A pipeline for developing digital cardiac twins integrating cardiovascular magnetic resonance and electrocardiographic imaging: results from the MyoFit46 study

Abstract Background Personalised cardiovascular medicine uses multimodal screening and diagnostic tools, spanning from organ to tissue to molecular levels, to generate a complex and detailed patient phenotype. Cardiac digital twins are powerful tools capable of merging and explore these multimodal data by developing virtual, mechanistic and predictive versions of the patient's heart. State-of-the-art pipelines are mainly based on artificial intelligence and optimization process to replicate clinical data and not used it as an input. We present a proof-of-principle pipeline for building digital heart twins based on cardiovascular magnetic resonance (CMR) imaging and integrated electrocardiographic imaging (ECGI) for diverse clinical scenarios by integrating them into the model (Fig. 1). Methods CMR at 3T was performed on participants all born in the same week in March 1946, as part of the longest-running continued surveillance birth cohort: the National Survey of Health and Development. Cine steady-state free precession end-diastolic frames provided anatomical information per participant while CMR multiparametric mapping and late gadolinium enhancement (LGE) provided tissue characteristics to the electrical myocardial model used in the finite element solver Alya. Lastly, the ECGI activation map was used as a source to define the intrinsic endocardial activation sequence by applying a case-specific correction based on wall thickness and myofibers orientation. Activation-repolarization intervals map was used to introduce the personalised repolarization heterogeneities. Results In total, 406 prospectively recruited participants (77.8 ± 0.1 years, 44% male) had multiparametric CMR with ECGI for digital twin reconstruction. The pipeline’s performance was evaluated by measuring the root mean square error (RMSE) between the ECGI activation and repolarization maps with the in-silico ones obtained without applying the proposed pipeline (Baseline) and after its application (ecgi2model). Results obtained using ecgi2model pipeline presented a significantly reduced activation and repolarization RMSE (Fig. 2b), even providing accurate results in patients with different LGE patterns (Fig. 2c). Conclusion Personalised virtual heart models can be constructed at scale using real-world myocardial structural, functional, and electrical properties, from advanced CMR and ECGI data. Proposed digital twins provide a framework for personalised arrhythmic risk assessment based on real clinical data and also its applicability for in-silico clinical trials. Fig.1 Digital twins pipeline. Fig.2 a) Flattened epicardial map. b) RMSE of activation (ACT), repolarization (RPL) and activation-repolarization intervals (ARI) for the different methods methods. c) ACT and RPL maps obtained from ECGI, baseline simulation and ecgi2model simulation in a healthy participant with normal myocardial tissue, another with subendocardial LGE, and another with subepicardial LGE.Figure 1Figure 2