Standardized unfold mapping: a technique to permit left atrial regional data display and analysis

Williams, Steven E.; Tobon-Gómez, Catalina; Zuluaga, María A.; Chubb, Henry; Butakoff, Constantine; Karim, Rashed; Ahmed, Elena; Cámara, Óscar; Rhode, Kawal

doi:10.1007/s10840-017-0281-3

Cited by 21 publications

(32 citation statements)

References 19 publications

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“…SUM provides more detailed regional division (24 regions (Williams et al, 2017) vs. our 5 regions) but this regional division may be inaccurate due to the small size of some regions and the influence of registration errors. FRF brings more control on the 3D regional division by manual placement of seed points and, in the flattening, constraining each region to its specific 2D area.…”

Section: Discussionmentioning

confidence: 99%

Fast Quasi-Conformal Regional Flattening of the Left Atrium

Nuñez‐Garcia¹,

Bernardino²,

Alarcón³

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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Two-dimensional representation of 3D anatomical structures is a simple and intuitive way for analysing patient information across populations and image modalities. It also allows convenient visualizations that can be included in clinical reports for a fast overview of the whole structure. While cardiac ventricles, especially the left ventricle, have an established standard representation (e.g. bull's eye plot), the 2D depiction of the left atrium (LA) is challenging due to its sub-structural complexity including the pulmonary veins (PV) and the left atrial appendage (LAA). Quasiconformal flattening techniques, successfully applied to cardiac ventricles, require additional constraints in the case of the LA to place the PV and LAA in the same geometrical 2D location for different cases. Some registrationbased methods have been proposed but 3D (or 2D) surface registration is time-consuming and prone to errors if the geometries are very different. We propose a novel atrial flattening methodology where a quasi-conformal 2D map of the LA is obtained quickly and without errors related to registration. In our approach, the LA is divided into 5 regions which are then mapped to their analogue two-dimensional regions. A dataset of 67 human left atria from magnetic resonance images (MRI) was studied to derive a population-based 2D LA template representing the averaged relative locations of the PVs and LAA. The clinical application of the proposed methodology is illustrated on different use cases including the integration of MRI and electroanatomical data.

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Section: Discussionmentioning

confidence: 99%

Fast Quasi-Conformal Regional Flattening of the Left Atrium

Nuñez‐Garcia¹,

Bernardino²,

Alarcón³

et al. 2020

IEEE Trans. Visual. Comput. Graphics

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“…A standardised parcellation of the segmented LA surface is necessary to define the areas where gaps will be searched and to have a common reference system for population-based analysis. We used a parcellation created on a template left atrium (atlas mesh), as we proposed in Williams et al (2017). An extra separation between left and right parts of the LA was added, leading to 28 regions.…”

Section: Semi-automatic Parcellation Of the Lamentioning

confidence: 99%

“…Afterwards, region labels were also brought to the original (non-registered) LA mesh, where the subsequent analysis was performed. Additionally, we used the 2D SUM representation (Williams et al, 2017) of the LA template for visualization purposes.…”

Section: Surface Mesh Pre-processingmentioning

confidence: 99%

Mind the gap: Quantification of incomplete ablation patterns after pulmonary vein isolation using minimum path search

Nuñez‐Garcia

Cámara

O’Neill

et al. 2019

Medical Image Analysis

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Pulmonary vein isolation (PVI) is a common procedure for the treatment of atrial fibrillation (AF) since the initial trigger for AF frequently originates in the pulmonary veins. A successful isolation produces a continuous lesion (scar) completely encircling the veins that stops activation waves from propagating to the atrial body. Unfortunately, the encircling lesion is often incomplete, becoming a combination of scar and gaps of healthy tissue. These gaps are potential causes of AF recurrence, which requires a redo of the isolation procedure. Late-gadolinium enhanced cardiac magnetic resonance (LGE-CMR) is a non-invasive method that may also be used to detect gaps, but it is currently a time-consuming process, prone to high inter-observer variability. In this paper, we present a method to semi-automatically identify and quantify ablation gaps. Gap quantification is performed through minimum path search in a graph where every node is a scar patch and the edges are the geodesic distances between patches. We propose the Relative Gap Measure (RGM) to estimate the percentage of gap around a vein, which is defined as the ratio of the overall gap length and the total length of the path that encircles the vein. Additionally, an advanced version of the RGM has been developed to integrate gap quantification estimates from different scar segmentation techniques into a single figure-of-merit. Population-based statistical and regional analysis of gap distribution was performed using a standardised parcellation of the left atrium. We have evaluated our method on synthetic and clinical data from 50 AF patients who underwent PVI with radiofrequency ablation. The population-based analysis concluded that the left superior PV is more prone to lesion gaps while the left inferior PV tends to have less gaps (p < 0.05 in both cases), in the processed data. This type of information can be very useful for the optimization and objective assessment of PVI interventions.

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“…Localization and quantification of the atrial scars can be made using the EAM system, which only focuses on the surface area of the LA. Inspired by this, we proposed a method to allow simultaneous representation of multiple parameters on a surface based on the template of an average LA mesh that is similar as Williams et al [11]. In this scheme, the atrial scar can be classified on the flattened surface projected from the 3D LA geometry, thereafter both the errors due to LA wall thickness and the misregistration of the WHS can be mitigated.…”

Section: Projection Of the Atrial Myocardiummentioning

confidence: 99%

Atrial Scar Segmentation via Potential Learning in the Graph-Cut Framework

Yang

et al. 2019

Statistical Atlases and Computational Models of the Heart. Atrial Segmentation and LV Quantification Challenges

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Late Gadolinium Enhancement Magnetic Resonance Imaging (LGE MRI) emerges as a routine scan for patients with atrial fibrillation (AF). However, due to the low image quality automating the quantification and analysis of the atrial scars is challenging. In this study, we proposed a fully automated method based on the graph-cut framework, where the potential of the graph is learned on a surface mesh of the left atrium (LA), using an equidistant projection and a deep neural network (DNN). For validation, we employed 100 datasets with manual delineation. The results showed that the performance of the proposed method was improved and converged with respect to the increased size of training patches, which provide important features of the structural and texture information learned by the DNN. The segmentation could be further improved when the contribution from the t-link and n-link is balanced, thanks to the inter-relationship learned by the DNN for the graph-cut algorithm. Compared with the existing methods which mostly acquired an initialization from manual delineation of the LA or LA wall, our method is fully automated and has demonstrated great potentials in tackling this task. The accuracy of quantifying the LA scars using the proposed method was 0.822, and the Dice score was 0.566. The results are promising and the method can be useful in diagnosis and prognosis of AF.

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Standardized unfold mapping: a technique to permit left atrial regional data display and analysis

Cited by 21 publications

References 19 publications

Fast Quasi-Conformal Regional Flattening of the Left Atrium

Fast Quasi-Conformal Regional Flattening of the Left Atrium

Mind the gap: Quantification of incomplete ablation patterns after pulmonary vein isolation using minimum path search

Atrial Scar Segmentation via Potential Learning in the Graph-Cut Framework

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