Literature DB >> 28958877

3D patient-specific models for left atrium characterization to support ablation in atrial fibrillation patients.

Maddalena Valinoti1, Claudio Fabbri2, Dario Turco3, Roberto Mantovan4, Antonio Pasini5, Cristiana Corsi6.   

Abstract

BACKGROUND: Radiofrequency ablation (RFA) is an important and promising therapy for atrial fibrillation (AF) patients. Optimization of patient selection and the availability of an accurate anatomical guide could improve RFA success rate. In this study we propose a unified, fully automated approach to build a 3D patient-specific left atrium (LA) model including pulmonary veins (PVs) in order to provide an accurate anatomical guide during RFA and without PVs in order to characterize LA volumetry and support patient selection for AF ablation.
METHODS: Magnetic resonance data from twenty-six patients referred for AF RFA were processed applying an edge-based level set approach guided by a phase-based edge detector to obtain the 3D LA model with PVs. An automated technique based on the shape diameter function was designed and applied to remove PVs and compute LA volume. 3D LA models were qualitatively compared with 3D LA surfaces acquired during the ablation procedure. An expert radiologist manually traced the LA on MR images twice. LA surfaces from the automatic approach and manual tracing were compared by mean surface-to-surface distance. In addition, LA volumes were compared with volumes from manual segmentation by linear and Bland-Altman analyses.
RESULTS: Qualitative comparison of 3D LA models showed several inaccuracies, in particular PVs reconstruction was not accurate and left atrial appendage was missing in the model obtained during RFA procedure. LA surfaces were very similar (mean surface-to-surface distance: 2.3±0.7mm). LA volumes were in excellent agreement (y=1.03x-1.4, r=0.99, bias=-1.37ml (-1.43%) SD=2.16ml (2.3%), mean percentage difference=1.3%±2.1%).
CONCLUSIONS: Results showed the proposed 3D patient-specific LA model with PVs is able to better describe LA anatomy compared to models derived from the navigation system, thus potentially improving electrograms and voltage information location and reducing fluoroscopic time during RFA. Quantitative assessment of LA volume derived from our 3D LA model without PVs is also accurate and may provide important information for patient selection for RFA.
Copyright © 2017 Elsevier Inc. All rights reserved.

Entities:  

Keywords:  Atrial fibrillation; Left atrium volume; Magnetic resonance angiography; Patient-specific 3D left atrium model; Radiofrequency ablation

Mesh:

Year:  2017        PMID: 28958877     DOI: 10.1016/j.mri.2017.09.012

Source DB:  PubMed          Journal:  Magn Reson Imaging        ISSN: 0730-725X            Impact factor:   2.546


  5 in total

1.  A fully automated left atrium segmentation approach from late gadolinium enhanced magnetic resonance imaging based on a convolutional neural network.

Authors:  Davide Borra; Alice Andalò; Michelangelo Paci; Claudio Fabbri; Cristiana Corsi
Journal:  Quant Imaging Med Surg       Date:  2020-10

2.  Evaluation of Stroke Risk in Patients With Atrial Fibrillation Using Morphological and Hemodynamic Characteristics.

Authors:  Lingfeng Wang; Zidun Wang; Runxin Fang; Zhi-Yong Li
Journal:  Front Cardiovasc Med       Date:  2022-04-29

3.  Quantifying Intermembrane Distances with Serial Image Dilations.

Authors:  Tristan Raisch; Momina Khan; Steven Poelzing
Journal:  J Vis Exp       Date:  2018-09-28       Impact factor: 1.355

4.  The Impact of Left Atrium Appendage Morphology on Stroke Risk Assessment in Atrial Fibrillation: A Computational Fluid Dynamics Study.

Authors:  Alessandro Masci; Lorenzo Barone; Luca Dedè; Marco Fedele; Corrado Tomasi; Alfio Quarteroni; Cristiana Corsi
Journal:  Front Physiol       Date:  2019-01-22       Impact factor: 4.566

5.  A Computational Framework to Benchmark Basket Catheter Guided Ablation in Atrial Fibrillation.

Authors:  Martino Alessandrini; Maddalena Valinoti; Laura Unger; Tobias Oesterlein; Olaf Dössel; Cristiana Corsi; Axel Loewe; Stefano Severi
Journal:  Front Physiol       Date:  2018-09-21       Impact factor: 4.566

  5 in total

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