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Book ; Online: Brain and Human Body Modeling 2020

Makarov, Sergey N. / Noetscher, Gregory M. / Nummenmaa, Aapo

Computational Human Models Presented at EMBC 2019 and the BRAIN Initiative® 2019 Meeting

2021

Author's details	edited by Sergey N. Makarov, Gregory M. Noetscher, Aapo Nummenmaa
Keywords	Biomedical engineering ; Electronic circuits
Subject code	610.28
Language	English
Size	1 Online-Ressource (XI, 407 p. 223 illus., 199 illus. in color)
Edition	1st ed. 2021
Publisher	Springer International Publishing ; Imprint: Springer
Publishing place	Cham
Document type	Book ; Online
HBZ-ID	HT020568157
ISBN	978-3-030-45623-8 ; 9783030456221 ; 9783030456245 ; 9783030456252 ; 3-030-45623-4 ; 3030456226 ; 3030456242 ; 3030456250
DOI	10.1007/978-3-030-45623-8
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Book ; Online: Brain and Human Body Modeling 2020

Makarov, Sergey N. / Noetscher, Gregory M. / Nummenmaa, Aapo

2021

Abstract: This open access book describes modern applications of computational human modeling in an effort to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any ... ...

Abstract	This open access book describes modern applications of computational human modeling in an effort to advance neurology, cancer treatment, and radio-frequency studies including regulatory, safety, and wireless communication fields. Readers working on any application that may expose human subjects to electromagnetic radiation will benefit from this book's coverage of the latest models and techniques available to assess a given technology's safety and efficacy in a timely and efficient manner. Describes computational human body phantom construction and application; Explains new practices in computational human body modeling for electromagnetic safety and exposure evaluations; Includes a survey of modern applications for which computational human phantoms are critical
Keywords	Medicine (General) ; Agriculture (General)
Size	1 electronic resource (407 pages)
Publisher	Springer Nature
Document type	Book ; Online
Note	English ; Open Access
HBZ-ID	HT020676428
ISBN	9783030456238 ; 3030456234
DOI	10.1007/978-3-030-45623-8
Database	ZB MED Catalogue: Medicine, Health, Nutrition, Environment, Agriculture

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Article ; Online: Electric Field Comparison for TMS Using Different Neuroimaging Segmentation Methods.

Zaidi, Tayeb A / Makarov, Sergey N / Fujimoto, Kyoko

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2021 Volume 2021, Page(s) 6565–6568

Abstract: Computational electromagnetic modeling is a powerful technique to evaluate the effects of electrical stimulation of the human brain. The results of these simulations can vary depending on the specific segmentation of the head and brain generated from the ...

Abstract	Computational electromagnetic modeling is a powerful technique to evaluate the effects of electrical stimulation of the human brain. The results of these simulations can vary depending on the specific segmentation of the head and brain generated from the patient images. Using an existing boundary element fast multipole method (BEM-FMM) electromagnetic solver, this work evaluates the electric field differences modeled using two neuroimaging segmentation methods. A transcranial magnetic stimulation (TMS) coil targeting both the primary motor cortex and the dorsolateral prefrontal cortex (DLPFC) was simulated. Average field differences along a 100 mm line from the coil were small (2% for motor cortex, 3% for DLPFC) and the average field differences in the regions directly surrounding the target stimulation point were 5% for the motor cortex and 2% for DLPFC. More studies evaluating different coils and other segmentation options may further improve the computational modeling for robust TMS treatment.Clinical relevance- Patient-specific computational modeling will provide more information to clinicians for improved localization and targeting of neuromodulation therapies.
MeSH term(s)	Brain/diagnostic imaging ; Dorsolateral Prefrontal Cortex ; Humans ; Motor Cortex/diagnostic imaging ; Neuroimaging ; Transcranial Magnetic Stimulation
Language	English
Publishing date	2021-12-10
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC46164.2021.9630719
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: The effect of meninges on the electric fields in TES and TMS. Numerical modeling with adaptive mesh refinement.

Weise, Konstantin / Wartman, William A / Knösche, Thomas R / Nummenmaa, Aapo R / Makarov, Sergey N

Brain stimulation

2022 Volume 15, Issue 3, Page(s) 654–663

Abstract: Background: When modeling transcranial electrical stimulation (TES) and transcranial magnetic stimulation (TMS) in the brain, the meninges - dura, arachnoid, and pia mater - are often neglected due to high computational costs.: Objective: We ... ...

Abstract	Background: When modeling transcranial electrical stimulation (TES) and transcranial magnetic stimulation (TMS) in the brain, the meninges - dura, arachnoid, and pia mater - are often neglected due to high computational costs. Objective: We investigate the impact of the meningeal layers on the cortical electric field in TES and TMS while considering the headreco segmentation as the base model. Method: We use T1/T2 MRI data from 16 subjects and apply the boundary element fast multipole method with adaptive mesh refinement, which enables us to accurately solve this problem and establish method convergence at reasonable computational cost. We compare electric fields in the presence and absence of various meninges for two brain areas (M1 Results: Maximum electric fields in the cortex for focal TES consistently increase by approximately 30% on average when the meninges are present in the CSF volume. Their effect on the maximum field can be emulated by reducing the CSF conductivity from 1.65 S/m to approximately 0.85 S/m. In stark contrast to that, the TMS electric fields in the cortex are only weakly affected by the meningeal layers and slightly (∼6%) decrease on average when the meninges are included. Conclusion: Our results quantify the influence of the meninges on the cortical TES and TMS electric fields. Both focal TES and TMS results are very consistent. The focal TES results are also in a good agreement with a prior relevant study. The solver and the mesh generator for the meningeal layers (compatible with SimNIBS) are available online.
MeSH term(s)	Brain/physiology ; Humans ; Meninges ; Surgical Mesh ; Transcranial Direct Current Stimulation/methods ; Transcranial Magnetic Stimulation/methods
Language	English
Publishing date	2022-04-18
Publishing country	United States
Document type	Journal Article ; Research Support, Non-U.S. Gov't ; Research Support, N.I.H., Extramural
ZDB-ID	2394410-9
ISSN	1876-4754 ; 1935-861X
ISSN (online)	1876-4754
ISSN	1935-861X
DOI	10.1016/j.brs.2022.04.009
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Degree of RF MRI coil detuning for an anatomically realistic respiratory cycle modeled with the finite element method.

Tran, Anh L / Makarov, Sergey N

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2017 Volume 2017, Page(s) 1405–1408

Abstract: Respiratory motion is an important problem in Magnetic Resonance Imaging (MRI), contributing to image blurring during data acquisition and coil detuning. Using the concept of an ideal (perfectly matched and tuned at all available ports) RF transmit coil ... ...

Abstract	Respiratory motion is an important problem in Magnetic Resonance Imaging (MRI), contributing to image blurring during data acquisition and coil detuning. Using the concept of an ideal (perfectly matched and tuned at all available ports) RF transmit coil and the VHP-Female v4.0 dynamic CAD model, we numerically model and estimate the detuning of a full-body RF coil during the respiratory cycle.
MeSH term(s)	Equipment Design ; Finite Element Analysis ; Magnetic Resonance Imaging ; Phantoms, Imaging ; Radio Waves
Language	English
Publishing date	2017-10-20
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC.2017.8037096
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Article ; Online: Accurate TMS Head Modeling: Interfacing SimNIBS and BEM-FMM in a MATLAB-Based Module.

Saturnino, Guilherme B / Wartman, William A / Makarov, Sergey N / Thielscher, Axel

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2020 Volume 2020, Page(s) 5326–5329

Abstract: We interface the head modelling, coil models, Graphical User Interface (GUI), and post-processing capabilities of the SimNIBS package with the boundary element fast multipole method (BEM-FMM), implemented in a MATLAB-based module. The resulting pipeline ... ...

Abstract	We interface the head modelling, coil models, Graphical User Interface (GUI), and post-processing capabilities of the SimNIBS package with the boundary element fast multipole method (BEM-FMM), implemented in a MATLAB-based module. The resulting pipeline combines the best of both worlds: the individualized head modelling and ease-of-use of SimNIBS with the numerical accuracy of BEM-FMM. The corresponding TMS (transcranial magnetic stimulation) modeling package is developed and made available online. It imports a SimNIBS surface segmentation and a coil field, and then exports electric-field values in selected surfaces or volumes. Additional information is also made available, such as discontinuous compartment surface electric fields and associated surface electric charge distributions.
MeSH term(s)	Head ; Transcranial Magnetic Stimulation
Language	English
Publishing date	2020-10-05
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC44109.2020.9175802
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Visible Human Project® female surface based computational phantom (Nelly) for radio-frequency safety evaluation in MRI coils.

Noetscher, Gregory M / Serano, Peter / Wartman, William A / Fujimoto, Kyoko / Makarov, Sergey N

PloS one

2021 Volume 16, Issue 12, Page(s) e0260922

Abstract: Quantitative modeling of specific absorption rate and temperature rise within the human body during 1.5 T and 3 T MRI scans is of clinical significance to ensure patient safety. This work presents justification, via validation and comparison, of the ... ...

Abstract	Quantitative modeling of specific absorption rate and temperature rise within the human body during 1.5 T and 3 T MRI scans is of clinical significance to ensure patient safety. This work presents justification, via validation and comparison, of the potential use of the Visible Human Project (VHP) derived Computer Aided Design (CAD) female full body computational human model for non-clinical assessment of female patients of age 50-65 years with a BMI of 30-36 during 1.5 T and 3 T based MRI procedures. The initial segmentation validation and four different application examples have been identified and used to compare to numerical simulation results obtained using VHP Female computational human model under the same or similar conditions. The first application example provides a simulation-to-simulation validation while the latter three application examples compare with measured experimental data. Given the same or similar coil settings, the computational human model generates meaningful results for SAR, B1 field, and temperature rise when used in conjunction with the 1.5 T birdcage MRI coils or at higher frequencies corresponding to 3 T MRI. Notably, the deviation in temperature rise from experiment did not exceed 2.75° C for three different heating scenarios considered in the study with relative deviations of 10%, 25%, and 20%. This study provides a reasonably systematic validation and comparison of the VHP-Female CAD v.3.0-5.0 surface-based computational human model starting with the segmentation validation and following four different application examples.
MeSH term(s)	Aged ; Female ; Humans ; Magnetic Resonance Imaging ; Middle Aged ; Phantoms, Imaging ; Radio Waves ; Radiographic Image Interpretation, Computer-Assisted/methods ; Visible Human Projects
Language	English
Publishing date	2021-12-10
Publishing country	United States
Document type	Comparative Study ; Journal Article
ISSN	1932-6203
ISSN (online)	1932-6203
DOI	10.1371/journal.pone.0260922
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Fast Individualized High-resolution Electric Field Modeling for Computational TMS Neuronavigation.

Daneshzand, Mohammad / Makarov, Sergey N / de Lara, Lucia I Navarro / Nummenmaa, Aapo

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2021 Volume 2021, Page(s) 1301–1304

Abstract: Transcranial Magnetic Stimulation (TMS) is a non-invasive method for safe and painless activation of cortical neurons. On-line visualization of the induced Electric field (E-field) has the potential to improve quantitative targeting and dosing of ... ...

Abstract	Transcranial Magnetic Stimulation (TMS) is a non-invasive method for safe and painless activation of cortical neurons. On-line visualization of the induced Electric field (E-field) has the potential to improve quantitative targeting and dosing of stimulation, however present commercially available systems are limited by simplified approximations of the anatomy. Here, we developed a near real-time method to accurately approximate the induced E-field of a freely moving TMS coil with an individualized high-resolution head model. We use a set of magnetic dipoles around the head to approximate the total E-field of a moving TMS coil. First, we match the incident field of the dipole basis set with the incident E-field of the moving coil. Then, based on the principle of superposition and uniqueness of the solutions, we apply same basis coefficients to the total E-field of the basis set. The computed E-fields results show high similarity with an established TMS solver both in terms of the amplitude and the spatial distribution patterns. The proposed method enables rapid visualization of the E-field with ~100 ms of computation time enabling interactive planning, targeting, dosing and coil positioning tasks for TMS neuronavigation.
MeSH term(s)	Electricity ; Neuronavigation ; Transcranial Magnetic Stimulation
Language	English
Publishing date	2021-12-07
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC46164.2021.9630065
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Application of a Neural Network Classifier to Radiofrequency-Based Osteopenia/Osteoporosis Screening.

Adams, Johnathan W / Zhang, Ziming / Noetscher, Gregory M / Nazarian, Ara / Makarov, Sergey N

IEEE journal of translational engineering in health and medicine

2021 Volume 9, Page(s) 4900907

Abstract: Objective: There is an unmet need for quick, physically small, and cost-effective office-based techniques that can measure bone properties without the use of ionizing radiation.: Methods: The present study reports the application of a neural network ... ...

Abstract	Objective: There is an unmet need for quick, physically small, and cost-effective office-based techniques that can measure bone properties without the use of ionizing radiation. Methods: The present study reports the application of a neural network classifier to the processing of previously collected data on very-low-power radiofrequency propagation through the wrist to detect osteoporotic/osteopenic conditions. Our approach categorizes the data obtained for two dichotomic groups. Group 1 included 27 osteoporotic/osteopenic subjects with low Bone Mineral Density (BMD), characterized by a Dual X-Ray Absorptiometry (DXA) T-score below - 1, measured within one year. Group 2 included 40 healthy and mostly young subjects without major clinical risk factors such as a (family) history of bone fracture. We process the complex radiofrequency spectrum from 30 kHz to 2 GHz. Instead of averaging data for both wrists, we process them independently along with the wrist circumference and then combine the results, which greatly increases the sensitivity. Measurements along with data processing require less than 1 min. Results: For the two dichotomic groups identified above, the neural network classifier of the radiofrequency spectrum reports a sensitivity of 83% and a specificity of 94%. Significance: These results are obtained without including any additional clinical risk factors. They justify that the radio transmission data are usable on their own as a predictor of bone density. This approach has the potential for screening patients at risk for fragility fractures in the office, given the ease of implementation, small device size, and low costs associated with both the technique and the equipment.
MeSH term(s)	Absorptiometry, Photon ; Bone Density ; Bone Diseases, Metabolic ; Humans ; Neural Networks, Computer ; Osteoporosis/diagnostic imaging
Language	English
Publishing date	2021-08-30
Publishing country	United States
Document type	Journal Article ; Research Support, N.I.H., Extramural ; Research Support, U.S. Gov't, Non-P.H.S.
ZDB-ID	2696555-0
ISSN	2168-2372 ; 2168-2372
ISSN (online)	2168-2372
ISSN	2168-2372
DOI	10.1109/JTEHM.2021.3108575
Database	MEDical Literature Analysis and Retrieval System OnLINE

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Article ; Online: Application of a Neural Network Classifier to Radiofrequency-Based Osteopenia/Osteoporosis Screening.

Adams, Johnathan / Zhang, Ziming / Noetscher, Gregory M / Nazarian, Ara / Makarov, Sergey N

Annual International Conference of the IEEE Engineering in Medicine and Biology Society. IEEE Engineering in Medicine and Biology Society. Annual International Conference

2021 Volume 2021, Page(s) 15–18

Abstract: This preliminary study reports application of a neural network classifier to the processing of previously collected data on low power radiofrequency propagation through the wrist with the goal to detect osteoporotic/osteopenic conditions. The data set ... ...

Abstract	This preliminary study reports application of a neural network classifier to the processing of previously collected data on low power radiofrequency propagation through the wrist with the goal to detect osteoporotic/osteopenic conditions. The data set used includes 67 subjects (23-94 years old, 50 females, 17 males, 27 osteoporotic/osteopenic, 40 healthy). We process the entire spectrum of the propagation coefficient through the wrist from 30 kHz to 2 GHz, with 201 sampling points in total. We found that the dichotomic diagnostic test of raw non-normalized radiofrequency data performed with the trained neural network approaches 90% specificity and ~70% sensitivity. These results are obtained without inclusion of any additional clinical risk factors. They justify that the radio transmission data are usable on their own as a predictor of bone density. With the inclusion of additional clinical risk factors, both specificity and sensitivity improve to 95% and 76% respectively. Our approach correlates well with the available DXA measurements and has the potential for screening patients at risk for fragility fractures, given the ease of implementation and low costs associated with both the technique and the equipment.Clinical Relevance- Dichotomic diagnostic test of raw non-normalized radiofrequency data performed with the trained neural network approaches 90% specificity and ~70% sensitivity. With the inclusion of other clinical risk factors, specificity and sensitivity increase to 95% and 76% respectively.
MeSH term(s)	Absorptiometry, Photon ; Adult ; Aged ; Aged, 80 and over ; Bone Density ; Bone Diseases, Metabolic ; Female ; Humans ; Male ; Middle Aged ; Neural Networks, Computer ; Osteoporosis/diagnosis ; Young Adult
Language	English
Publishing date	2021-12-07
Publishing country	United States
Document type	Journal Article
ISSN	2694-0604
ISSN (online)	2694-0604
DOI	10.1109/EMBC46164.2021.9630944
Database	MEDical Literature Analysis and Retrieval System OnLINE

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