•  Implantable cardioverter‐defibrillators (ICDs) with transvenous leads often cannot be implanted in a standard manner in paediatric and congenital heart defect (CHD) patients. Currently, there is no reliable approach to predict the optimal ICD placement in these patients. •  A pipeline for constructing personalized, electrophysiological heart–torso models from clinical magnetic resonance imaging scans was developed and applied to a paediatric CHD patient. •  Optimal ICD placement was determined using patient‐specific simulations of the defibrillation process. In a patient with tricuspid valve atresia, two configurations with epicardial leads were found to have the lowest defibrillation threshold. •  We demonstrated that determining extracellular potential (Φe) gradients during the shock – without actually simulating defibrillation – was not sufficient to predict defibrillation success or failure. •  Using the proposed methodology, the optimal ICD placement in paediatric/CHD patients can be predicted computationally, which could reduce defibrillation energy if the pipeline is used as part of ICD implantation planning. Abstract  There is currently no reliable way of predicting the optimal implantable cardioverter‐defibrillator (ICD) placement in paediatric and congenital heart defect (CHD) patients. This study aimed to: (1) develop a new image processing pipeline for constructing patient‐specific heart–torso models from clinical magnetic resonance images (MRIs); (2) use the pipeline to determine the optimal ICD configuration in a paediatric tricuspid valve atresia patient; (3) establish whether the widely used criterion of shock‐induced extracellular potential (Φe) gradients ≥5 V cm−1 in ≥95% of ventricular volume predicts defibrillation success. A biophysically detailed heart–torso model was generated from patient MRIs. Because transvenous access was impossible, three subcutaneous and three epicardial lead placement sites were identified along with five ICD scan locations. Ventricular fibrillation was induced, and defibrillation shocks were applied from 11 ICD configurations to determine defibrillation thresholds (DFTs). Two configurations with epicardial leads resulted in the lowest DFTs overall and were thus considered optimal. Three configurations shared the lowest DFT among subcutaneous lead ICDs. The Φe gradient criterion was an inadequate predictor of defibrillation success, as defibrillation failed in numerous instances even when 100% of the myocardium experienced such gradients. In conclusion, we have developed a new image processing pipeline and applied it to a CHD patient to construct the first active heart–torso model from clinical MRIs.