Pipeline for Forward Modeling and Source Imaging of Magnetocardiographic Recordings via Spatiotemporal Kalman Filtering
| Autor: | Laith Hamid, Michael Siniatchkin, Nawar Habboush, Ulrich Stephani, Andreas Galka |
|---|---|
| Rok vydání: | 2018 |
| Předmět: |
0301 basic medicine
Computer science Pipeline (computing) 0206 medical engineering 02 engineering and technology Signal-To-Noise Ratio 03 medical and health sciences symbols.namesake Signal-to-noise ratio medicine Humans Signal processing Magnetocardiography medicine.diagnostic_test Brain Records Magnetic resonance imaging Signal Processing Computer-Assisted Kalman filter Inverse problem 020601 biomedical engineering Finite element method 030104 developmental biology Additive white Gaussian noise symbols Tomography Algorithm Electromagnetic Phenomena Head Algorithms Software |
| Zdroj: | EMBC |
| ISSN: | 2694-0604 |
| Popis: | the aim of this proof-of-concept work was to apply the spatiotemporal Kalman filter (STKF) algorithm to magnetocardiographic (MCG) recordings of the heart. Due to the lack of standardized software and pipelines for MCG source imaging, we needed to construct a pipeline for MCG forward modeling before we could apply the STKF method. In the forward module, the finite element method (FEM) solvers in SimBio software are used to solve the MCG forward problem. In the inverse module, STKF and Low Resolution Brain Electromagnetic Tomography (LORETA) algorithms are applied. The work was conducted using two simulated datasets contaminated with different levels of additive white Gaussian noise (AWGN). Then the inverse problem was solved using both LORETA and STKF. The results indicate that STKF outperformed LORETA for MCG datasets with low signal-to-noise ratio (SNR). In the future clinical MCG recordings and more sophisticated simulations will be used to evaluate the accuracy of MCG source imaging via STKF. |
| Databáze: | OpenAIRE |
| Externí odkaz: |
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