The Laplace wall thickness algorithm was validated on the left atrium phantom. Left atrial wall thickness measurements were performed on 10 patients. Mean LAWT measurements ranged from 0. Left atrial wall thickness can be measured robustly and efficiently across the whole left atrium using a solution of the Laplace equation over a finite element mesh of the left atrium.
Further studies are indicated to determine whether the integration of LAWT maps into pre-existing 3D anatomical mapping systems may provide important anatomical information for guiding radiofrequency ablation. We describe and demonstrate a protocol for segmenting out the atrial myocardium tissue from contrast cardiac computed tomography images. We have developed a robust and automated algorithm for calculating maps of atrial wall thickness across the entirety of the left atrium.
The wall thickness algorithm is validated using on a 3D printed atrial phantom. The ability to calculate the left atrial wall thickness maps across the atrium is demonstrated in 10 patient cases. Atrial fibrillation AF is a prevalent and progressive condition associated with increased morbidity and mortality.
Isolation of the pulmonary veins via radiofrequency ablation RFA offers effective treatment of symptoms in drug refractory symptomatic patients with paroxysmal AF. However, long-term success rates of ablation procedures remain suboptimal with many patients requiring multiple procedures.
The thickness of the left atrial wall is related not only to the risk of cardiac perforation and tamponade following ablation 1 , 2 but also to the success rates in achieving effective transmural lesions. Furthermore, LAWT has been shown to predict the transition from paroxysmal to persistent AF, 3 be associated with complex fractionated electrograms, 4 and to influence the behaviour of rotor activation patterns 5 that may identify suitable ablation targets for patients with both paroxysmal and persistent AF.
Despite these potential benefits, the measurement of LAWT poses a number of challenges. The morphology and regional wall thickness of the left atrium is highly variable and in the presence of AF, there is an implicit need for an imaging technique with both a high temporal and spatial resolution to be able to measure the LAWT reliably.
These factors are largely accountable for the absence of any routine approach for measuring LAWT in clinical practice. The primary aim of this study was to determine whether the application of an image derived finite element method could be used to provide a reliable semi-automated technique for providing a LAWT map of the entire left atrial surface in patients undergoing routine coronary computed tomographic angiography CTA.
The secondary aim was to validate this approach for measuring LAWT using a 3D printed left atrium phantom. All patients underwent detailed analysis of their coronary arteries for clinical purposes, followed by an assessment of LAWT using a freely available segmentation tools package and a finite element-based method, as detailed below.
All patients received 0. Descending aorta contrast-triggered HU , ECG-gated scanning was then performed in a single breath hold. Scanning parameters included heart rate dependent pitch 0. The acquired coronary CTA data were reconstructed using iterative reconstruction iDose level 4 with the use of 0. If reconstruction from standard phases of the cardiac cycle resulted in left atrial wall boundaries that were marred by cardiac motion artefact, additional phases were reconstructed and analysed.
In the presence of significant ventricular ectopy, ECG-editing was performed using vendor-specific software. The first stage required the cropping of the CT image to isolate the atria. The second stage involved the segmentation of the left atrium from the entire cardiac CTA dataset, whilst the third stage involved solving the Laplace equations over a computational model generated from the respective segmented left atrium.
Creating a segmentation of the atria from any imaging modality is an essential part of calculating wall thickness. Images were segmented using the Python scripting tools for automating image processing steps within the freely available Seg3D2 software package. An automated script applied a 4-point median filter to the image and segmented the left atrial blood cavity using a threshold value of HU.
The blood pool threshold was manually corrected to remove spurious connections with the aorta, coronary sinus, and right atria. A sphere was place in the left ventricle that identified the location of the mitral valve, and separated any labelled ventricular blood from the left atrial blood pool.
An automated script then identified all viable atrial myocardial tissue. First, regions with 50— HU this elevated value reflects the presence of contrast agent in the blood elevating the HU value of myocardium were thresholded.
These values were chosen based on the HU values of the left ventricular myocardium. Large thick contiguous regions were removed from the mask of potential atrial tissue, due to the undesired inclusion of the un-contrasted right atrial blood pool and basal left ventricular tissue. The atrial blood pool, identified previously, was then removed from the reduced mask to identify all viable regions of atrial tissue. The atrial wall was then segmented in layers.
The first layer was formed by dilating the blood pool region by 1 voxel as the atrial wall was assumed to be at least 1 voxel thick.
The remaining layers were determined by dilating the current atrial wall and adding any regions that fell on voxels labelled previously as being viable atrial tissue. This was repeated four times. The final image was then dilated and eroded by 2 voxels to remove any spurious segmentations.
At its simplest wall thickness can be calculated by the length of the normal projection Figure 2 A from either the endocardial point x or epicardial point y surface or the shortest Euclidian distance between the two surfaces at any two points Figure 2 B. Schematic showing different approaches for measuring atrial wall thickness between the endocardium turquoise surface and epicardium green surface.
B Left atrial wall thickness calculated using a nearest point on the opposing surface generates a spurious result for point y. C The Laplace method calculates smooth dashed lines between the endocardium and epicardium. Paths that are orthogonal to these lines black arrows are used to calculate the wall thickness removing the spurious results observed in the normal projection and nearest point approaches. However, as demonstrated in Figure 2 , these approaches can generate spurious solutions, as indicated by the red arrows.
From electromagnetism we know that by solving the Laplace equation we can derive a smooth set of non-intersecting field lines between two bodies, be they points, lines, sheets, or arbitrary objects.
As shown in Figure 2 C , a family of dashed curves can be created that provide smooth, non-intersecting, and continuous lines between the surfaces. The distance of curves orthogonal to these lines, then provides a measure of the wall thickness at all points on the endocardial and epicardial surface. The length of the field lines is calculated in three steps. In brief, Eq. Unstructured tetrahedral meshes are used to allow subvoxel resolution and remove potential bias in the solution from regular voxel-based meshes.
A flood fill algorithm is then used to identify all unconnected node groups that lie on the mesh surface. The first and second largest node groups are then set as the epicardium and endocardium, respectively. All other surface nodes are labelled as lying on the remaining boundary surface. To calculate the length of field line particles is seeded on the endocardium and epicardium. The particles move away from their initial surface in the direction of the vector field Eq.
The internal cavity of the heart is divided into four chambers:. The two atria are thin-walled chambers that receive blood from the veins. The two ventricles are thick-walled chambers that forcefully pump blood out of the heart. Differences in thickness of the heart chamber walls are due to variations in the amount of myocardium present, which reflects the amount of force each chamber is required to generate. The right atrium receives deoxygenated blood from systemic veins; the left atrium receives oxygenated blood from the pulmonary veins.
Pumps need a set of valves to keep the fluid flowing in one direction and the heart is no exception. The heart has two types of valves that keep the blood flowing in the correct direction. At the roof, LA wall thickness was thickest in middle plane 2. Whereas in anterior wall, the wall thickness in left PVs vestibule plane was thicker than in middle and right PVs vestibule plane.
It receives the oxygenated, nutrient rich blood that it needs from the coronary arteries which branch off the aorta. The deoxygenated blood is then returned to the right atrium through the cardiac veins. The ventricles of the heart have thicker muscular walls than the atria.
This is because blood is pumped out of the heart at greater pressure from these chambers compared to the atria. The left ventricle also has a thicker muscular wall than the right ventricle, as seen in the adjacent image. This is due to the higher forces needed to pump blood through the systemic circuit around the body compared to the pulmonary circuit.
There are four valves within the heart which serve to prevent backflow of blood as it passes through the various chambers of the heart and out through the associated arteries.
The tricuspid valve is positioned between the right atrium and ventricle, and the mitral valve sits between the left atrium and ventricle, as seen in the adjacent image. As blood is pumped out of the ventricles through the aorta and the pulmonary arteries, these valves close to ensure the blood does not get pumped back into the respective atria it came from. The pulmonary valve sits between the right ventricle and the pulmonary artery. Its role is to prevent the backflow of blood into the right ventricle after it contracts.
0コメント