Numerical body models of children are used for designing medical devices, including but not limited to optical imaging, ultrasound, CT, EEG/MEG, and MRI. These models are used in many clinical and neuroscience research applications, such as radiation safety dosimetric studies and source localization. Although several such adult models have been reported, there are few reports of full-body pediatric models, and those described have several limitations. Some, for example, are either morphed from older children or do not have detailed segmentations. Here, we introduce a 29-month-old male whole-body native numerical model, “MARTIN”, that includes 28 head and 86 body tissue compartments, segmented directly from the high spatial resolution MRI and CT images. An advanced auto-segmentation tool was used for the deep-brain structures, whereas 3D Slicer was used to segment the non-brain structures and to refine the segmentation for all of the tissue compartments. Our MARTIN model was developed and validated using three separate approaches, through an iterative process, as follows. First, the calculated volumes, weights, and dimensions of selected structures were adjusted and confirmed to be within 6% of the literature values for the 2-3-year-old age-range. Second, all structural segmentations were adjusted and confirmed by two experienced, sub-specialty certified neuro-radiologists, also through an interactive process. Third, an additional validation was performed with a Bloch simulator to create synthetic MR image from our MARTIN model and compare the image contrast of the resulting synthetic image with that of the original MRI data; this resulted in a “structural resemblance” index of 0.97. Finally, we used our model to perform pilot MRI safety simulations of an Active Implantable Medical Device (AIMD) using a commercially available software platform (Sim4Life), incorporating the latest International Standards Organization guidelines. This model will be made available on the Athinoula A. Martinos Center for Biomedical Imaging website.

Vagus nerve stimulation (VNS) is commonly used to treat drug-resistant epilepsy and depression. The therapeutic effect of VNS depends on stimulating the afferent vagal fibers. However, the vagus is a mixed nerve containing afferent and efferent fibers, and the stimulation of cardiac efferent fibers during VNS may produce a rare but severe risk of bradyarrhythmia. This side effect is challenging to mitigate since VNS, via electrical stimulation technology used in clinical practice, requires unique electrode design and pulse optimization for selective stimulation of only the afferent fibers. Here we describe a method of VNS using micro-magnetic stimulation (µMS), which may be an alternative technique to induce a focal stimulation, enabling a selective fiber stimulation. Micro-coils were implanted into the cervical vagus nerve in adult male Wistar rats. For comparison, the physiological responses were recorded continuously before, during, and after stimulation with arterial blood pressure (ABP), respiration rate (RR), and heart rate (HR). The electrical VNS caused a decrease in ABP, RR, and HR, whereas µM-VNS only caused a transient reduction in RR. The absence of an HR modulation indicated that µM-VNS might provide an alternative technology to VNS with fewer heart-related side effects, such as bradyarrhythmia. Numerical electromagnetic simulations helped estimate the optimal coil orientation with respect to the nerve to provide information on the electric field’s spatial distribution and strength. Furthermore, a transmission emission microscope provided very high-resolution images of the cervical vagus nerve in rats, which identified two different populations of nerve fibers categorized as large and small myelinated fibers.

Purpose: Safety limits for the permitted specific absorption rate (SAR) place restrictions on pulse sequence design, especially at ultrahigh fields (≥ 7 tesla). Due to intersubject variability, the SAR is usually conservatively estimated based on standard human models that include an applied safety margin to ensure safe operation. One approach to reducing the restrictions is to create more accurate subject‐specific models from their segmented MR images. This study uses electromagnetic simulations to investigate the minimum number of tissue groups required to accurately determine SAR in the human head.Methods: Tissue types from a fully characterized electromagnetic human model with 47 tissue types in the head and neck region were grouped into different tissue clusters based on the conductivities, permittivities, and mass densities of the tissues. Electromagnetic simulations of the head model inside a parallel transmit head coil at 7 tesla were used to determine the minimum number of required tissue clusters to accurately determine the subject‐specific SAR. The identified tissue clusters were then evaluated using 2 additional well‐characterized electromagnetic human models.Results: A minimum of 4‐clusters‐plus‐air was found to be required for accurate SAR estimation. These tissue clusters are centered around gray matter, fat, cortical bone, and cerebrospinal fluid. For all 3 simulated models, the parallel transmit maximum 10g SAR was consistently determined to within an error of <12% relative to the full 47‐tissue model.Conclusion: A minimum of 4‐clusters‐plus‐air are required to produce accurate personalized SAR simulations of the human head when using parallel transmit at 7 tesla.

Recently, white-matter fiber tract pathways carrying neural signals through the brain were shown to follow curved, orthogonal grids. This study focuses on how these white-matter fibers may be selectively excited using micromagnetic stimulation (μMS), a new type of neuronal stimulation, which generates microscopic eddy currents capable of directionally activating neurons. One of the most remarkable properties of this novel type of stimulation is that the μMS fields provide unique directional activation of neuronal elements not seen with traditional electrical stimulation. An initial prototype built with SU-8 based photolithography technology shows that the structure can be fabricated. The coil design was optimized through electrical resistance calculations and electric field simulations to elicit the brain's maximal focal and directional neural responses.

We introduce a new electroencephalogram (EEG) net, which will allow clinicians to monitor EEG while tracking head motion. Motion during MRI limits patient scans, especially of children with epilepsy. EEG is also severely affected by motion-induced noise, predominantly ballistocardiogram (BCG) noise due to the heartbeat. Methods: The MotoNet was built using polymer thick film (PTF) EEG leads and motion sensors on opposite sides in the same flex circuit. EEG/motion measurements were made with a standard commercial EEG acquisition system in a 3 Tesla (T) MRI. A Kalman filtering-based BCG correction tool was used to clean the EEG in healthy volunteers. Results: MRI safety studies in 3 T confirmed the maximum heating below 1 °C. Using an MRI sequence with spatial localization gradients only, the position of the head was linearly correlated with the average motion sensor output. Kalman filtering was shown to reduce the BCG noise and recover artifact-clean EEG. Conclusions: The MotoNet is an innovative EEG net design that co-locates 32 EEG electrodes with 32 motion sensors to improve both EEG and MRI signal quality. In combination with custom gradients, the position of the net can, in principle, be determined. In addition, the motion sensors can help reduce BCG noise.

Objective. Combining magnetic resonance imaging (MRI) and electroencephalography (EEG) provides a powerful tool for investigating brain function at varying spatial and temporal scales. Simultaneous acquisition of both modalities can provide unique information that a single modality alone cannot reveal. However, current simultaneous EEG-fMRI studies are limited to a small set of MRI sequences due to the image quality and safety limitations of commercially available MR-conditional EEG nets. We tested whether the Inknet2, a high-resistance polymer thick film based EEG net that uses conductive ink, could enable the acquisition of a variety of MR image modalities with minimal artifacts by reducing the radiofrequency-shielding caused by traditional MR-conditional nets. Approach. We first performed simulations to model the effect of the EEG nets on the magnetic field and image quality. We then performed phantom scans to test image quality with a conventional copper EEG net, with the new Inknet2, and without any EEG net. Finally, we scanned five human subjects at 3 Tesla (3 T) and three human subjects at 7 Tesla (7 T) with and without the Inknet2 to assess structural and functional MRI image quality. Main results. Across these simulations, phantom scans, and human studies, the Inknet2 induced fewer artifacts than the conventional net and produced image quality similar to scans with no net present. Significance. Our results demonstrate that high-quality structural and functional multimodal imaging across a variety of MRI pulse sequences at both 3 T and 7 T is achievable with an EEG net made with conductive ink and polymer thick film technology.