Computational Modeling of Blunt Impact to Head and Correlation of Biomechanical Measures With Medical Images

2019 ◽  
Vol 3 (1) ◽  
Author(s):  
X. Gary Tan ◽  
Maria M. D'Souza ◽  
Subhash Khushu ◽  
Raj K. Gupta ◽  
Virginia G. DeGiorgi ◽  
...  
Keyword(s):  
Clinical Data ◽  
Bone Structure ◽  
Road Traffic ◽  
Medical Images ◽  
Imaging Techniques ◽  
Computational Simulation ◽  
Viscoelastic Model ◽  
Human Head ◽  
Brain Morphology ◽  
Head Model

Abstract Mild traumatic brain injury (TBI) is a common injury to service members in recent conflicts. We attempt to correlate simulation results with clinical data from advanced imaging techniques to identify TBI-related subtle alterations in brain morphology, function, and metabolism. Magnetic resonance image (MRI) data were obtained for a young adult male, after a concussive head injury caused by a road traffic accident. A similar fall of a pedestrian using an articulated human body biodynamics model was integrated with the finite element (FE) analysis using a high-resolution human head model to investigate TBI from an accident. The hyper-viscoelastic model was used to represent the strain rate dependence in brain tissues. The bone structure was simulated using an elastoplastic model to capture the focal permanent deformation. Enhanced tetrahedral elements were used in modeling nearly incompressible tissues. The localized large deformation in the head was simulated and compared with those from the MRI images. Biomechanical measures, such as stresses and strains, were correlated with postaccident medical images with respect to injury location and severity in the brain. The correspondence between model results and MRI findings shows a new way to relate computational simulation response of human head to blunt impacts with clinical data from such incidents and thus enhances our understanding of the mechanism, extent, and effects of TBI.

Computational Modeling of Blunt Impact to Head and Correlation of Biomechanical Measures With Medical Images

2018 ◽  
Author(s):  
X. Gary Tan ◽  
Maria M. D’Souza ◽  
Subhash Khushu ◽  
Raj K. Gupta ◽  
Virginia G. DeGiorgi ◽  
...  
Keyword(s):  
Brain Injury ◽  
High Resolution ◽  
Computational Modeling ◽  
Diffusion Tensor ◽  
Medical Images ◽  
Human Head ◽  
Biological Tissues ◽  
Brain Morphology ◽  
Head Model ◽  
Human Head Model

Mild traumatic brain injury (TBI) is a very common injury to service members in recent conflicts. Computational models can offer insights in understanding the underlying mechanism of brain injury, which can aid in the development of effective personal protective equipment. This paper attempts to correlate simulation results with clinical data from advanced techniques such as magnetic resonance imaging (MRI), diffusion tensor imaging (DTI), functional MRI (fMRI), MR spectroscopy and susceptibility weighted imaging (SWI), to identify TBI related subtle alterations in brain morphology, function and metabolism. High-resolution image data were obtained from the MRI scan of a young adult male, from a concussive head injury caused by a road traffic accident. The falling accident of human was modeled by combing high-resolution human head model with an articulated human body model. This mixed, multi-fidelity computational modeling approach can efficiently investigate such accident-related TBI. A high-fidelity computational head model was used to accurately reproduce the complex structures of the head. For most soft materials, the hyper-viscoelastic model was used to captures the strain rate dependence and finite strain nonlinearity. Stiffer materials, such as bony structure were simulated using an elasto-plastic material model to capture the permanent deformation. We used the enhanced linear tetrahedral elements to remove the parasitic locking problem in modeling such incompressible biological tissues. The bio-fidelity of human head model was validated from human cadaver tests. The accidental fall was reconstructed using such multi-fidelity models. The localized large deformation in the head was simulated and compared with the MRI images. The shear stress and shear strain were used to correlate with the post-accident medical images with respect to the injury location and severity in the brain. The correspondence between model results and MRI findings further validates the human head models and enhances our understanding of the mechanism, extent and impact of TBI.

A Novel Secure and Robust encryption scheme for medical images

2020 ◽  
Vol 16 ◽  
Author(s):  
Siyamol Chirakkarottu ◽  
Sheena Mathew
Keyword(s):  
Medical Images ◽  
Technological Development ◽  
Imaging Techniques ◽  
Chaotic Map ◽  
Two Phase ◽  
Positron Emission ◽  
Medical Diagnostic ◽  
Wireless Medium ◽  
Health Care Applications ◽  
Magnetic Resonance Imaging Mri

Background: Medical imaging encloses different imaging techniques and processes to image the human body for medical diagnostic and treatment purposes. Hence it plays an important role to improve public health. The technological development in biomedical imaging specifically in X-ray, Computed Tomography (CT), nuclear ultrasound including Positron Emission Tomography (PET), optical and Magnetic Resonance Imaging (MRI) can provide valuable information unique to a person. Objective: In health care applications, the images are needed to be exchanged mostly over wireless medium. The diagnostic images with confidential information of a patient need to be protected from unauthorized access during transmission. In this paper, a novel encryption method is proposed to improve the security and integrity of medical images. Methods: Chaotic map along with DNA cryptography is used for encryption. The proposed method describes a two phase encryption of medical images. Results: Performance of the proposed method is also tested by various analysis metrics. Robustness of the method against different noises and attacks is analyzed. Conclusion: The results show that the method is efficient and well suitable to medical images.

scholarly journals Neural silences can be localized rapidly using noninvasive scalp EEG

2021 ◽  
Vol 4 (1) ◽  
Author(s):  
Alireza Chamanzar ◽  
Marlene Behrmann ◽  
Pulkit Grover
Keyword(s):  
Center Of Mass ◽  
Cost Effective ◽  
Human Head ◽  
Head Model ◽  
Cortical Function ◽  
Scalp Eeg ◽  
Localization Algorithms ◽  
Important Benefit ◽  
Eeg Recordings ◽  
Detection And Localization

AbstractA rapid and cost-effective noninvasive tool to detect and characterize neural silences can be of important benefit in diagnosing and treating many disorders. We propose an algorithm, SilenceMap, for uncovering the absence of electrophysiological signals, or neural silences, using noninvasive scalp electroencephalography (EEG) signals. By accounting for the contributions of different sources to the power of the recorded signals, and using a hemispheric baseline approach and a convex spectral clustering framework, SilenceMap permits rapid detection and localization of regions of silence in the brain using a relatively small amount of EEG data. SilenceMap substantially outperformed existing source localization algorithms in estimating the center-of-mass of the silence for three pediatric cortical resection patients, using fewer than 3 minutes of EEG recordings (13, 2, and 11mm vs. 25, 62, and 53 mm), as well for 100 different simulated regions of silence based on a real human head model (12 ± 0.7 mm vs. 54 ± 2.2 mm). SilenceMap paves the way towards accessible early diagnosis and continuous monitoring of altered physiological properties of human cortical function.

scholarly journals Levels of detail analysis of microwave scattering from human head models for brain stroke detection

PeerJ ◽  
2017 ◽  
Vol 5 ◽  
pp. e4061 ◽  
Author(s):  
Awais Munawar Qureshi ◽  
Zartasha Mustansar
Keyword(s):  
Scattering Problem ◽  
Human Head ◽  
Computational Time ◽  
Head Model ◽  
Microwave Scattering ◽  
Levels Of Detail ◽  
Realistic Head Model ◽  
Different Types ◽  
Brain Stroke ◽  
Brain Tissues

In this paper, we have presented a microwave scattering analysis from multiple human head models. This study incorporates different levels of detail in the human head models and its effect on microwave scattering phenomenon. Two levels of detail are taken into account; (i) Simplified ellipse shaped head model (ii) Anatomically realistic head model, implemented using 2-D geometry. In addition, heterogenic and frequency-dispersive behavior of the brain tissues has also been incorporated in our head models. It is identified during this study that the microwave scattering phenomenon changes significantly once the complexity of head model is increased by incorporating more details using magnetic resonance imaging database. It is also found out that the microwave scattering results match in both types of head model (i.e., geometrically simple and anatomically realistic), once the measurements are made in the structurally simplified regions. However, the results diverge considerably in the complex areas of brain due to the arbitrary shape interface of tissue layers in the anatomically realistic head model.After incorporating various levels of detail, the solution of subject microwave scattering problem and the measurement of transmitted and backscattered signals were obtained using finite element method. Mesh convergence analysis was also performed to achieve error free results with a minimum number of mesh elements and a lesser degree of freedom in the fast computational time. The results were promising and the E-Field values converged for both simple and complex geometrical models. However, the E-Field difference between both types of head model at the same reference point differentiated a lot in terms of magnitude. At complex location, a high difference value of 0.04236 V/m was measured compared to the simple location, where it turned out to be 0.00197 V/m. This study also contributes to provide a comparison analysis between the direct and iterative solvers so as to find out the solution of subject microwave scattering problem in a minimum computational time along with memory resources requirement.It is seen from this study that the microwave imaging may effectively be utilized for the detection, localization and differentiation of different types of brain stroke. The simulation results verified that the microwave imaging can be efficiently exploited to study the significant contrast between electric field values of the normal and abnormal brain tissues for the investigation of brain anomalies. In the end, a specific absorption rate analysis was carried out to compare the ionizing effects of microwave signals to different types of head model using a factor of safety for brain tissues. It is also suggested after careful study of various inversion methods in practice for microwave head imaging, that the contrast source inversion method may be more suitable and computationally efficient for such problems.

Efficient Electromagnetic Analysis of a Dispersive Head Model Due to Smart Glasses Embedded Antennas at Wi-Fi and 5G Frequencies

2021 ◽  
Vol 36 (2) ◽  
pp. 159-167
Author(s):  
Fatih Kaburcuk ◽  
Atef Elsherbeni
Keyword(s):  
Numerical Study ◽  
Computation Time ◽  
Human Head ◽  
Head Model ◽  
Computer Memory ◽  
Large Computer ◽  
Smart Glasses ◽  
Embedded Antennas ◽  
Human Head Model ◽  
Difference Time

Numerical study of electromagnetic interaction between an adjacent antenna and a human head model requires long computation time and large computer memory. In this paper, two speeding up techniques for a dispersive algorithm based on finite-difference time-domain method are used to reduce the required computation time and computer memory. In order to evaluate the validity of these two speeding up techniques, specific absorption rate (SAR) and temperature rise distributions in a dispersive human head model due to radiation from an antenna integrated into a pair of smart glasses are investigated. The antenna integrated into the pair of smart glasses have wireless connectivity at 2.4 GHz and 5th generation (5G) cellular connectivity at 4.9 GHz. Two different positions for the antenna integrated into the frame are considered in this investigation. These techniques provide remarkable reduction in computation time and computer memory.

Computational Modeling of Combat Helmet Performance Analysis Integrating Blunt and Blast Loadings

2020 ◽  
Author(s):  
X. Gary Tan ◽  
Amit Bagchi
Keyword(s):  
System Analysis ◽  
Plastic Material ◽  
Human Head ◽  
Material Model ◽  
Head Model ◽  
Ballistic Impacts ◽  
Rate Dependent ◽  
Brain Responses ◽  
Suspension Geometry ◽  
System Configurations

Abstract Combat helmets have gone through many changes, from shells made of metal to advanced composites using Kevlar and Dyneema, along with introduction of pad suspensions to provide comfort and protection. Helmets have been designed to perform against ballistic and blunt impact threats. But, in today’s warfare, combat helmets are expected to protect against all three threats, blunt, ballistic impacts and blast effects to minimize traumatic brain injury (TBI) and provide a better thermal comfort. We are developing a helmet system analysis methodology integrating the effect of multiple threats, i.e., blast and blunt impacts, to achieve an optimal helmet system design, by utilizing multi-physics computational tools. We used a validated human head model to represent the warfighter’s head. The helmet composite shell was represented by an orthotropic elasto-plastic material model. A strain rate dependent model was employed for pad suspension material. Available dynamic loading data was used to calibrate the material parameters. Multiple helmet system configurations subjected to blast and blunt loadings were considered to quantify their influence on brain biomechanical response. Parametric studies were carried out to assess energy absorption for different suspension geometry and material morphology for different loadings. The resulting brain responses were used with published injury criteria to characterize the helmet system performance through a single metric for each threat type. Approaches to combine single-threat metrics to allow aggregating performance against multiple threats were discussed.

scholarly journals MRI-Based Multiscale Model for Electromagnetic Analysis in the Human Head with Implanted DBS

2013 ◽  
Vol 2013 ◽  
pp. 1-12 ◽  
Author(s):  
Maria Ida Iacono ◽  
Nikos Makris ◽  
Luca Mainardi ◽  
Leonardo M. Angelone ◽  
Giorgio Bonmassar
Keyword(s):  
Electric Field ◽  
Ex Vivo ◽  
Human Head ◽  
Multiscale Model ◽  
Head Model ◽  
Tissue Heating ◽  
Electromagnetic Simulations ◽  
Anatomical Detail ◽  
Magnetic Resonance Imaging Mri ◽  
Deep Brain

Deep brain stimulation (DBS) is an established procedure for the treatment of movement and affective disorders. Patients with DBS may benefit from magnetic resonance imaging (MRI) to evaluate injuries or comorbidities. However, the MRI radio-frequency (RF) energy may cause excessive tissue heating particularly near the electrode. This paper studies how the accuracy of numerical modeling of the RF field inside a DBS patient varies with spatial resolution and corresponding anatomical detail of the volume surrounding the electrodes. A multiscale model (MS) was created by an atlas-based segmentation using a 1 mm3head model (mRes) refined in the basal ganglia by a 200 μm2ex-vivo dataset. Four DBS electrodes targeting the left globus pallidus internus were modeled. Electromagnetic simulations at 128 MHz showed that the peak of the electric field of the MS doubled (18.7 kV/m versus 9.33 kV/m) and shifted 6.4 mm compared to the mRes model. Additionally, the MS had a sixfold increase over the mRes model in peak-specific absorption rate (SAR of 43.9 kW/kg versus 7 kW/kg). The results suggest that submillimetric resolution and improved anatomical detail in the model may increase the accuracy of computed electric field and local SAR around the tip of the implant.

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