Head Model Research Articles

Relationship between Bilateral Landmarks of Facial Asymmetry in Skeletal Class II and Class III in Vertical Dimension: 3D Facial Scan and Cone-Beam Computed Tomography.

Significant facial asymmetry can lead to both functional and aesthetic issues. Often, such asymmetry originates from irregularities in the jaw structure. It is critical to recognize that asymmetries can be concealed by soft tissue, which may be overlooked. The aim of this study is to investigate the relationships between bilateral landmarks in the vertical dimension of facial asymmetry among individuals with skeletal Class II and Class III malocclusions. Fifty-two adult patients with a mean age of 24.4 ± 3.79 years with facial asymmetry who underwent orthodontic and orthognathic surgery were studied. Cone-beam computed tomography and facial scans were used to create 3D virtual head models which enhanced the accuracy in addressing facial asymmetry to ensure effective treatment planning. Each landmark was measured and digitized using the Dolphin Imaging program. The findings showed a correlation between gender and type of skeletal discrepancies with the menton deviation direction. In conclusion, the vertical discrepancy between bilateral landmarks was observable in both hard and soft tissues with more pronounced expression in soft tissues. This discrepancy was characterized by an elevation on the same side as the menton deviation, which was a feature observed in both skeletal Class II and Class III individuals.

Head3D: Complete 3D Head Generation via Tri-plane Feature Distillation

Head generation with diverse identities is an important task in computer vision and computer graphics, widely used in multimedia applications. However, current full-head generation methods require a large number of three-dimensional (3D) scans or multi-view images to train the model, resulting in expensive data acquisition costs. To address this issue, we propose Head3D, a method to generate full 3D heads with limited multi-view images. Specifically, our approach first extracts facial priors represented by tri-planes learned in EG3D, a 3D-aware generative model, and then proposes feature distillation to deliver the 3D frontal faces within complete heads without compromising head integrity. To mitigate the domain gap between the face and head models, we present a dual-discriminator to guide the frontal and back head generation. Our model achieves cost-efficient and diverse complete head generation with photo-realistic renderings and high-quality geometry representations. Extensive experiments demonstrate the effectiveness of our proposed Head3D, both qualitatively and quantitatively.

MR-based electrical property tomography using a physics-informed network at 3 and 7 T.

Magnetic resonance electrical propert tomography promises to retrieve electrical properties (EPs) quantitatively and non-invasively in vivo, providing valuable information for tissue characterization and pathology diagnosis. However, its clinical implementation has been hindered by, for example, B1 measurement accuracy, reconstruction artifacts resulting from inaccuracies in underlying models, and stringent hardware/software requirements. To address these challenges, we present a novel approach aimed at accurate and high-resolution EPs reconstruction based on water content maps by using a physics-informed network (PIN-wEPT). The proposed method utilizes standard clinical protocols and conventional multi-channel receive arrays that have been routinely equipped in clinical settings, thus eliminating the need for specialized RF sequence/coil configurations. Compared with the original wEPT method, the network generates accurate water content maps that effectively eliminate the influence of and by incorporating data mismatch with electrodynamic constraints derived from the Helmholtz equation. Subsequent regression analysis develops a broad relationship between water content and EPs across various types of brain tissue. A series of numerical simulations was conducted at 7 T to assess the feasibility and performance of the method, which encompassed four normal head models and models with tumorous tissues incorporated, and the results showed normalized mean square error below 1.0% in water content, below 11.7% in conductivity, and below 1.1% in permittivity reconstructions for normal brain tissues. Moreover, in vivo validations conducted over five healthy subjects at both 3 and 7 T showed reasonably good consistency with empirical EPs values across the white matter, gray matter, and cerebrospinal fluid. The PIN-wEPT method, with its demonstrated efficacy, flexibility, and compatibility with current MRI scanners, holds promising potential for future clinical application.

78 Targeting tumor-promoting myeloid cell functions in head & neck cancer

Myeloid cells are a major component of immune cells infiltrating tumors, interacting with each other, with tumor cells and other stromal cells, and demonstrating a prominent plasticity. Tumor-associated myeloid cells, such as neutrophils (or so-called MDSCs: suppressory neutrophils) contribute to cancer progression via multiple mechanisms. Neutrophils are the most abundant myeloid blood cells, which have been shown to exert heterogeneous phenotypic and functional states: reaching from anti- to protumoral. On the one hand these cells can suppress anti-tumor immune responses, support tumor angiogenesis or metastatic dissemination. On the other hand, neutrophils actively kill tumor cells, or activate T-cell dependent immune responses.There is still little information on tumor-infiltrating neutrophils and their clinical relevance. Nevertheless, neutrophils were demonstrated to be associated with poor clinical outcome in patients with different kinds of cancer, including renal cancer, non-small-cell lung carcinoma, melanoma and head and neck cancer. Enrichment of neutrophils was also associated with metastases in various tumor entities. The recruitment of neutrophils within the tumor microenvironment is strictly regulated by chemokines, such as CXCL2 or CXCL8/IL-8.Neutrophil activity strongly depends on the stimulus present in the microenvironment. Since these cells are highly plastic and can easily alter their functionality, they are an ideal target for designing anti-tumor therapies. However, most of therapeutic strategies to target neutrophils in tumor-bearing hosts are dealing with the blocking of their migration into the tumor site. The efficiency of such approaches is, however, limited, as it does not address heterogeneity of these cells, and thus depletes as well antitumoral cells, leading to cancer progression and infectious complications.Given a critical role of neutrophils in tumor progression, we tested several alternative strategies to neutralize pro-tumoral activity of these cells, including reprogramming and inhibition of their immunosuppressive functions. Our recent data using own designed CCL2 peptide MyeloMIB show promising therapeutic results in head and neck cancer preclinical models. Application of this peptide leads not only to the specific inhibition of neutrophil migration into tumor tissue, but also reprograms these cells into anti-tumoral state. These results suggest a dual-edged role of neutrophils as essential regulators of anti-cancer immune responses and argue for approaches fostering anti-cancer activity of these cells, not only their migration, during cancer immunotherapy.

Clinical relevance and therapeutic predictive ability of hypoxia biomarkers in head and neck cancer tumour models.

Tumour hypoxia promotes poor patient outcomes, with particularly strong evidence for head and neck squamous cell carcinoma (HNSCC). To effectively target hypoxia, therapies require selection biomarkers and preclinical models that can accurately model tumour hypoxia. We established 20 patient-derived xenograft (PDX) and cell line-derived xenograft (CDX) models of HNSCC that we characterised for their fidelity to represent clinical HNSCC in gene expression, hypoxia status and proliferation and that were evaluated for their sensitivity to hypoxia-activated prodrugs (HAPs). PDX models showed greater fidelity in gene expression to clinical HNSCC than cell lines, as did CDX models relative to their paired cell lines. PDX models were significantly more hypoxic than CDX models, as assessed by hypoxia gene signatures and pimonidazole immunohistochemistry, and showed similar hypoxia gene expression to clinical HNSCC tumours. Hypoxia or proliferation status alone could not determine HAP sensitivity across our 20 HNSCC and two non-HNSCC tumour models by either tumour growth inhibition or killing of hypoxia cells in an ex vivo clonogenic assay. In summary, our tumour models provide clinically relevant HNSCC models that are suitable for evaluating hypoxia-targeting therapies; however, additional biomarkers to hypoxia are required to accurately predict drug sensitivity.

Engineering models of head and neck and oral cancers on-a-chip.

Head and neck cancers (HNCs) rank as the sixth most common cancer globally and result in over 450 000 deaths annually. Despite considerable advancements in diagnostics and treatment, the 5-year survival rate for most types of HNCs remains below 50%. Poor prognoses are often attributed to tumor heterogeneity, drug resistance, and immunosuppression. These characteristics are difficult to replicate using in vitro or in vivo models, culminating in few effective approaches for early detection and therapeutic drug development. Organs-on-a-chip offer a promising avenue for studying HNCs, serving as microphysiological models that closely recapitulate the complexities of biological tissues within highly controllable microfluidic platforms. Such systems have gained interest as advanced experimental tools to investigate human pathophysiology and assess therapeutic efficacy, providing a deeper understanding of cancer pathophysiology. This review outlines current challenges and opportunities in replicating HNCs within microphysiological systems, focusing on mimicking the soft, glandular, and hard tissues of the head and neck. We further delve into the major applications of organ-on-a-chip models for HNCs, including fundamental research, drug discovery, translational approaches, and personalized medicine. This review emphasizes the integration of organs-on-a-chip into the repertoire of biological model systems available to researchers. This integration enables the exploration of unique aspects of HNCs, thereby accelerating discoveries with the potential to improve outcomes for HNC patients.

A parametric design framework for the mass customization of bicycle helmet

Cluster analysis of 3D head shapes plays a crucial role in the mass customization design of products related to the head. Head shapes exhibit variations across different races, and designing helmets exclusively for Chinese individuals cannot solely rely on or reference foreign head models. Currently, research on cluster analysis of Chinese head shapes is limited, especially concerning shape variances. To address this, we developed an improved k-medoids algorithm and integrated Cluster Validity Index as an assessment metric. This enabled us to cluster 339 Chinese young males aged 18 to 30 into 7 groups based on their head shapes. By comparing our improved algorithm to the traditional k-medoids method, we affirmed its superiority in achieving higher sample participation rates and reducing inter-cluster sample disparities. To simplify the helmet design and editing process, and to improve the efficiency of mass customization, we have developed a parametric modeling program for bicycle helmets based on the head shape clustering results. Results from the Helmet Fit Index and stress simulation analysis demonstrate that our approach significantly enhances helmet fit and wearer comfort.

Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks

Adaptive Machine Learning Head Model Across Different Head Impact Types Using Unsupervised Domain Adaptation and Generative Adversarial Networks

Development of personalized non-invasive ventilation masks for critically ill children: a bench study

BackgroundObtaining a properly fitting non-invasive ventilation (NIV) mask to treat acute respiratory failure is a major challenge, especially in young children and patients with craniofacial abnormalities. Personalization of NIV masks holds promise to improve pediatric NIV efficiency. As current customization methods are relatively time consuming, this study aimed to test the air leak and surface pressure performance of personalized oronasal face masks using 3D printed soft materials. Personalized masks of three different biocompatible materials (silicone and photopolymer resin) were developed and tested on three head models of young children with abnormal facial features during preclinical bench simulation of pediatric NIV. Air leak percentages and facial surface pressures were measured and compared for each mask.ResultsPersonalized NIV masks could be successfully produced in under 12 h in a semi-automated 3D production process. During NIV simulation, overall air leak performance and applied surface pressures were acceptable, with leak percentages under 30% and average surface pressure values mostly remaining under normal capillary pressure. There was a small advantage of the masks produced with soft photopolymer resin material.ConclusionThis first, proof-of-concept bench study simulating NIV in children with abnormal facial features, showed that it is possible to obtain biocompatible, personalized oronasal masks with acceptable air leak and facial surface pressure performance using a relatively short, and semi-automated production process. Further research into the clinical value and possibilities for application of personalized NIV masks in critically ill children is needed.

Conductivity Profile Reconstruction of Stroke Head Models Based on Divide-and-Conquer Method and the Genetic Algorithm

Conductivity Profile Reconstruction of Stroke Head Models Based on Divide-and-Conquer Method and the Genetic Algorithm

A low‐cost and multifunctional long‐life anthropomorphic head phantom for microwave brain imaging systems

AbstractTo assess the performance of microwave brain imaging (MWBI) systems, an anthropomorphic multifunctional head phantom with long life, low cost, and reliable dielectric properties over the intended frequency range is a principal requisite. A solid head model has been developed using a mold casting method, incorporating graphite, aluminum oxide, carbon black powder, and brass powder with epoxy/hardener. Four healthy solid brain tissues consisting of skin, skull, gray matter, and white matter are fabricated to mimic realistic head tissue properties along with cerebrospinal fluid. The constructed head model incorporates four cavities of varying sizes and positions, designed to accommodate healthy or unhealthy cores, thereby simulating diverse stroke scenarios without necessitating separate healthy and unhealthy models. Utilizing a single‐head model preserves dielectric properties during comparative analysis (healthy/unhealthy), except for core components. Tissue properties were measured twice, three months apart, showing good agreement between theoretical and measured results.

Influence of centroid acceleration acquisition and filtering class on head injury criterion evaluation

BackgroundAlthough the Head Injury Criteria (HIC) has been widely applied to assess head impact injuries, it faces two outstanding problems: 1) HIC is affected strongly by the cut-off frequency when processing acceleration signals. And these cut-off frequencies are experiential and lack unified guidelines; 2) If the head was impacted on a different part, should the corresponding HIC threshold be the same? If these problems are not resolved, it could potentially lead to a critical misinterpretation of the safety assessment. MethodsFinite element method was used to reconstruct head impacts. The head model includes tissues like skull, brainstem, cerebrospinal fluid, etc. The head model was impacted in the frontal, occipital, parietal or lateral direction with different impact velocities. Acceleration signals of the head model were extracted directly from the skull and the head centroid node. To obtain a robust HIC, the filtering class of acceleration signals were analyzed carefully. Then, the relation between rigid body HIC and the centroid node HIC were studied systematically. ResultsWhen the filtering class of rigid body acceleration and centroid node acceleration reached the cut-off frequency, the corresponding derivative of HIC tended to change smoothly. Using these cut-off frequencies, robust HICs were obtained. The rigid body HIC far exceeded that of centroid node HIC, such as 8, 9, 14 and 31 times exceeded in the frontal, occipital, parietal and lateral impact conditions, respectively. Moreover, approximate linear relations were found between the rigid body HIC and the centroid node HIC in different impact directions, respectively. From these relations, the injury thresholds of rigid body HIC of various directions were given quantitatively. ConclusionsThe rational filtering class like CFC 800 and CFC 700 were given for rigid body HIC and centroid node HIC, respectively. The rigid body HIC had a significant discrepancy from the centroid node HIC. Linear relations between the rigid body HIC and centroid node HIC were found, and their slopes changed with impact directions. From these relations, we can adjust the injury thresholds reasonably if the head receives different impacts. These findings can effectively enhance the applicability of HIC.

An adaptive h-refinement method for the boundary element fast multipole method for quasi-static electromagnetic modeling

Objective. In our recent work pertinent to modeling of brain stimulation and neurophysiological recordings, substantial modeling errors in the computed electric field and potential have sometimes been observed for standard multi-compartment head models. The goal of this study is to quantify those errors and, further, eliminate them through an adaptive mesh refinement (AMR) algorithm. The study concentrates on transcranial magnetic stimulation (TMS), transcranial electrical stimulation (TES), and electroencephalography (EEG) forward problems. Approach. We propose, describe, and systematically investigate an AMR method using the boundary element method with fast multipole acceleration (BEM-FMM) as the base numerical solver. The goal is to efficiently allocate additional unknowns to critical areas of the model, where they will best improve solution accuracy. The implemented AMR method’s accuracy improvement is measured on head models constructed from 16 Human Connectome Project subjects under problem classes of TES, TMS, and EEG. Errors are computed between three solutions: an initial non-adaptive solution, a solution found after applying AMR with a conservative refinement rate, and a ‘silver-standard’ solution found by subsequent 4:1 global refinement of the adaptively-refined model. Main results. Excellent agreement is shown between the adaptively-refined and silver-standard solutions for standard head models. AMR is found to be vital for accurate modeling of TES and EEG forward problems for standard models: an increase of less than 25% (on average) in number of mesh elements for these problems, efficiently allocated by AMR, exposes electric field/potential errors exceeding 60% (on average) in the solution for the unrefined models. Significance. This error has especially important implications for TES dosing prediction—where the stimulation strength plays a central role—and for EEG lead fields. Though the specific form of the AMR method described here is implemented for the BEM-FMM, we expect that AMR is applicable and even required for accurate electromagnetic simulations by other numerical modeling packages as well.

Retrieval of chromophore concentration changes in a digital human head model using analytical mean partial pathlengths of photons.

Continuous-wave functional near-infrared spectroscopy has proved to be a valuable tool for assessing hemodynamic activity in the human brain in a non-invasively and inexpensive way. However, most of the current processing/analysis methods assume the head is a homogeneous medium, and hence do not appropriately correct for the signal coming from the scalp. This effect can be reduced by considering light propagation in a layered model of the human head, being the Monte Carlo (MC) simulations the gold standard to this end. However, this implies large computation times and demanding hardware capabilities. In this work, we study the feasibility of replacing the homogeneous model and the MC simulations by means of analytical multilayered models, combining in this way, the speed and simplicity of implementation of the former with the robustness and accuracy of the latter. Oxy- and deoxyhemoglobin (HbO and HbR, respectively) concentration changes were proposed in two different layers of a magnetic resonance imaging (MRI)-based meshed model of the human head, and then these changes were retrieved by means of (i)a typical homogeneous reconstruction and (ii)a theoretical layered reconstruction. Results suggest that the use of analytical models of light propagation in layered models outperforms the results obtained using traditional homogeneous reconstruction algorithms, providing much more accurate results for both, the extra- and the cerebral tissues. We also compare the analytical layered reconstruction with MC-based reconstructions, achieving similar degrees of accuracy, especially in the gray matter layer, but much faster (between 4 and 5 orders of magnitude). We have successfully developed, implemented, and validated a method for retrieving chromophore concentration changes in the human brain, combining the simplicity and speed of the traditional homogeneous reconstruction algorithms with robustness and accuracy much more similar to those provided by MC simulations.

MODERN GLOBAL SCIENTIFIC TRENDS OF FORENSIC RESEARCH METHODS IN CASES OF FIREARM TRAUMA

This literature review aimed to investigate the new scientific trends in the methods concerning criminalistics in cases of firearm trauma. Articles on this topic published in the high-scoreforensic scientifi c journals between 2019-2023 years were analyzed. A literature search revealed the following major scientifi c areas of forensic science which are commonly developed today in cases of fi rearm trauma: fi rearm examination itself, shooting incident reconstruction and wound ballistics. A great importance of wound ballistics has underlined in the review. Basic studies on soft tissue simulants, main ways and methods in which radiology techniques can be applied during forensic medical and ballistic expertize were lightened too. Special attention in the article payed to molecular ballistics and ballistic head model.

Infant head and brain segmentation from magnetic resonance images using fusion-based deep learning strategies

Magnetic resonance (MR) imaging is widely used for assessing infant head and brain development and for diagnosing pathologies. The main goal of this work is the development of a segmentation framework to create patient-specific head and brain anatomical models from MR images for clinical evaluation. The proposed strategy consists of a fusion-based Deep Learning (DL) approach that combines the information of different image sequences within the MR acquisition protocol, including the axial T1w, sagittal T1w, and coronal T1w after contrast. These image sequences are used as input for different fusion encoder–decoder network architectures based on the well-established U-Net framework. Specifically, three different fusion strategies are proposed and evaluated, namely early, intermediate, and late fusion. In the early fusion approach, the images are integrated at the beginning of the encoder–decoder architecture. In the intermediate fusion strategy, each image sequence is processed by an independent encoder, and the resulting feature maps are then jointly processed by a single decoder. In the late fusion method, each image is individually processed by an encoder–decoder, and the resulting feature maps are then combined to generate the final segmentations. A clinical in-house dataset consisting of 19 MR scans was used and divided into training, validation, and testing sets, with 3 MR scans defined as a fixed validation set. For the remaining 16 MR scans, a cross-validation approach was adopted to assess the performance of the methods. The training and testing processes were carried out with a split ratio of 75% for the training set and 25% for the testing set. The results show that the early and intermediate fusion methodologies presented the better performance (Dice coefficient of 97.6 ± 1.5% and 97.3 ± 1.8% for the head and Dice of 94.5 ± 1.7% and 94.8 ± 1.8% for the brain, respectively), whereas the late fusion method generated slightly worst results (Dice of 95.5 ± 4.4% and 93.8 ± 3.1% for the head and brain, respectively). Nevertheless, the volumetric analysis showed that no statistically significant differences were found between the volumes of the models generated by all the segmentation strategies and the ground truths. Overall, the proposed frameworks demonstrate accurate segmentation results and prove to be feasible for anatomical model analysis in clinical practice.

Microfluidics produced ATRA-loaded PLGA NPs reduced tuberculosis burden in alveolar epithelial cells and enabled high delivered dose under simulated human breathing pattern in 3D printed head models

Tuberculosis, caused by Mycobacterium tuberculosis (Mtb), is second only to COVID-19 as the top infectious disease killer worldwide. Multi-drug resistant TB (MDR-TB) may arise because of poor patient adherence to medications due to lengthy treatment duration and side effects. Delivering novel host directed therapies (HDT), like all trans retinoic acid (ATRA) may help to improve drug regimens and reduce the incidence of MDR-TB. Local delivery of ATRA to the site of infection leads to higher bioavailability and reduced systemic side effects. ATRA is poorly soluble in water and has a short half-life in plasma. Therefore, it requires a formulation step before it can be administered in vivo. ATRA loaded PLGA nanoparticles suitable for nebulization were manufactured and optimized using a scalable nanomanufacturing microfluidics (MF) mixing approach (MF-ATRA-PLGA NPs). MF-ATRA-PLGA NPs demonstrated a dose dependent inhibition of Mtb growth in TB-infected A549 alveolar epithelial cell model while preserving cell viability. The MF-ATRA-PLGA NPs were nebulized with the Aerogen Solo vibrating mesh nebulizer, with aerosol droplet size characterized using laser diffraction and the estimated delivered dose was determined. The volume median diameter (VMD) of the MF-ATRA-PLGA NPs was 3.00 ± 0.18 μm. The inhaled dose delivered in adult and paediatric 3D printed head models under a simulated normal adult and paediatric breathing pattern was found to be 47.05 ± 3 % and 20.15 ± 3.46 % respectively. These aerosol characteristics of MF-ATRA-PLGA NPs supports its suitability for delivery to the lungs via inhalation. The data generated on the efficacy of an inhalable, scalable and regulatory friendly ATRA-PLGA NPs formulation provides a foundation on which further pre-clinical testing can be built. Overall, the results of this project are promising for future research into ATRA loaded NPs formulations as inhaled host directed therapies for TB.

Immunotherapy targeting tumor-associated antigen in a mouse model of head and neck cancer.

The identification of epitope peptides from tumor-associated antigens (TAAs) is informative for developing tumor-specific immunotherapy. However, only a few epitopes have been detected in mouse TAAs of head and neck cancer (HNSCC). Novel mouse c-Met-derived T-cell epitopes were predicted by computer-based algorithms. Mouse HNSCC cell line-bearing mice were treated with a c-Met peptide vaccine. The effects of CD8 and/or CD4 T-cell depletion, and vaccine combination with immune checkpoint inhibitors (ICIs) were evaluated. Tumor re-inoculation was performed to assess T-cell memory. We identified c-Met-derived short and long epitopes that elicited c-Met-reactive antitumor CD8 and/or CD4 T-cell responses. Vaccination using these peptides showed remarkable antitumor responses via T cells in which ICIs were not required. The c-Met peptide-vaccinated mice rejected the re-inoculated tumors. We demonstrated that novel c-Met peptide vaccines can induce antitumor T-cell response, and could be a potent immunotherapy in a syngeneic mouse HNSCC model.

Subject-specific finite element head models for skull fracture evaluation-a new tool in forensic pathology.

Post-mortem computed tomography (PMCT) enables the creation of subject-specific 3D head models suitable for quantitative analysis such as finite element analysis (FEA). FEA of proposed traumatic events is an objective and repeatable numerical method for assessing whether an event could cause a skull fracture such as seen at autopsy. FEA of blunt force skull fracture in adults with subject-specific 3D models in forensic pathology remains uninvestigated. This study aimed to assess the feasibility of FEA for skull fracture analysis in routine forensic pathology. Five cases with blunt force skull fracture and sufficient information on the kinematics of the traumatic event to enable numerical reconstruction were chosen. Subject-specific finite element (FE) head models were constructed by mesh morphing based on PMCT 3D models and A Detailed and Personalizable Head Model with Axons for Injury Prediction (ADAPT) FE model. Morphing was successful in maintaining subject-specific 3D geometry and quality of the FE mesh in all cases. In three cases, the simulated fracture patterns were comparable in location and pattern to the fractures seen at autopsy/PMCT. In one case, the simulated fracture was in the parietal bone whereas the fracture seen at autopsy/PMCT was in the occipital bone. In another case, the simulated fracture was a spider-web fracture in the frontal bone, whereas a much smaller fracture was seen at autopsy/PMCT; however, the fracture in the early time steps of the simulation was comparable to autopsy/PMCT. FEA might be feasible in forensic pathology in cases with a single blunt force impact and well-described event circumstances.

High-resolution EEG source localization in personalized segmentation-free head model with multi-dipole fitting

Objective. Electroencephalograms (EEGs) are often used to monitor brain activity. Several source localization methods have been proposed to estimate the location of brain activity corresponding to EEG readings. However, only a few studies evaluated source localization accuracy from measured EEG using personalized head models in a millimeter resolution. In this study, based on a volume conductor analysis of a high-resolution personalized human head model constructed from magnetic resonance images, a finite difference method was used to solve the forward problem and to reconstruct the field distribution. Approach. We used a personalized segmentation-free head model developed using machine learning techniques, in which the abrupt change of electrical conductivity occurred at the tissue interface is suppressed. Using this model, a smooth field distribution was obtained to address the forward problem. Next, multi-dipole fitting was conducted using EEG measurements for each subject (N = 10 male subjects, age: 22.5 ± 0.5), and the source location and electric field distribution were estimated. Main results. For measured somatosensory evoked potential for electrostimulation to the wrist, a multi-dipole model with lead field matrix computed with the volume conductor model was found to be superior than a single dipole model when using personalized segmentation-free models (6/10). The correlation coefficient between measured and estimated scalp potentials was 0.89 for segmentation-free head models and 0.71 for conventional segmented models. The proposed method is straightforward model development and comparable localization difference of the maximum electric field from the target wrist reported using fMR (i.e. 16.4 ± 5.2 mm) in previous study. For comparison, DUNEuro based on sLORETA was (EEG: 17.0 ± 4.0 mm). In addition, somatosensory evoked magnetic fields obtained by Magnetoencephalography was 25.3 ± 8.5 mm using three-layer sphere and sLORETA. Significance. For measured EEG signals, our procedures using personalized head models demonstrated that effective localization of the somatosensory cortex, which is located in a non-shallower cortex region. This method may be potentially applied for imaging brain activity located in other non-shallow regions.