Reproducibility of Atrial Fibrosis Assessment Using CMR Imaging and an Open Source Platform

Sim, Iain; Razeghi, Orod; Karim, Rashed; Chubb, Henry; Whitaker, John; O’Neill, Louisa; Mukherjee, Rahul; Roney, Caroline; Razavi, Reza; Wright, Matthew; O’Neill, Mark; Niederer, Steven; Williams, Steven E.

doi:10.1016/j.jcmg.2019.03.027

Cited by 24 publications

(32 citation statements)

References 4 publications

(1 reference statement)

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“…MRI data consisted of contrast enhanced magnetic resonance angiogram (CE-MRA) scans, which were used to delineate the left atrial (LA) endocardial wall, together with LGE-MRI data, which were processed for fibrosis tissue distribution. Image acquisition details have been previously published ( Sim et al, 2019 ) and are described in the Supplementary Material . Ethical approval was granted by regional ethics committee (17/LO/0150 and 15/LO/1803).…”

Section: Methodsmentioning

confidence: 99%

“…To build an anatomical model for each patient, the left atrium was segmented from the CE-MRA images using semi-automated tools within CemrgApp software 1 ( Sim et al, 2019 ; Razeghi et al, 2020 , see Figure 1A ). LGE-MRI scans were registered with CE-MRA images to inform the distribution of fibrotic tissue incorporated into each virtual patient model (see Figure 1B ).…”

Section: Methodsmentioning

confidence: 99%

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In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation

et al. 2020

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Catheter ablation therapy for persistent atrial fibrillation (AF) typically includes pulmonary vein isolation (PVI) and may include additional ablation lesions that target patient-specific anatomical, electrical, or structural features. Clinical centers employ different ablation strategies, which use imaging data together with electroanatomic mapping data, depending on data availability. The aim of this study was to compare ablation techniques across a virtual cohort of AF patients. We constructed 20 paroxysmal and 30 persistent AF patientspecific left atrial (LA) bilayer models incorporating fibrotic remodeling from late-gadolinium enhancement (LGE) MRI scans. AF was simulated and post-processed using phase mapping to determine electrical driver locations over 15 s. Six different ablation approaches were tested: (i) PVI alone, modeled as wide-area encirclement of the pulmonary veins; PVI together with: (ii) roof and inferior lines to model posterior wall box isolation; (iii) isolating the largest fibrotic area (identified by LGE-MRI); (iv) isolating all fibrotic areas; (v) isolating the largest driver hotspot region [identified as high simulated phase singularity (PS) density]; and (vi) isolating all driver hotspot regions. Ablation efficacy was assessed to predict optimal ablation therapies for individual patients. We subsequently trained a random forest classifier to predict ablation response using (a) imaging metrics alone, (b) imaging and electrical metrics, or (c) imaging, electrical, and ablation lesion metrics. The optimal ablation approach resulting in termination, or if not possible atrial tachycardia (AT), varied among the virtual patient cohort: (i) 20% PVI alone, (ii) 6% box ablation, (iii) 2% largest fibrosis area, (iv) 4% all fibrosis areas, (v) 2% largest driver hotspot, and (vi) 46% all driver hotspots. Around 20% of cases remained in AF for all ablation strategies. The addition of patientspecific and ablation pattern specific lesion metrics to the trained random forest classifier improved predictive capability from an accuracy of 0.73 to 0.83. The trained classifier results demonstrate that the surface areas of pre-ablation driver regions and of fibrotic tissue not isolated by the proposed ablation strategy are both important for predicting ablation outcome. Overall, our study demonstrates the need to select the optimal ablation Roney et al.

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Section: Methodsmentioning

confidence: 99%

Section: Methodsmentioning

confidence: 99%

In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation

et al. 2020

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“…The LGE-CMR scan is loaded into the application, where it is segmented. A surface mesh is generated from the segmentation, where the scalar values of each node corresponds to the signal intensity for the scar, as documented in [7].…”

Section: Methodsmentioning

confidence: 99%

Software Framework to Quantify Pulmonary Vein Isolation Atrium Scar Tissue

Solís-Lemus

Razeghi

Roney

et al. 2020

2020 Computing in Cardiology Conference (CinC)

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Pulmonary vein isolation (PVI) is a recommended treatment for drug refractory atrial fibrillation (AF). The ablation causes non-conductive scar tissue that separates the body of the atrium from regions around the pulmonary veins that trigger AF. This scar tissue can be seen in the atrium using cardiac MR. We aim to develop a systematic workflow to quantify the location and size of scar tissue due to PVI from CMR images to evaluate ablation procedures. Input meshes were created from segmented late-gadolineum-enhanced (LGE) cardiac magnetic resonance (CMR) scans with scalar values at each node representing the signal intensity of the scar. The software has three features: (i) Calculating the area of the scar tissue, (ii) assessing ablation lesions to identify the percentage of the pulmonary vein encircled by scar and the number of gaps in it, and (iii) comparing pre-and postablation scar tissues qualitatively. Six patients were assessed as a proof of concept, where patients had undergone pre-and post-ablation scans. In all tests, an increase in fibrotic tissue was found, from averages of 2+1.9% and 36+18%. Post-ablation lesions were assessed showing an average of 75+12% pulmonary vein encirclement, with gaps ranging from 2 to 4. The software presented is a semi-automated, user friendly framework where users are able to assess an ablation procedure.

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“…One of the challenges associated with clinical implementation of atrial LGE imaging is the requirement for standardised image processing techniques and, as such, Sim et al published a standardised, reproducible open-source platform for AF assessment. [ 90 ] Areas of fibrosis may be identified as areas of low voltage and ablated (box isolation of fibrotic areas[ 74 ]) or imaging data may be used to identify areas of high LGE intensity. Kircher et al compared applying PVI together with either linear ablation or ablation of low-voltage areas to find that ablating low-voltage areas increased arrhythmia-free survival rate.…”

Section: Cardiac Mapping Techniquesmentioning

confidence: 99%

“…[ 131 ] However, patient-specific modelling of AF is challenging due to the anatomical and structural complexity of the atria and the dynamic nature of the electrical substrate. Future research into improved methodologies for model construction, calibration and uncertainty quantification is required for aspects including segmentation,[ 90 ] anatomical structures,[ 128 ] electrical and structural anisotropy,[ 52 , 132 , 133 ] repolarisation heterogeneity and restitution,[ 134 ] conduction heterogeneity,[ 51 ] registration,[ 135 ] fibrotic remodelling[ 118 ] and performing predictions on clinical timescales. [ 136 ]…”

Section: Future Perspectivesmentioning

confidence: 99%

Challenges Associated with Interpreting Mechanisms of AF

Roney

Wit

Peters

2020

Arrhythm Electrophysiol Rev

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Determining optimal treatment strategies for complex arrhythmogenesis in AF is confounded by the lack of consensus regarding the mechanisms causing AF. Studies report different mechanisms for AF, ranging from hierarchical drivers to anarchical multiple activation wavelets. Differences in the assessment of AF mechanisms are likely due to AF being recorded across diverse models using different investigational tools, spatial scales and clinical populations. The authors review different AF mechanisms, including anatomical and functional re-entry, hierarchical drivers and anarchical multiple wavelets. They then describe different cardiac mapping techniques and analysis tools, including activation mapping, phase mapping and fibrosis identification. They explain and review different data challenges, including differences between recording devices in spatial and temporal resolutions, spatial coverage and recording surface, and report clinical outcomes using different data modalities. They suggest future research directions for investigating the mechanisms underlying human AF.

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Reproducibility of Atrial Fibrosis Assessment Using CMR Imaging and an Open Source Platform

Cited by 24 publications

References 4 publications

In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation

In silico Comparison of Left Atrial Ablation Techniques That Target the Anatomical, Structural, and Electrical Substrates of Atrial Fibrillation

Software Framework to Quantify Pulmonary Vein Isolation Atrium Scar Tissue

Challenges Associated with Interpreting Mechanisms of AF

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