (2002).</li><li>Wu, G., 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=ISB+recommendation+on+definitions+of+joint+coordinate+systems+of+various+joints+for+the+reporting+of+human+joint+motion--Part+II:+shoulder,+elbow,+wrist+and+hand.">ISB recommendation on definitions of joint coordinate systems of various joints for the reporting of human joint motion--Part II: shoulder, elbow, wrist and hand.</a> Journal of Biomechanics. 38 (5), 981-992 (2005).</li><li>Crawford, N. 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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 ...

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 2...

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 ...

A method to reconstruct the motion of a moving bone with respect to the fixed bone from 4D CT data, facilitating the description of detailed joint kinematics. Once the soft of the whole bone has been created, and your reference points have been selected, most of the reconstruction processes will be completed automatically. With the target joint of the subject inside the CT gantry of the 4D CT image, ask the subject to move the joint during the 10-second scan time, while obtaining a series of volume data.For surface semi-automatic segmentation of the 3D CT data, load the CT DICOM data into the appropriate software, and click Edit New Label Field to open the label field. Identify which threshold CT attenuation value is appropriate to extract cortical bone from the source bone, and select materials with CT attenuation values above the threshold. Check the label for bone cortex selection and use an appropriate editing tool to manually modify the demarkation.To generate the surface data from the labeled bone cortex position data, click Generate Surface and use the slider to smooth the extent. Then click Apply. To save the surface data in STL format, click File and Export Data As, and select STL binary Little Endian.To perform an automatic segmentation of the 4D CT volume data, use the DICOM reading module in the programming software to extract the geometric data showing the CT attenuation values above the threshold from all 51 frames of the 4D CT data. Then use a batch processing script to reconstruct all of the surface data of the point cloud with higher CT attenuation values than the threshold for all of the 4D CT frames in the image processing software. To perform surface registration from static 3D CT to the first frame of the 4D CT, use the selecting phase function to trim the bones in a static 3D CT into partial segment data that are included in all of the frames of the 4D CT movie data.The 4D CT surface data are only partial segments that will be included in each volume image, because the surface registration requires that one surface data point is included in another surface. Use the Pick Points function to select three landmarks in the fixed and moving bones that can be easily identified from the trimmed 3D CT surface and the surface data of the first frame of 4D CT in the 3D mesh editing software. Then match the partial fixed and moving bones roughly on the first frame of the 4D CT surface data according to the picked landmarks, and use the iterative closest-point algorithm to perform surface registration in the open-source software.To perform a sequential surface registration, use the iterative closest-point module in the open-source software to first match the partial surfaces of the fixed bone in the first 4D CT frame onto the surface data of the second frame. Then, reconstruct moving bone motion with respect to the fixed bone. When the rotation parameters have been measured, define the coordinate systems of the fixed and moving bones.The example data of knee kinematics shows that the Varus angle of the tibia gradually decreases as the tibia is extended. The tibial external rotation increases at the end of the extension, corresponding with the home movement of the knee in previous reports. Graphs of the error for translation and rotation show that the error is tolerable for femur lengths longer than 9%of the whole length, and tibia lengths longer than 7%of the whole length, as the CT slice thickness is 0.5 milimeters, and exceeds the error size.In addition, the calculation of patellar kinematics demonstrates that the lateral tilt of the patella corresponds to the knee flexion angle. The surface reconstruction of the bone cortex should be visually checked and may require manual segmentation. Recently, many studies of automatic segmentations have used Using these techniques, reconstruction can performed automatically.

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