Computational modeling and simulations are increasingly being used to complement experimental testing for analysis of safety and efficacy of medical devices. Multiple voxel- and surface-based whole- and partial-body models have been proposed in the literature, typically with spatial resolution in the range of 1–2 mm and with 10–50 different tissue types resolved. We have developed a multimodal imaging-based detailed anatomical model of the human head and neck, named “MIDA”. The model was obtained by integrating three different magnetic resonance imaging (MRI) modalities, the parameters of which were tailored to enhance the signals of specific tissues: i) structural T1- and T2-weighted MRIs; a specific heavily T2-weighted MRI slab with high nerve contrast optimized to enhance the structures of the ear and eye; ii) magnetic resonance angiography (MRA) data to image the vasculature, and iii) diffusion tensor imaging (DTI) to obtain information on anisotropy and fiber orientation. The unique multimodal high-resolution approach allowed resolving 153 structures, including several distinct muscles, bones and skull layers, arteries and veins, nerves, as well as salivary glands. The model offers also a detailed characterization of eyes, ears, and deep brain structures. A special automatic atlas-based segmentation procedure was adopted to include a detailed map of the nuclei of the thalamus and midbrain into the head model. The suitability of the model to simulations involving different numerical methods, discretization approaches, as well as DTI-based tensorial electrical conductivity, was examined in a case-study, in which the electric field was generated by transcranial alternating current stimulation. The voxel- and the surface-based versions of the models are freely available to the scientific community.

Abstract-Deep brain stimulation (DBS) of the subthalamic nucleus (STN) has been shown to reduce some of the symptoms of advanced, levodopa-responsive Parkinson's disease that are not adequately controlled with medication. However, the precise mechanism of the therapeutic action of DBS is still unclear. Stimulation-induced side effects are not uncommon and require electrical "dose" adjustments. Quantitative methods are needed to fully characterize the electric field in the deep brain region that surrounds the electrodes in order to help with adjustments and maximize the efficacy of the device.Herein we report a magnetic resonance imaging (MRI)-based head model proposed for analysis of fields generated by deep brain stimulation (DBS). The model was derived from multimodal image data at 0.5mm isotropic spatial resolution and distinguishes 142 anatomical structures, including the basal ganglia and 38 nuclei of the thalamus. Six bipolar electrode configurations (1-2, 1-3, 1-4, 2-3, 2-4, 3-4) were modeled in order to assess the effects of the inter-electrode distance of the electric field. Increasing the distance between the electrodes results in an attenuated stimulation, with up to 25% reduction in electric field amplitude delivered (2-3 vs. 1-4). The map of the deep brain structures provided a highly precise anatomical detail which is useful for the quantitative assessment of current spread around the electrode and a better evaluation of the stimulation setting for the treatment optimization.

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