Literature DB >> 28541896

Solving Inaccuracies in Anatomical Models for Electrocardiographic Inverse Problem Resolution by Maximizing Reconstruction Quality.

Miguel Rodrigo, Andreu M Climent, Alejandro Liberos, Ismael Hernandez-Romero, Angel Arenal, Javier Bermejo, Francisco Fernandez-Aviles, Felipe Atienza, Maria S Guillem.   

Abstract

Electrocardiographic Imaging has become an increasingly used technique for non-invasive diagnosis of cardiac arrhythmias, although the need for medical imaging technology to determine the anatomy hinders its introduction in the clinical practice. This paper explores the ability of a new metric based on the inverse reconstruction quality for the location and orientation of the atrial surface inside the torso. Body surface electrical signals from 31 realistic mathematical models and four AF patients were used to estimate the optimal position of the atria inside the torso. The curvature of the L-curve from the Tikhonov method, which was found to be related to the inverse reconstruction quality, was measured after application of deviations in atrial position and orientation. Independent deviations in the atrial position were solved by finding the maximal L-curve curvature with an error of 1.7 ± 2.4 mm in mathematical models and 9.1 ± 11.5 mm in patients. For the case of independent angular deviations, the error in location by using the L-curve was 5.8±7.1° in mathematical models and 12.4° ± 13.2° in patients. The ability of the L-curve curvature was tested also under superimposed uncertainties in the three axis of translation and in the three axis of rotation, and the error in location was of 2.3 ± 3.2 mm and 6.4° ± 7.1° in mathematical models, and 7.9±10.7 mm and 12.1°±15.5° in patients. The curvature of L-curve is a useful marker for the atrial position and would allow emending the inaccuracies in its location.

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Year:  2017        PMID: 28541896     DOI: 10.1109/TMI.2017.2707413

Source DB:  PubMed          Journal:  IEEE Trans Med Imaging        ISSN: 0278-0062            Impact factor:   10.048


  10 in total

1.  Noninvasive Assessment of Complexity of Atrial Fibrillation: Correlation With Contact Mapping and Impact of Ablation.

Authors:  Miguel Rodrigo; Andreu M Climent; Ismael Hernández-Romero; Alejandro Liberos; Tina Baykaner; Albert J Rogers; Mahmood Alhusseini; Paul J Wang; Francisco Fernández-Avilés; Maria S Guillem; Sanjiv M Narayan; Felipe Atienza
Journal:  Circ Arrhythm Electrophysiol       Date:  2020-02-13

2.  ECG-Based Reconstruction of Heart Position and Orientation with Bayesian Optimization.

Authors:  Jaume Coll-Font; Setareh Ariafar; Dana H Brooks
Journal:  Comput Cardiol (2010)       Date:  2018-04-05

3.  Body Surface Potential Mapping: Contemporary Applications and Future Perspectives.

Authors:  Jake Bergquist; Lindsay Rupp; Brian Zenger; James Brundage; Anna Busatto; Rob S MacLeod
Journal:  Hearts (Basel)       Date:  2021-11-05

4.  Improving Localization of Cardiac Geometry Using ECGI.

Authors:  Jake A Bergquist; Jaume Coll-Font; Brian Zenger; Lindsay C Rupp; Wilson W Good; Dana H Brooks; Rob S MacLeod
Journal:  Comput Cardiol (2010)       Date:  2021-02-10

5.  Reconstruction of cardiac position using body surface potentials.

Authors:  Jake A Bergquist; Jaume Coll-Font; Brian Zenger; Lindsay C Rupp; Wilson W Good; Dana H Brooks; Rob S MacLeod
Journal:  Comput Biol Med       Date:  2022-01-20       Impact factor: 4.589

6.  A new approach to the intracardiac inverse problem using Laplacian distance kernel.

Authors:  Raúl Caulier-Cisterna; Sergio Muñoz-Romero; Margarita Sanromán-Junquera; Arcadi García-Alberola; José Luis Rojo-Álvarez
Journal:  Biomed Eng Online       Date:  2018-06-20       Impact factor: 2.819

7.  Nonlinear electrocardiographic imaging using polynomial approximation networks.

Authors:  Abhejit Rajagopal; Vincent Radzicki; Hua Lee; Shivkumar Chandrasekaran
Journal:  APL Bioeng       Date:  2018-10-16

8.  Effects of torso mesh density and electrode distribution on the accuracy of electrocardiographic imaging during atrial fibrillation.

Authors:  Rubén Molero; Ana González-Ascaso; Ismael Hernández-Romero; David Lundback-Mompó; Andreu M Climent; María S Guillem
Journal:  Front Physiol       Date:  2022-08-29       Impact factor: 4.755

9.  ECG Localization Method Based on Volume Conductor Model and Kalman Filtering.

Authors:  Yuki Nakano; Essam A Rashed; Tatsuhito Nakane; Ilkka Laakso; Akimasa Hirata
Journal:  Sensors (Basel)       Date:  2021-06-22       Impact factor: 3.576

10.  Statistical guidance of VT ablation.

Authors:  Miguel Rodrigo; Sanjiv M Narayan
Journal:  J Cardiovasc Electrophysiol       Date:  2018-06-07       Impact factor: 2.942

  10 in total

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