Name: four-dimensional computed tomography-guided valve sizing for transcatheter pulmonary valve replacement
Uploaded: 2022-01-20T14:30:00
Description: Watch this Scientific Journal Video about Four-Dimensional Computed Tomography-Guided Valve Sizing for Transcatheter Pulmonary Valve Replacement at JoVE.com

Xiaolin Sun and Yimeng Hao contributed equally to this manuscript and share first authorship. Heartfelt appreciation is extended to all who contributed to this work, both past and present members. This work was supported by grants from the German Federal Ministry for Economic Affairs and Energy, EXIST - Transfer of Research (03EFIBE103). Xiaolin Sun and Yimeng Hao are supported by the China Scholarship Council (Xiaolin Sun- CSC: 201908080063, Yimeng Hao-CSC: 202008450028).

All cardiac CT data were obtained from GrOwnValve preclinical trials with the approval of the legal and ethical committee of the Regional Office for Health and Social Affairs, Berlin (LAGeSo). All animals received humane care in compliance with the guidelines of the European and German Societies of Laboratory Animal Science (FELASA, GV-SOLAS). In this study, the Pre-CT from sheep J was chosen to illustrate the procedures.
1. Perform 3D cardiac CT in sheep
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To date, this is the first study to illustrate a patient-specific measurement of the morphology and dynamic parameters of RVOT-PA with a straightened cardiac model generated from a 4D CT sequence, which can be applied to predict the optimal valve size for TPVR. This methodology wasillustrated using sheep J Pre-CT imaging to obtain the dynamic deformations, right ventricular volumes, right ventricular function, and magnitude of RVOT/PA change from the RVOT to the pulmonary trunk in five planes at every 10% reconstruction ...

Dysfunction of the right ventricular outflow tract (RVOT) and pulmonary valve abnormalities are two of the most frequent consequences of severe congenital heart disease, for example, patients with repaired tetralogy of Fallot (TOF), certain types of double outlet right ventricle (DORV), and transposition of the great arteries1,2,3. The majority of these patients face multiple operations throughout their lives and along with advancing age, the risks of complexity and comorbidities increase. These patients may benefit from transcatheter pulmonary valve replacement (TPVR) as a minimally invasive treatment4. To date, there has been a steady growth in the number of patients undergoing TPVR and several thousands of these procedures have been performed worldwide. Compared with traditional open-heart surgery, TPVR requires a more accurate anatomical measurement of the xenograft or homograft from the right ventricle (RV) to pulmonary artery (PA), as well as the repair of pulmonary and RVOT stenosis via transannular patch, by computed tomography angiography (CTA) prior to intervention and to ensure that the patients are free from stent fracture and paravalvular leak (PVL)5,6.
A prospective, multicenter study demonstrated that a multidetector CT annular sizing algorithm played an important role in selecting the appropriate valve size, which could decrease the degree of paravalvular regurgitation7. In recent years, quantitative analysis has been more and more applied in clinical medicine. Quantitative analysis has enormous potential to enable objective and correct interpretation of clinical imaging and to verify that patients are free of stent fracture and paravalvular leak, which can enhance patient-specific therapy and treatment response evaluation. In previous clinical practice, it was feasible to reconstruct CT imaging from three planes (sagittal, coronal, and axial) with two-dimensional (2D) CT to obtain a visualization model8. Contrast-enhanced electrocardiogram (ECG)-gated CT has become more important in the evaluation of RVOT/PA 3D morphology and function, as well as in the identification of patients with a suitable RVOT implantation site that is capable of maintaining TPVR stability throughout the cardiac cycle9,10.
However, in the contemporary standard clinical and preclinical settings, the acquired 4D CT data are usually translated into 3D planes for manual quantification and visual evaluation which cannot show 3D/4D dynamic information11. Furthermore, even with 3D information, the measurements obtained from multiplanar reconstruction (MPR) have various limitations, such as poor quality of visualization and lack of dynamic deformation due to the different directions of blood flow in the right heart12. Measurements are time-consuming to gather and prone to mistakes, as 2D alignment and sectioning can be imprecise, resulting in misinterpretation and distensibility. Currently, there is no consensus on which measurement of RVOT-PA could reliably provide accurate information about the indications and valve sizing for TPVR in patients with dysfunctional RVOT and/or pulmonary valve disease.
In this study, the method for measuring RVOT-PA using a straightened right heart model via a 4D cardiac CT sequence is provided to determine how best to characterize the 3D deformations of RVOT-PA throughout the cardiac cycle. The spatio-temporal correlation imaging was completed by including the temporal dimension and, therefore, were able to measure variations in RVOT-PA magnitude. Additionally, the deformation of the straightened models could positively impact TPVR valve sizing and procedural planning.

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			<td>Software: 3D Slicer image computing platform</td>
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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.

In sheep J, the 4D total heart and right heart models were successfully generated from the 4D cardiac CT sequence which showed the deformation throughout the entire cardiac cycle. For better visualization, the whole deformation of the beating heart and right heart is exhibited in every direction in Figure 3 - Figure 4 and in Video 1 - Video 2.
The str...

Watch this Scientific Journal Video about Four-Dimensional Computed Tomography-Guided Valve Sizing for Transcatheter Pulmonary Valve Replacement at JoVE.com

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

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 ...

This study provides a straightened model generated from the 4D cardiac CT to obtain the desired amendments for our sizing in the application of transcatheter pulmonary valve replacement. It's a straightened 4D model that enables valve sizing for TPV, and provides an ideal virtual reality presenting a promising method for TPV and device innovations. This method can also enable actual valve sizing for transcatheter arctic valve plantations, with an idea virtual reality as well as for device innovations.Begin by select volume rendering in the modules dropdown menu. Then select the 4D sequence in the volume dropdown menu. Select CT cardiac three in the preset dropdown menu to display the 4D heart.Adjust the cursor below the preset dropdown menu to show the heart only. Click on sequence browser in the module's dropdown menu to select and display the 4D sequence. Drag the 4D heart into the 3D scene to observe the heart from various directions.Select the enable and display ROI functions in the crop options below the shift bar to crop the 4D volume of the beating heart, for observing the structures of the heart better. Select segment editor in the module's dropdown menu. Then click the scissors effect with the fill inside operation to cut one frame.Click on the mask volume effect and apply it to link the segmentation to the 4D heart as a masked volume. Select the scissors effect with the erase inside operation to remove the bones and other unexpected areas. Select the islands effect, with the keep largest island operation to remove small areas.Choose the erase effect with the one to 3%sphere brush to remove the tissues at the aortic arc with attachments to the main pulmonary artery, and the tissue between the ascending aorta and the superior vena cava. After each step apply the mask volume effect to mask the 4D volume and continue removing the areas until the right heart model is shown in the 3D scene. Click on the sequence browser and go to the next frame.Use the scissors effect with the erase inside option to cut any area in the 3D scene. Apply the same method to the rest of the frames until the entire 4D sequence has been segmented. Click on the sequence browser button to display the right heart 4D volume.Select the segment editor mode in the toolbar. Add two segmentations for each 10%frame of the 4D sequence and name them accordingly. Select the paint effect tool in the segment editor module with editable intensity range.Which depends on the CT images to paint the right heart with the sequence superior vena cava, right atrium, right ventricle, and pulmonary artery. Click on other segmentation. Use the paint tool to paint other areas to trace the boundaries of the right heart in general.Select the grow from seeds effect. Select initialize and apply to apply the effect. Click on showed 3D button in the segment editor module to display the 3D model of the contemporary frame.As demonstrated previously, remove or improve the 3D model according to the CT images in the three directions, followed by removal of the left and right branches of the pulmonary artery at the bifurcation. The right heart 3D model will then show the 3D scene in each frame. Loan the segmentations in the data tree as a backup.Name the segmentations. For example, 10%segmentation original, and 10%segmentation for straightened model. Select exact center line in the module's dropdown menu.Select segmentation in the surface dropdown menu in the input section of the extract Centerline module. Click on create new markups fiducial in the endpoints dropdown menu. Click on the place a markup point button to add endpoints on the top plane of the superior vena cava, and the end plane of the main pulmonary artery.Select create a new model as a Centerline model, and create new markups curve as a Centerline curve in the tree of the outputs menu. Click on apply to show the Centerline right heart model. Click on the data module, then right click on the Centerline curve to edit its properties.Click on the eye icon to display the control points. And in the resample section, set the number of resampled points to 40 to lower the computer load. Select curved planar reformat in the module's dropdown menu.Shift the cursor after curve resolution and slice resolution to 0.8 millimeters. Set the slice size to 130 to 140 millimeters. Create a new volume as output straightened volume.Click on apply to obtain the straightened volume. Select volume rendering in the module dropdown menu to show the straightened volume. Select the straightened volume in the volume dropdown menu and click on the eye icon.Select CT cardiac three as the preset. Move the shift cursor to show the straightened right heart volume in the 3D scene. Column the straightened volume in the data tree in the name of straightened volume for segmentation.And right click to segment the straightened volume. Select the threshold effect in the segment editor module to color the desired straightened right heart. And click apply to apply the operation.Select the mask volume effect to mask the straightened volume by choosing the straightened volume for segmentation. Volume as input volume and output volume. And click apply to apply the operation.Click apply to apply the settings to remove bones, unexpected and small areas, and tissue, to keep the straightened right heart segmentation only. Then check the straightened right heart volume and 3D model of the straightened right heart segmentation in the 3D scene. After obtaining the straightened right heart volume rendering and straightened segmentations for the other frames, save them in the respective folder.Add the five planes described in the manuscript into the straightened models in each frame by holding the shift key on the keyboard and using the cross hair or function in the toolbar of the five planes. Click on the create and place module in the toolbar to select the plane effect. Select the line effect to measure the perimeters.Select the closed curve effect to obtain the circumferences and cross-sectional areas. And copy the data to build the dataset. To perform right ventricular volume measurements in the straightened model, column the straightened segmentation in each frame obtained from the 4D sequence, and label the segmentation according to the matching frame for volume measurement.Select the segment statistics module in the module dropdown menu. Select segmentation and scale of volume in the inputs menu, and later X percent segmentation for volume measurement. Select create new table as the output table, and click on apply to apply the operations to get the volume table.The video shows 4D total heart and right heart models generated from the 4D cardiac CT sequence, which showed the defamation throughout the entire cardiac cycle. The whole defamation of the beating heart and right heart is exhibited in every direction. The figure shows straightened right heart models, obtained following the mask volume in each 10%of the segmentation, to illustrate the deformations of the right heart in a straightened model in ship J pre CT.The changes in cross-sectional area, perimeter, and circumference were obtained in different phases of the cardiac cycle to generate the tendency diagrams as shown in figure one.For this method, it is crucial to you mask the volume and centralized to the segmentations, built straightened models, and perform the NPR measurements on the five selected plans. This method pivotal the way for the researchers to analyze cardiac ICT, more visualized for interventional hardware treatments, and medical device development after is development.

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