Huang et al., 2019 - Google Patents
Realistic volumetric-approach to simulate transcranial electric stimulation—ROAST—a fully automated open-source pipelineHuang et al., 2019
View PDF- Document ID
- 2891514704961250982
- Author
- Huang Y
- Datta A
- Bikson M
- Parra L
- Publication year
- Publication venue
- Journal of neural engineering
External Links
Snippet
Objective. Research in the area of transcranial electrical stimulation (TES) often relies on computational models of current flow in the brain. Models are built based on magnetic resonance images (MRI) of the human head to capture detailed individual anatomy. To …
- 230000011218 segmentation 0 abstract description 140
Classifications
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F17/00—Digital computing or data processing equipment or methods, specially adapted for specific functions
- G06F17/50—Computer-aided design
- G06F17/5009—Computer-aided design using simulation
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/30—Subject of image; Context of image processing
- G06T2207/30004—Biomedical image processing
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10084—Hybrid tomography; Concurrent acquisition with multiple different tomographic modalities
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T2207/00—Indexing scheme for image analysis or image enhancement
- G06T2207/10—Image acquisition modality
- G06T2207/10072—Tomographic images
- G06T2207/10088—Magnetic resonance imaging [MRI]
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06F—ELECTRICAL DIGITAL DATA PROCESSING
- G06F19/00—Digital computing or data processing equipment or methods, specially adapted for specific applications
- G06F19/30—Medical informatics, i.e. computer-based analysis or dissemination of patient or disease data
-
- G—PHYSICS
- G06—COMPUTING; CALCULATING; COUNTING
- G06T—IMAGE DATA PROCESSING OR GENERATION, IN GENERAL
- G06T7/00—Image analysis
Similar Documents
Publication | Publication Date | Title |
---|---|---|
Huang et al. | Realistic volumetric-approach to simulate transcranial electric stimulation—ROAST—a fully automated open-source pipeline | |
JP7383679B2 (en) | TTFIELD treatment with optimized electrode position on the head based on MRI conductivity measurements | |
Huang et al. | ROAST: an open-source, fully-automated, realistic volumetric-approach-based simulator for TES | |
Huang et al. | Automated MRI segmentation for individualized modeling of current flow in the human head | |
Shahid et al. | The value and cost of complexity in predictive modelling: role of tissue anisotropic conductivity and fibre tracts in neuromodulation | |
Acar et al. | Neuroelectromagnetic forward head modeling toolbox | |
Whitfield et al. | Automated delineation of radiotherapy volumes: are we going in the right direction? | |
Jiang et al. | Enhanced tES and tDCS computational models by meninges emulation | |
EP3247269B1 (en) | Tissue-orientation-based simulation of deep brain stimulation | |
Indahlastari et al. | Changing head model extent affects finite element predictions of transcranial direct current stimulation distributions | |
Cheng et al. | Altered topology of large-scale structural brain networks in chronic stroke | |
Shim et al. | Rapid prediction of brain injury pattern in mTBI by combining FE analysis with a machine-learning based approach | |
Jordan et al. | Cluster confidence index: A streamline‐wise pathway reproducibility metric for diffusion‐weighted MRI tractography | |
Klein et al. | Sensitivity analysis of neurodynamic and electromagnetic simulation parameters for robust prediction of peripheral nerve stimulation | |
Htet et al. | Collection of CAD human head models for electromagnetic simulations and their applications | |
Vonach et al. | A method for rapid production of subject specific finite element meshes for electrical impedance tomography of the human head | |
Timmons et al. | End-to-end workflow for finite element analysis of tumor treating fields in glioblastomas | |
Farcito et al. | Accurate anatomical head segmentations: a data set for biomedical simulations | |
Kalloch et al. | Semi‐automated generation of individual computational models of the human head and torso from MR images | |
Kataja et al. | A probabilistic transcranial magnetic stimulation localization method | |
Indahlastari et al. | Benchmarking transcranial electrical stimulation finite element models: a comparison study | |
Kalloch et al. | A flexible workflow for simulating transcranial electric stimulation in healthy and lesioned brains | |
Wartman et al. | An adaptive h-refinement method for the boundary element fast multipole method for quasi-static electromagnetic modeling | |
Alonso et al. | Biophysical modeling of the electric field magnitude and distribution induced by electrical stimulation with intracerebral electrodes | |
Kybartaite | Computational representation of a realistic head and brain volume conductor model: electroencephalography simulation and visualization study |