OBJECTIVE OF THE CHALLENGE The spirit of CRT-EPiggy19 is to collectively review the current state-of-the-art for computational cardiology models and their ability to predict pacing-based therapy outcomes, as well as the identification of the most critical phases and more promising solutions in the personalization modelling pipeline. More specifically, participants will be asked to predict the electrical response of CRT and to propose the optimal device configuration in a swine model of left bundle branch block, given fully controlled data. All challenge participants will be invited to contribute to the preparation of a journal article summarizing the main findings from the CRT-EPiggy19 challenge, similarly to the CESC’10 challenge (Camara et al., Prog Biophys Mol Bio 2011). TRAINING/TEST DATASETS Some years ago, researchers at Hospital Clínic de Barcelona and Universitat Pompeu Fabra developed a swine model of left bundle branch block (LBBB) for experimental studies of CRT (Rigol et al., J Cardiovasc Transl Res 2013). Radiofrequency applications were performed to induce LBBB, and half of the animals presented a myocardial infarction located at the septal wall. Imaging data and electro-anatomical maps (EAM) were acquired at baseline, with the induced LBBB and after implantation of a CRT devicE. This rich data is well suited for evaluating some features of the different cardiac computational models available nowadays, and will be the basis of the CRT-EPiggy19 challenge. The training data will include two complete infarcted and two non-infarcted datasets (total of 4 cases), while the test data is composed of four cases for each of the two categories (infarcted vs non-infarcted; total of 8 cases). The electrical activation patterns of the training datasets have already been described with detail in Soto-Iglesias et al. (IEEE J Transl Eng Health Med 2016). Check the Datasets section for a preview of the training data and the procedure to download it. Unlike LBBB and CRT activation maps, baseline maps will not initially be released, since they do not necessarily contribute to the prediction of CRT from LBBB. Some of the main sources of variability in the personalization of cardiac models involve the extraction of anatomical data from medical images and the creation of the geometrical domain where models are run. In order to reduce this variability in the CRT-EPiggy19 challenge, biventricular finite element meshes will be provided to each participant, which were built from the segmentation of cine-MRI data. These meshes will include cardiomyocyte orientation (obtained with rule-based models; see Doste et al. Int J Numer Meth Bio 2019 for details), several regional labels (AHA regions, endo- and epi-cardial walls, different ventricles) and the local activation times projected from EAM data. Additionally, the affected AHA segments and its transmurality will be given for infarcted cases. Furthermore, for visualization and analysis purposes, 2D bi-ventricular representations will be given. EVALUATION METRICS Global and regional differences between simulated and measured CRT activation maps will be used to evaluate the prediction accuracy of each proposed model. As global metric, we will use the difference in Total Activation Time (TAT, whole heart fully activated). The TAT will also individually be assessed for the LV, the RV, as well as for each AHA segment. TAT differences will be separately analysed between simulations and measurements for infarcted vs. non-infarcted cases. Histograms of isochrones of electrical activation will be derived from simulations to estimate inter- and intra-ventricular electrical dyssynchrony (Soto-Iglesias et al., IEEE J Transl Eng Health Med 2016). Each participant will be asked to report the used hardware infrastructure, computational times and details about the implementation and a self-reported analysis for model integration onto a clinical workflow. CONTACT You are welcome to contact Oscar Camara should you have any questions at: oscar.camara@upf.edu. More details on the CRT-EPiggy19 challenge can be found in the following website: crt-epiggy19.surge.sh. LICENSE All data at the CRT-EPiggy19 challenge are released under Creative Commons (CC) licenses. CITATION If you use the CRT-EPiggy19 challenge dataset or part of it, please cite the following paper: Rigol et al., J Cardiovasc Transl Res 2013. Rigol M, Solanes N, Fernandez-Armenta J, Silva E, Doltra A, Duchateau N, Barcelo A, Gabrielli L, Bijnens B, Berruezo A, Brugada J, Sitges M. Development of a swine model of left bundle branch block for experimental studies of cardiac resynchronization therapy. J Cardiovasc Transl Res. 2013 Aug;6(4):616-22. doi: 10.1007/s12265-013-9464-1. You may also consider citing the paper, which describes the electrophysiological patterns of the training data: Soto-Iglesias et al., IEEE J Transl Eng Health Med 2016 Soto Iglesias D, Duchateau N, Kostantyn Butakov CB, Andreu D, Fernandez-Armenta J, Bijnens B, Berruezo A, Sitges M, Camara O. Quantitative Analysis of Electro-Anatomical Maps: Application to an Experimental Model of Left Bundle Branch Block/Cardiac Resynchronization Therapy. IEEE J Transl Eng Health Med. 2016 Dec 16;5:1900215. doi: 10.1109/JTEHM.2016.2634006. ACKNOWLEDGMENTS This work was supported in part by the Spanish Ministry of Science and Innovation (TIN2011-28067, REDINSCOR RD06/003/008), the Spanish Industrial and Technological Development Center (cvREMOD-CEN-20091044), the Seventh Framework Programme (FP7/2007-2013) for research, technological and demonstration under grant agreement VP2HF (no. 611823), and the Spanish Ministry of Economy and Competitiveness under the Maria de Maeztu Units of Excellence Programme (MDM-2015-0502).

Training dataset file v1.0: The compressed file includes several documents including: the biventricular 3D finite-element meshes (vtk format) for the 4 cases of the training dataset with local activation times pre- and post-CRT mapped on the mesh nodes; the acquired electro-anatomical points; some slides with 2D bull's eye plot representations of the electrical data; and a pre-print version of the Soto-Iglesias et al. paper where the 4 training datasets are fully described.