Bhat Vikas R, Kotegar Karunakar, Anitha H
Department of Biomedical Engineering, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Department of Data Science and Computer Applications, Manipal Institute of Technology, Manipal Academy of Higher Education, Manipal, Karnataka, 576104, India.
Sci Rep. 2025 Jul 11;15(1):25115. doi: 10.1038/s41598-025-09466-1.
The human heart produces electrical signals to contract and relax its muscles that helps in its blood pumping activities. These electrical impulses give rise to electric potentials on the body surface and tiny magnetic field around the thorax. These functional activities can be investigated using Electro/Magnetocardiogram (E/MCG). The more challenging task in the E/MCG research is to image the cardiac dysfunctions in three dimensions not at the surface level but at the source level and this is called the inverse problem. To solve this, one has to model a generic structure of the discretised heart enclosed in the thorax mesh and their spatial relation with location of the MCG detectors, called forward problem. In this current research, sources in a homogeneous volume conductor model is used in the construction of forward problem. A novel algorithm is implemented that uses Vectocardiography (VCG) signals in the forward problem of MCG. Another objective of this paper includes the utilization of dynamic lead field based on VCG orientations in the inverse problem. In this study, the ill-posed problems are solved using Bayesian approach and the results are compared with the deterministic approach for measurements on noise signals. The analysis revealed that the proposed algorithms with VCG priors (that are extracted from the VCG signals) in the Bayesian framework significantly improved MCG source localization in cases of Myocardial Ischemia. Analysis from the study showed that the proposed algorithms with VCG priors in probabilistic methods significantly captured a good region of spread of the reconstructed borders of inverse solutions. The average spread for deterministic methods was around 3.5 - 4.3cm in the diseased cases with simulated true ruptured region of spread being 2.5cm. In contrast, Bayesian methods and total variation methods with VCG priors reduced the spread to 2.9 to 3.03cm, respectively. The introduction of VCG signals in the forward problem of MCG not only increases the accuracy of cardiomagnetic imaging but also provides a path for more reliable diagnostic tools in cardiology.
人类心脏产生电信号以收缩和舒张其肌肉，这有助于其血液泵送活动。这些电脉冲会在身体表面产生电势，并在胸部周围产生微小的磁场。这些功能活动可以通过心电图/心磁图（E/MCG）进行研究。E/MCG研究中更具挑战性的任务是在三维空间中对心脏功能障碍进行成像，不是在表面水平，而是在源水平，这被称为逆问题。为了解决这个问题，必须对包含在胸部网格中的离散心脏的一般结构及其与MCG探测器位置的空间关系进行建模，这称为正问题。在当前的这项研究中，在构建正问题时使用了均匀体积导体模型中的源。实现了一种新颖的算法，该算法在MCG的正问题中使用向量心电图（VCG）信号。本文的另一个目标包括在逆问题中利用基于VCG方向的动态导联场。在本研究中，使用贝叶斯方法解决不适定问题，并将结果与确定性方法对噪声信号测量的结果进行比较。分析表明，在贝叶斯框架中具有VCG先验（从VCG信号中提取）的所提出算法在心肌缺血情况下显著改善了MCG源定位。研究分析表明，概率方法中具有VCG先验的所提出算法显著捕获了逆解重建边界的良好扩展区域。在模拟真实破裂区域扩展为2.5cm的患病病例中，确定性方法的平均扩展约为3.5 - 4.3cm。相比之下，具有VCG先验的贝叶斯方法和总变分方法分别将扩展减少到2.9至3.03cm。在MCG的正问题中引入VCG信号不仅提高了心脏磁成像的准确性，还为心脏病学中更可靠的诊断工具提供了一条途径。
