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Highlights: • Non-linear iterative structural normalization method focused on the deep brain. • Multi-modality image data from deep brain stimulation patients. • Comparison of ANTS, FNIRT and DRAMMS for the non-linear registrations using different settings for each. • Evaluation of the registration tools based on the analysis of 58 structures of the deep brain segmented manually by a single expert. • ANTS was identified as the best performing non-linear registration tool., Deep brain stimulation (DBS) therapy requires extensive patient-specific planning prior to implantation to achieve optimal clinical outcomes. Collective analysis of patient’s brain images is promising in order to provide more systematic planning assistance. In this paper the design of a normalization pipeline using a group specific multi-modality iterative template creation process is presented. The focus was to compare the performance of a selection of freely available registration tools and select the best combination. The workflow was applied on 19 DBS patients with T1 and WAIR modality images available. Non-linear registrations were computed with ANTS, FNIRT and DRAMMS, using several settings from the literature. Registration accuracy was measured using single-expert labels of thalamic and subthalamic structures and their agreement across the group. The best performance was provided by ANTS using the High Variance settings published elsewhere. Neither FNIRT nor DRAMMS reached the level of performance of ANTS. The resulting normalized definition of anatomical structures were used to propose an atlas of the diencephalon region defining 58 structures using data from 19 patients.