Development, validation, and pilot MRI safety study of a high-resolution, open source, whole body pediatric numerical simulation model.

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.

Numerical and Experimental Analysis of Radiofrequency-Induced Heating Versus Lead Conductivity During EEG-MRI at 3 T.

This study investigates radiofrequency (RF)-induced heating in a head model with a 256-channel electroencephalogram (EEG) cap during magnetic resonance imaging (MRI). Nine computational models were implemented each with different EEG lead electrical conductivity, ranging from 1 to 5.8 × 107 S/m. The peak values of specific absorption rate (SAR) averaged over different volumes were calculated for each lead conductivity. Experimental measurements were also performed at 3-T MRI with a Gracilaria Lichenoides (GL) phantom with and without a low-conductive EEG lead cap ("InkNet"). The simulation results showed that SAR was a nonlinear function of the EEG lead conductivity. The experimental results were in line with the numerical simulations. Specifically, there was a ΔT of 1.7 °C in the GL phantom without leads compared to ΔT of 1.8 °C calculated with the simulations. Additionally, there was a ΔT of 1.5 °C in the GL phantom with the InkNet compared to a ΔT of 1.7 °C in the simulations with a cap of similar conductivity. The results showed that SAR is affected by specific location, number of electrodes, and the volume of tissue considered. As such, SAR averaged over the whole head, or even SAR averaged over volumes of 1 or 0.1 g, may conceal significant heating effects and local analysis of RF heating (in terms of peak SAR and temperature) is needed.

A Study on the Feasibility of the Deep Brain Stimulation (DBS) Electrode Localization Based on Scalp Electric Potential Recordings.

Deep Brain Stimulation (DBS) is an effective therapy for patients disabling motor symptoms from Parkinson's disease, essential tremor, and other motor disorders. Precise, individualized placement of DBS electrodes is a key contributor to clinical outcomes following surgery. Electroencephalography (EEG) is widely used to identify the sources of intracerebral signals from the potential on the scalp. EEG is portable, non-invasive, low-cost, and it could be easily integrated into the intraoperative or ambulatory environment for localization of either the DBS electrode or evoked potentials triggered by stimulation itself. In this work, we studied with numerical simulations the principle of extracting the DBS electrical pulse from the patient's EEG - which normally constitutes an artifact - and localizing the source of the artifact (i.e., the DBS electrodes) using EEG localization methods. A high-resolution electromagnetic head model was used to simulate the EEG potential at the scalp generated by the DBS pulse artifact. The potential distribution on the scalp was then sampled at the 256 electrode locations of a high-density EEG Net. The electric potential was modeled by a dipole source created by a given pair of active DBS electrodes. The dynamic Statistical Parametric Maps (dSPM) algorithm was used to solve the EEG inverse problem, and it allowed localization of the position of the stimulus dipole in three DBS electrode bipolar configurations with a maximum error of 1.5 cm. To assess the accuracy of the computational model, the results of the simulation were compared with the electric artifact amplitudes over 16 EEG electrodes measured in five patients. EEG artifacts measured in patients confirmed that simulated data are commensurate to patients' data (0 ± 6.6 µV). While we acknowledge that further work is necessary to achieve a higher accuracy needed for surgical navigation, the results presented in this study are proposed as the first step toward a validated computational framework that could be used for non-invasive localization not only of the DBS system but also brain rhythms triggered by stimulation at both proximal and distal sites in the human central nervous system.

Textile Pressure Mapping Sensor for Emotional Touch Detection in Human-Robot Interaction.

In this paper, we developed a fully textile sensing fabric for tactile touch sensing as the robot skin to detect human-robot interactions. The sensor covers a 20-by-20 cm 2 area with 400 sensitive points and samples at 50 Hz per point. We defined seven gestures which are inspired by the social and emotional interactions of typical people to people or pet scenarios. We conducted two groups of mutually blinded experiments, involving 29 participants in total. The data processing algorithm first reduces the spatial complexity to frame descriptors, and temporal features are calculated through basic statistical representations and wavelet analysis. Various classifiers are evaluated and the feature calculation algorithms are analyzed in details to determine each stage and segments' contribution. The best performing feature-classifier combination can recognize the gestures with a 93 . 3 % accuracy from a known group of participants, and 89 . 1 % from strangers.

Polymer thick film technology for improved simultaneous dEEG/MRI recording: Safety and MRI data quality.

PURPOSE: To develop a 256-channel dense-array electroencephalography (dEEG) sensor net (the Ink-Net) using high-resistance polymer thick film (PTF) technology to improve safety and data quality during simultaneous dEEG/MRI. METHODS: Heating safety was assessed with temperature measurements in an anthropomorphic head phantom during a 30-min, induced-heating scan at 7T. MRI quality assessment used B1 field mapping and functional MRI (fMRI) retinotopic scans in three humans at 3T. Performance of the 256-channel PTF Ink-Net was compared with a 256-channel MR-conditional copper-wired electroencephalography (EEG) net and to scans with no sensor net. A visual evoked potential paradigm assessed EEG quality within and outside the 3T scanner. RESULTS: Phantom temperature measurements revealed nonsignificant heating (ISO 10974) in the presence of either EEG net. In human B1 field and fMRI scans, the Ink-Net showed greatly reduced cross-modal artifact and less signal degradation than the copper-wired net, and comparable quality to MRI without sensor net. Cross-modal ballistocardiogram artifact in the EEG was comparable for both nets. CONCLUSION: High-resistance PTF technology can be effectively implemented in a 256-channel dEEG sensor net for MR conditional use at 7T and with significantly improved structural and fMRI data quality as assessed at 3T. Magn Reson Med 77:895-903, 2017. © 2016 International Society for Magnetic Resonance in Medicine.

Intracranial hemorrhage alters scalp potential distribution in bioimpedance cerebral monitoring: Preliminary results from FEM simulation on a realistic head model and human subjects.

PURPOSE: Current diagnostic neuroimaging for detection of intracranial hemorrhage (ICH) is limited to fixed scanners requiring patient transport and extensive infrastructure support. ICH diagnosis would therefore benefit from a portable diagnostic technology, such as electrical bioimpedance (EBI). Through simulations and patient observation, the authors assessed the influence of unilateral ICH hematomas on quasisymmetric scalp potential distributions in order to establish the feasibility of EBI technology as a potential tool for early diagnosis. METHODS: Finite element method (FEM) simulations and experimental left-right hemispheric scalp potential differences of healthy and damaged brains were compared with respect to the asymmetry caused by ICH lesions on quasisymmetric scalp potential distributions. In numerical simulations, this asymmetry was measured at 25 kHz and visualized on the scalp as the normalized potential difference between the healthy and ICH damaged models. Proof-of-concept simulations were extended in a pilot study of experimental scalp potential measurements recorded between 0 and 50 kHz with the authors' custom-made bioimpedance spectrometer. Mean left-right scalp potential differences recorded from the frontal, central, and parietal brain regions of ten healthy control and six patients suffering from acute/subacute ICH were compared. The observed differences were measured at the 5% level of significance using the two-sample Welch t-test. RESULTS: The 3D-anatomically accurate FEM simulations showed that the normalized scalp potential difference between the damaged and healthy brain models is zero everywhere on the head surface, except in the vicinity of the lesion, where it can vary up to 5%. The authors' preliminary experimental results also confirmed that the left-right scalp potential difference in patients with ICH (e.g., 64 mV) is significantly larger than in healthy subjects (e.g., 20.8 mV; P < 0.05). CONCLUSIONS: Realistic, proof-of-concept simulations confirmed that ICH affects quasisymmetric scalp potential distributions. Pilot clinical observations with the authors' custom-made bioimpedance spectrometer also showed higher left-right potential differences in the presence of ICH, similar to those of their simulations, that may help to distinguish healthy subjects from ICH patients. Although these pilot clinical observations are in agreement with the computer simulations, the small sample size of this study lacks statistical power to exclude the influence of other possible confounders such as age, sex, and electrode positioning. The agreement with previously published simulation-based and clinical results, however, suggests that EBI technology may be potentially useful for ICH detection.

Stroke damage detection using classification trees on electrical bioimpedance cerebral spectroscopy measurements.

After cancer and cardio-vascular disease, stroke is the third greatest cause of death worldwide. Given the limitations of the current imaging technologies used for stroke diagnosis, the need for portable non-invasive and less expensive diagnostic tools is crucial. Previous studies have suggested that electrical bioimpedance (EBI) measurements from the head might contain useful clinical information related to changes produced in the cerebral tissue after the onset of stroke. In this study, we recorded 720 EBI Spectroscopy (EBIS) measurements from two different head regions of 18 hemispheres of nine subjects. Three of these subjects had suffered a unilateral haemorrhagic stroke. A number of features based on structural and intrinsic frequency-dependent properties of the cerebral tissue were extracted. These features were then fed into a classification tree. The results show that a full classification of damaged and undamaged cerebral tissue was achieved after three hierarchical classification steps. Lastly, the performance of the classification tree was assessed using Leave-One-Out Cross Validation (LOO-CV). Despite the fact that the results of this study are limited to a small database, and the observations obtained must be verified further with a larger cohort of patients, these findings confirm that EBI measurements contain useful information for   assessing on the health of brain tissue after stroke and supports the hypothesis that classification features based on Cole parameters, spectral information and the geometry of EBIS measurements are useful to differentiate between healthy and stroke damaged brain tissue.