Name: four-dimensional ct analysis using sequential 3d-3d registration
Uploaded: 2019-11-23T13:00:00.000+00:00
Duration: 5 min 5 s
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 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 ...

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><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.&lt;br/&gt; Surface reconstruction from CT DICOM data and point cloud data.&lt;br/&gt; * Ryan, T. M. &amp;amp; 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&lt;br/&gt; ** MeshLab: an Open-Source Mesh Processing Tool. Sixth Eurographics Italian Chapter Conference, page 129-136, 2008.&lt;br/&gt; 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.&lt;br/&gt; *** 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 (&#039;dicom.py&#039; module).&lt;br/&gt; Calculation of the rotation matrices. (Numpy module)&lt;br/&gt; 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...

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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 ...

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7.8K 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

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

The generation of four-dimensional (4D) confocal datasets; consisting of 3D image sequences over time; provides an excellent methodology to capture cellular behaviors involved in developmental processes.&#160; The ability to track and follow cell movements is limited by sample movements that occur due to drift of the sample or, in some cases, growth during image acquisition. Tracking cells in datasets affected by drift and/or growth will incorporate these movements into any analysis of cell position. This may result in the apparent movement of static structures within the sample. Therefore prior to cell tracking, any sample drift should be corrected. Using the open source Fiji distribution 1 &#160;of ImageJ 2,3 and the incorporated LOCI tools 4, we developed the Correct 3D drift plug-in to remove erroneous sample movement in confocal datasets. This protocol effectively compensates for sample translation or alterations in focal position by utilizing phase correlation to register each time-point of a four-dimensional confocal datasets while maintaining the ability to visualize and measure cell movements over extended time-lapse experiments.

Time-lapse microscopy allows the visualization of developmental processes. Growth or drift of samples during image acquisition reduces the ability to accurately follow and measure cell movements during development. We describe the use of open source image processing software to correct for three dimensional sample drift over time.

The generation of four-dimensional (4D) confocal datasets; consisting of 3D image sequences over time; provides an excellent methodology to capture ...

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Epilepsy surgery is challenging and the use of 3D multimodality image integration (3DMMI) to aid presurgical planning is well-established. Multimodality image integration can be technically demanding, and is underutilised in clinical practice. We have developed a single software platform for image integration, 3D visualization and surgical planning. Here, our pipeline is described in step-by-step fashion, starting with image acquisition, proceeding through image co-registration, manual segmentation, brain and vessel extraction, 3D visualization and manual planning of stereoEEG (SEEG) implantations. With dissemination of the software this pipeline can be reproduced in other centres, allowing other groups to benefit from 3DMMI. We also describe the use of an automated, multi-trajectory planner to generate stereoEEG implantation plans. Preliminary studies suggest this is a rapid, safe and efficacious adjunct for planning SEEG implantations. Finally, a simple solution for the export of plans and models to commercial neuronavigation systems for implementation of plans in the operating theater is described. This software is a valuable tool that can support clinical decision making throughout the epilepsy surgery pathway.

We describe the steps to use our custom designed software for image integration, visualization and planning in epilepsy surgery.

Epilepsy surgery is challenging and the use of 3D multimodality image integration (3DMMI) to aid presurgical planning is well-established. Multimodality ...

Medicine

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

The shoulder is one of the human body's most complex joint systems, with motion occurring through the coordinated actions of four individual joints, multiple ligaments, and approximately 20 muscles. Unfortunately, shoulder pathologies (e.g., rotator cuff tears, joint dislocations, arthritis) are common, resulting in substantial pain, disability, and decreased quality of life. The specific etiology for many of these pathologic conditions is not fully understood, but it is generally accepted that shoulder pathology is often associated with altered joint motion. Unfortunately, measuring shoulder motion with the necessary level of accuracy to investigate motion-based hypotheses is not trivial. However, radiographic-based motion measurement techniques have provided the advancement necessary to investigate motion-based hypotheses and provide a mechanistic understanding of shoulder function. Thus, the purpose of this article is to describe the approaches for measuring shoulder motion using a custom biplanar videoradiography system. The specific objectives of this article are to describe the protocols to acquire biplanar videoradiographic images of the shoulder complex, acquire CT scans, develop 3D bone models, locate anatomical landmarks, track the position and orientation of the humerus, scapula, and torso from the biplanar radiographic images, and calculate the kinematic outcome measures. In addition, the article will describe special considerations unique to the shoulder when measuring joint kinematics using this approach.

Biplane videoradiography can quantify shoulder kinematics with a high degree of accuracy. The protocol described herein was specifically designed to track the scapula, humerus, and the ribs during planar humeral elevation, and outlines the procedures for data collection, processing, and analysis. Unique considerations for data collection are also described.

The shoulder is one of the human body's most complex joint systems, with motion occurring through the coordinated actions of four individual joints, ...

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

The fusion of 3D anatomical models derived from high-fidelity pre-interventional computed tomography angiography (CTA), and x-ray (XR) fluoroscopy to facilitate anatomical guidance is of huge interest for complex cardiac interventions like TAVI procedures with cerebral protection. Co-registration of CTA and XR has been introduced either based on additional intraoperative non-/contrast-enhanced cone-beam computed tomography (CBCT) or two separate aortograms. With the related increase of radiation exposure and/or contrast agent (CA) dose, a potential additional risk for the patient is introduced. Here, we propose a modified co-registration approach making use of arteriograms of the iliofemoral arteries, routinely performed during the femoral puncture and sheath introduction. On-the-fly refinement of the co-registration during the on-going procedure enables accurate co-registration without any additional angiograms, thus reducing CA, XR dose and procedure time, while simultaneously improving operator confidence and procedure safety.

The aim of this study was to improve the co-registration for image fusion (IF) of pre-interventional CT data with real-time x-ray (XR) fluoroscopy during transfemoral transcatheter aortic valve implantation (TAVI).

The fusion of 3D anatomical models derived from high-fidelity pre-interventional computed tomography angiography (CTA), and x-ray (XR) fluoroscopy to ...

Three-Dimensional Printing of a Complex Aortic Anomaly

Complex congenital aortic anomalies include diverse types of malformations that may be clinically asymptomatic or present with respiratory or esophageal symptoms. These anomalies may be associated with other congenital heart diseases. It is hard to identify the accurate anatomic vessel location from two-dimensional imaging data, such as computed tomography. As an additive manufacturing method, three-dimensional (3-D) printing can covert the acquired imaging data into 3-D physical models. This protocol describes the procedure for modeling the volumetric DICOM imaging into 3-D data and printing it as an anatomically realistic 3-D model. Using this model, surgeons can identify the vessel location of complex aortic anomalies, which is helpful for pre-operative planning and intra-operative guidance.

Here, we present a protocol to use three dimensional printed models for pre-operative planning and intra-operative reorganization of complicated vascular locations when handling a congenital aortic anomaly.

Complex congenital aortic anomalies include diverse types of malformations that may be clinically asymptomatic or present with respiratory or esophageal ...

Effective Co-Registration of Two Preclinical Imaging Modalities Across Devices

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Integrated preclinical multimodal imaging systems, such as X-ray computed tomography (CT) combined with positron emission tomography (PET) or magnetic resonance imaging (MRI) combined with PET, are widely available and typically provide robustly co-registered volumes. However, separate devices are often needed to combine a standalone MRI with an existing PET-CT or to incorporate additional data from optical tomography or high-resolution X-ray microtomography. This necessitates image co-registration, which involves complex aspects such as multimodal mouse bed design, fiducial marker inclusion, image reconstruction, and software-based image fusion. Fiducial markers often pose problems for in vivo data due to dynamic range issues, limitations on the imaging field of view, difficulties in marker placement, or marker signal loss over time (e.g., from drying or decay). These challenges must be understood and addressed by each research group requiring image co-registration, resulting in repeated efforts, as the relevant details are rarely described in existing publications.
This protocol outlines a general workflow that overcomes these issues. Although a differential transformation is initially created using fiducial markers or visual structures, such markers are not required in production scans. The requirements for the volume data and the metadata generated by the reconstruction software are detailed. The discussion covers achieving and verifying requirements separately for each modality. A phantom-based approach is described to generate a differential transformation between the coordinate systems of two imaging modalities. This method showcases how to co-register production scans without fiducial markers. Each step is illustrated using available software, with recommendations for commercially available phantoms. The feasibility of this approach with different combinations of imaging modalities installed at various sites is showcased.

Author Spotlight: An Efficient and Robust Software for Automated Fusion of Multiple Preclinical Imaging Modalities

The combination of multiple imaging modalities is often necessary to gain a comprehensive understanding of pathophysiology. This approach utilizes phantoms to generate a differential transformation between the coordinate systems of two modalities, which is then applied for co-registration. This method eliminates the need for fiducials in production scans.

Integrated preclinical multimodal imaging systems, such as X-ray computed tomography (CT) combined with positron emission tomography (PET) or magnetic ...

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

In the United States, more than 80% of all abdominal aortic aneurysms are treated by endovascular aortic aneurysm repair (EVAR). The endovascular approach warrants good early results, but adequate follow-up imaging after EVAR is imperative to maintain long-term positive outcomes. Potential graft-related complications are graft migration, infection, fraction, and endoleaks, with the last one being the most common. The most frequently used imaging after EVAR is computed tomography angiography (CTA) and duplex ultrasound. Dynamic, time-resolved computed tomography angiography (d-CTA) is a reasonably new technique to characterize the endoleaks. Multiple scans are done sequentially around the endograft during acquisition that grants good visualization of the contrast passage and graft-related complications. This high diagnostic accuracy of d-CTA can be implemented into therapy via image fusion and reduce additional radiation and contrast material exposure.
This protocol describes the technical aspects of this modality: patient selection, preliminary image review, d-CTA scan acquisition, image processing, qualitative and quantitative endoleak characterization. The steps of integrating dynamic CTA into intra-operative fluoroscopy using 2D-3D fusion-imaging to facilitate targeted embolization are also demonstrated. In conclusion, time-resolved, dynamic CTA is an ideal modality for endoleak characterization with additional quantitative analysis. It can reduce radiation and iodinated contrast material exposure during endoleak treatment by guiding interventions.

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Dynamic computed tomography angiography (CTA) imaging provides additional diagnostic value in characterizing aortic endoleaks. This protocol describes a qualitative and quantitative approach using time-attenuation curve analysis to characterize endoleaks. The technique of integrating dynamic CTA imaging with fluoroscopy using 2D-3D image fusion is illustrated for better image guidance during treatment.

In the United States, more than 80% of all abdominal aortic aneurysms are treated by endovascular aortic aneurysm repair (EVAR). The endovascular approach ...

Four-Dimensional Computed Tomography-Guided Valve Sizing for Transcatheter Pulmonary Valve Replacement

The measurements of the right ventricle (RV) and pulmonary artery (PA), for selecting the optimal prosthesis size for transcatheter pulmonary valve replacement (TPVR), vary considerably. Three-dimensional (3D) computed tomography (CT) imaging for device size prediction is insufficient to assess the displacement of the right ventricular outflow tract (RVOT) and PA, which could increase the risk of stent misplacement and paravalvular leak. The aim of this study is to provide a dynamic model to visualize and quantify the anatomy of the RVOT to PA over the entire cardiac cycle by four-dimensional (4D) cardiac CT reconstruction to obtain an accurate quantitative evaluation of the required valve size. In this pilot study, cardiac CT from sheep J was chosen to illustrate the procedures. 3D cardiac CT was imported into 3D reconstruction software to build a 4D sequence which was divided into eleven frames over the cardiac cycle to visualize the deformation of the heart. Diameter, cross-sectional area, and circumference of five imaging planes at the main PA, sinotubular junction, sinus, basal plane of the pulmonary valve (BPV), and RVOT were measured at each frame in 4D straightened models prior to valve implantation to predict the valve size. Meanwhile, dynamic changes in the RV volume were also measured to evaluate right ventricular ejection fraction (RVEF). 3D measurements at the end of the diastole were obtained for comparison with the 4D measurements. In sheep J, 4D CT measurements from the straightened model resulted in the same choice of valve size for TPVR (30 mm) as 3D measurements. The RVEF of sheep J from pre-CT was 62.1 %. In contrast with 3D CT, the straightened 4D reconstruction model not only enabled accurate prediction for valve size selection for TPVR but also provided an ideal virtual reality, thus presenting a promising method for TPVR and the innovation of TPVR devices.

This study assessed a new methodology with a straightened model generated from the four-dimensional cardiac computed tomography sequence to obtain the desired measurements for valve sizing in the application of transcatheter pulmonary valve replacement.

The measurements of the right ventricle (RV) and pulmonary artery (PA), for selecting the optimal prosthesis size for transcatheter pulmonary valve ...

Three-Dimensional Preoperative Virtual Planning in Derotational Proximal Femoral Osteotomy

Anterior knee pain (AKP) is a common pathology among adolescents and adults. Increased femoral anteversion (FAV) has many clinical manifestations, including AKP. There is growing evidence that increased FAV plays a major role in the genesis of AKP. Furthermore, this same evidence suggests that derotational femoral osteotomy is beneficial for these patients, as good clinical results have been reported. However, this type of surgery is not widely used among orthopedic surgeons.
The first step in attracting orthopedic surgeons to the field of rotational osteotomy is to give them a methodology that simplifies preoperative surgical planning and allows for the previsualization of the results of surgical interventions on computers. To that end, our working group uses 3D technology. The imaging dataset used for surgical planning is based on a CT scan of the patient. This 3D method is open access (OA), meaning it is accessible to any orthopedic surgeon at no economic cost. Furthermore, it not only allows for the quantification of femoral torsion but also for carrying out virtual surgical planning. Interestingly, this 3D technology shows that the magnitude of the intertrochanteric rotational femoral osteotomy does not present a 1:1 relationship with the correction of the deformity. Additionally, this technology allows for the adjustment of the osteotomy so that the relationship between the magnitude of the osteotomy and the correction of the deformity is 1:1. This paper outlines this 3D protocol.

This work presents a detailed surgical planning protocol using 3D technology with free open-source software. This protocol can be used to correctly quantify femoral anteversion and simulate derotational proximal femoral osteotomy for the treatment of anterior knee pain.

Anterior knee pain (AKP) is a common pathology among adolescents and adults. Increased femoral anteversion (FAV) has many clinical manifestations, ...

3D Cephalometric Landmark Annotation Demonstration on Human CBCT Scans

Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans

Craniofacial cephalometric analysis is a diagnostic tool used for the assessment of the relationship of various bones and soft tissues in the head and face. Cephalometric analysis has been traditionally conducted with the use of 2D radiographs and landmark sets and restricted to size, linear and angular measurements, and 2D relationships. The increasing use of 3D cone beam computed tomography (CBCT) scans in the dental field dictates the need for the evolution to 3D cephalometric analysis, which incorporates shape and a more realistic analysis of longitudinal development in all three planes. This study is a demonstration of 3D cephalometric analysis with the use of a validated set of skeletal tissue landmarks on human CBCT scans. Detailed instructions for the annotation of each landmark on a 3D volume are provided as part of a step-by-step protocol. The generated measurements and 3D coordinates of the landmarks can be exported and used both for clinical and research purposes. The introduction of 3D cephalometric analysis in basic and clinical craniofacial studies will lead to future advancements in the field of craniofacial growth and development.

Author Spotlight: Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans  

Presented here is a detailed protocol for the conduction of three-dimensional cephalometric analysis with the use of human cone beam computed tomography scans.

Craniofacial cephalometric analysis is a diagnostic tool used for the assessment of the relationship of various bones and soft tissues in the head and ...

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

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Chapters in this video

Sample Drift Correction Following 4D Confocal Time-lapse Imaging

A Pipeline for 3D Multimodality Image Integration and Computer-assisted Planning in Epilepsy Surgery

Measuring 3D In-vivo Shoulder Kinematics using Biplanar Videoradiography

Improved Registration of 3D CT Angiography with X-ray Fluoroscopy for Image Fusion During Transcatheter Aortic Valve Implantation

Three-Dimensional Printing of a Complex Aortic Anomaly

Multimodal Cross-Device and Marker-Free Co-Registration of Preclinical Imaging Modalities

Time-Resolved, Dynamic Computed Tomography Angiography for Characterization of Aortic Endoleaks and Treatment Guidance via 2D-3D Fusion-Imaging

Four-Dimensional Computed Tomography-Guided Valve Sizing for Transcatheter Pulmonary Valve Replacement

Three-Dimensional Preoperative Virtual Planning in Derotational Proximal Femoral Osteotomy

Three-Dimensional Cephalometric Landmark Annotation Demonstration on Human Cone Beam Computed Tomography Scans