Published December 13, 2022
| Version v1.0.0
Dataset
Open
Model Reproducibility Study on Left Atrial Fibres
Creators
- 1. King's College London
- 2. University of Edinburgh
- 3. Sapienza Università di Roma
- 4. University of Cambridge
- 5. Queen Mary University of London
Description
This dataset contains 100 models of the left atrium. Models come from 50 distinct patients, divided amongst 5 users to assess for inter- and intra-operator variability. The split used was 30 pairs (60 models) for inter-operator variability and 20 pairs (40 models) for intra operator variability. Models were created with a specific version of the software CemrgApp (cemrgapp.com), in which users processed a contrast enhanced magnetic resonance angiogram, and a late gadolinium enhanced (LGE) contrast magnetic resonance (CMR).
Two types of simulations were run on each of the 100 processed cases: baseline pacing to calculate local activation time (LAT) maps and atrial fibrillation simulations for which phase singularity (PS) maps were calculated. The openCARP simulator (Plank et al., 2021) was used to run the simulations, using the Courtemance human atrial model with AF electrical remodelling.
This dataset contains the labelled surface meshes, output to the CemrgApp software, which in turn are utilised as inputs for the electrophisiological simulations.
The dataset is split into 100 folders labelled M1 to M100. Included in file `Cases_and_Users_Paths.csv` is the pairs for each of the comparisons, whether inter- or intra-observer variability.
Notes
Additional funding acknowledgements:
British Heart Foundation (PG/15/91/31812, and PG/13/37/30280).
Kings Health Partners London National Institute for Health Research (NIHR) Biomedical Research Centre.
National Institute of Health (NIH) (R01-HL152256)
Files
Cases_and_Users_Paths.csv
Files
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Additional details
Funding
- UK Research and Innovation
- Personalised Model Based Optimal Lead Guidance in Cardiac Resynchronisation Therapy EP/M012492/1
- European Commission
- PREDICT-HF – Predicting Outcome of Rate or Rhythm Control in Patients with Atrial Fibrillation and Heart Failure 864055
- UK Research and Innovation
- Uncertainty Quantification in Prospective and Predictive Patient Specific Cardiac Models EP/P01268X/1
- UK Research and Innovation
- Wellcome EPSRC Centre for Medical Engineering NS/A000049/1
- Wellcome Trust
- King's College London Medical Engineering Centre of Research Excellence 203148
References
- Razeghi, et al. (2020). CemrgApp: An interactive medical imaging application with image processing, computer vision, and machine learning toolkits for cardiovascular research. DOI:10.1016/j.softx.2020.100570
- Plank, et al. (2021). The openCARP simulation environment for cardiac electrophysiology. DOI: 10.1016/j.cmpb.2021.106223.