Simulation models of current density imaging in studying cardiac states
Bibliographic record
Abstract
Electro-mechanical disorders in cardiac function result in arrhythmias. Due to the non-stationary nature of these arrhythmias and, owing to lethality associated with certain type of arrhythmias, they are challenging to study. Most of the existing studies are limited in that they extract electrical activity from surface intracardiac electrical activity, either through the use of electrical or optical mapping. One way of studying current pathways inside and through biological tissues is by using Magnetic Resonance Imaging (MRI) based Low Frequency Current Density Imaging (LFCDI). For the first time CDI was used to study ex-vivo beating hearts in different cardiac states. It should be said that; this approach involves heavy logistical and procedural complexity, hence, it would be beneficial to adapt existing electrophysiological computer models to investigate and simulate current density maps specific to studying cardiac function. In achieving this, the proposed work presents an approach to model the current density maps in 3D and study the current distributions in different electrophysiological states of the heart. The structural and fiber orientation of the heart used in this study were extracted using MRI-based Diffusion Tensor Imaging. The monodomain and bidomain Aliev-Panfilov electrophysiological models were used for CDI modeling, and the results indicate that different states were distinguishable using range and correlation of simulated current density maps. The obtained results through modeling were corroborated with actual experimental CDI data from porcine hearts. Individually and comparatively, the experimental and simulation results for various states have the same trend in terms of variations (trend correlation coefficients ≥ 0.98) and state correlations (trend correlation coefficients ≥ 0.89). The results also show that the root mean square (RMS) error in average range ratios between bidomain CDI model results and real CDI data is 0.1972 and the RMS error in state correlations between bidomain CDI model results and real CDI data is 0.2833. These results indicate, as expected, the proposed bidomain model simulation of CDI corroborates well with experimental data and can serve as a valuable tool for studying lethal cardiac arrhythmias under different simulation conditions that are otherwise not possible or difficult in a real-world experimental setup.
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How this classification was reachedexpand
Full frame distilled prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. Learned from the 10,348 direct Codex labels and 10,348 direct Gemma labels. Candidate is the union of thresholded teacher heads; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels or direct frontier model labels.
Codex and Gemma teacher scores by category
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.000 | 0.000 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.001 | 0.000 |
| Bibliometrics | 0.000 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.000 | 0.000 |
| Open science | 0.000 | 0.000 |
| Research integrity | 0.000 | 0.001 |
| Insufficient payload (model declined to judge) | 0.000 | 0.000 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one teacher head, not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".