Jake A Bergquist1,2,3, Jaume Coll-Font4, Brian Zenger1,2,3,5, Lindsay C Rupp1,2,3, Wilson W Good1,2,3, Dana H Brooks6, Rob S MacLeod1,2,3. 1. Scientific Computing and Imaging Institute, University of Utah, SLC, UT, USA. 2. Nora Eccles Cardiovascular Research and Training Institute, University of Utah, SLC, UT, USA. 3. Department of Biomedical Engineering, University of Utah, SLC, UT, USA. 4. Cardiovascular Bioengineering & Imaging (CBM) Lab at the Massachusetts General Hospital, Boston (MA) and Harvard Medical School, Boston, MA, USA. 5. School of Medicine, University of Utah, SLC, UT, USA. 6. Department of Electrical and Computer Engineering, Northeastern University, Boston, MA, USA.
Abstract
INTRODUCTION: Electrocardiographic imaging (ECGI) requires a model of the torso, and inaccuracy in the position of the heart is a known source of error. We previously presented a method to localize the heart when body and heart surface potentials are known. The goal of this study is to extend this approach to only use body surface potentials. METHODS: We used an iterative coordinate descent optimization to estimate the positions of the heart for several consecutive heartbeats relying on the assumption that the epicardial potential sequence is the same in each beat. The method was tested with data synthesized using measurements from a isolated-heart, torso-tank preparation. Improvement was evaluated in terms of both heart localization and ECGI accuracy. RESULTS: The geometric correction resulted in cardiac geometries closely matching ground truth geometry. ECGI accuracy increased dramatically by all metrics using the corrected geometry. DISCUSSION: Future studies will employ more realistic animal models and then human subjects. Success could impact clinical ECGI by reducing errors from respiratory movement and perhaps decrease imaging requirements, reducing both cost and logistical difficulty of ECGI, widening clinical applicability.
INTRODUCTION: Electrocardiographic imaging (ECGI) requires a model of the torso, and inaccuracy in the position of the heart is a known source of error. We previously presented a method to localize the heart when body and heart surface potentials are known. The goal of this study is to extend this approach to only use body surface potentials. METHODS: We used an iterative coordinate descent optimization to estimate the positions of the heart for several consecutive heartbeats relying on the assumption that the epicardial potential sequence is the same in each beat. The method was tested with data synthesized using measurements from a isolated-heart, torso-tank preparation. Improvement was evaluated in terms of both heart localization and ECGI accuracy. RESULTS: The geometric correction resulted in cardiac geometries closely matching ground truth geometry. ECGI accuracy increased dramatically by all metrics using the corrected geometry. DISCUSSION: Future studies will employ more realistic animal models and then human subjects. Success could impact clinical ECGI by reducing errors from respiratory movement and perhaps decrease imaging requirements, reducing both cost and logistical difficulty of ECGI, widening clinical applicability.
Authors: Miguel Rodrigo; Andreu M Climent; Alejandro Liberos; Ismael Hernandez-Romero; Angel Arenal; Javier Bermejo; Francisco Fernandez-Aviles; Felipe Atienza; Maria S Guillem Journal: IEEE Trans Med Imaging Date: 2017-05-23 Impact factor: 10.048
Authors: Darrell J Swenson; Sarah E Geneser; Jeroen G Stinstra; Robert M Kirby; Rob S MacLeod Journal: Ann Biomed Eng Date: 2011-09-10 Impact factor: 3.934
Authors: Matthijs Cluitmans; Dana H Brooks; Rob MacLeod; Olaf Dössel; María S Guillem; Peter M van Dam; Jana Svehlikova; Bin He; John Sapp; Linwei Wang; Laura Bear Journal: Front Physiol Date: 2018-09-20 Impact factor: 4.566
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