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用于心电图成像的基于电学测量的心房位置优化

Atrial location optimization by electrical measures for Electrocardiographic Imaging.

作者信息

Gisbert Víctor, Jiménez-Serrano Santiago, Roses-Albert Eduardo, Rodrigo Miguel

机构信息

ITACA Institute, Universitat Politècnica de València, Valencia, Spain.

ITACA Institute, Universitat Politècnica de València, Valencia, Spain; Proteu Tecnologia Aplicada Coop V, Spain.

出版信息

Comput Biol Med. 2020 Dec;127:104031. doi: 10.1016/j.compbiomed.2020.104031. Epub 2020 Oct 9.

Abstract

BACKGROUND

The Electrocardiographic Imaging (ECGI) technique, used to non-invasively reconstruct the epicardial electrical activity, requires an accurate model of the atria and torso anatomy. Here we evaluate a new automatic methodology able to locate the atrial anatomy within the torso based on an intrinsic electrical parameter of the ECGI solution.

METHODS

In 28 realistic simulations of the atrial electrical activity, we randomly displaced the atrial anatomy for ±2.5 cm and ±30° on each axis. An automatic optimization method based on the L-curve curvature was used to estimate the original position using exclusively non-invasive data.

RESULTS

The automatic optimization algorithm located the atrial anatomy with a deviation of 0.5 ± 0.5 cm in position and 16.0 ± 10.7° in orientation. With these approximate locations, the obtained electrophysiological maps reduced the average error in atrial rate measures from 1.1 ± 1.1 Hz to 0.5 ± 1.0 Hz and in the phase singularity position from 7.2 ± 4.0 cm to 1.6 ± 1.7 cm (p < 0.01).

CONCLUSIONS

This proposed automatic optimization may help to solve spatial inaccuracies provoked by cardiac motion or respiration, as well as to use ECGI on torso and atrial anatomies from different medical image systems.

摘要

背景

心电图成像(ECGI)技术用于无创重建心外膜电活动,需要准确的心房和躯干解剖模型。在此,我们评估一种基于ECGI解的固有电参数在躯干内定位心房解剖结构的新自动方法。

方法

在28个心房电活动的真实模拟中,我们在每个轴上对心房解剖结构进行±2.5厘米和±30°的随机移位。使用基于L曲线曲率的自动优化方法,仅利用无创数据估计原始位置。

结果

自动优化算法定位心房解剖结构时,位置偏差为0.5±0.5厘米,方向偏差为16.0±10.7°。利用这些近似位置,获得的电生理图将心房率测量的平均误差从1.1±1.1赫兹降至0.5±1.0赫兹,相位奇点位置的平均误差从7.2±4.0厘米降至1.6±1.7厘米(p<0.01)。

结论

这种提出的自动优化方法可能有助于解决由心脏运动或呼吸引起的空间不准确问题,以及在来自不同医学图像系统的躯干和心房解剖结构上使用ECGI。

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