The Impact of Anisotropy on the Accuracy of Conductivity Imaging: A Quantitative Validation Study

Deep Learning-Based Development of Personalized Human Head Model With Non-Uniform Conductivity for Brain Stimulation

Electromagnetic stimulation of the human brain is a key tool for neurophysiological characterization and the diagnosis of several neurological disorders. Transcranial magnetic stimulation (TMS) is a commonly used clinical procedure. However, personalized TMS requires a pipeline for individual head model generation to provide target-specific stimulation. This process includes intensive segmentation of several head tissues based on magnetic resonance imaging (MRI), which has significant potential for segmentation error, especially for low-contrast tissues. Additionally, a uniform electrical conductivity is assigned to each tissue in the model, which is an unrealistic assumption based on conventional volume conductor modeling. This study proposes a novel approach for fast and automatic estimation of the electric conductivity in the human head for volume conductor models without anatomical segmentation. A convolutional neural network is designed to estimate personalized electrical conductivity values based on anatomical information obtained from T1- and T2-weighted MRI scans. This approach can avoid the time-consuming process of tissue segmentation and maximize the advantages of position-dependent conductivity assignment based on the water content values estimated from MRI intensity values. The computational results of the proposed approach provide similar but smoother electric field distributions of the brain than that provided by conventional approaches.

Learning-based estimation of dielectric properties and tissue density in head models for personalized radio-frequency dosimetry

Radio-frequency dosimetry is an important process in assessments for human exposure safety and for compliance of related products. Recently, computational human models generated from medical images have often been used for such assessment, especially to consider the inter-subject variability. However, a common procedure to develop personalized models is time consuming because it involves excessive segmentation of several components that represent different biological tissues, which is a major obstacle in the inter-subject variability assessment of radiation safety. Deep learning methods have been shown to be a powerful approach for pattern recognition and signal analysis. Convolutional neural networks with deep architecture are proven robust for feature extraction and image mapping in several biomedical applications. In this study, we develop a learning-based approach for fast and accurate estimation of the dielectric properties and density of tissues directly from magnetic resonance images in a single shot. The smooth distribution of the dielectric properties in head models, which is realized using a process without tissue segmentation, improves the smoothness of the specific absorption rate (SAR) distribution compared with that in the commonly used procedure. The estimated SAR distributions, as well as that averaged over 10 g of tissue in a cubic shape, are found to be highly consistent with those computed using the conventional methods that employ segmentation.

Conductivity Anisotropy Influence on Acoustic Sources for Magnetoacoustic Tomography With Magnetic Induction

OBJECTIVE

As the multi-physics imaging approach, magnetoacoustic tomography with magnetic induction (MAT-MI) attracts more and more attentions, focusing on image reconstruction for conductivity isotropic tissues.

METHODS

By introducing vector analyses to electromagnetic stimulation and magnetoacoustic excitation for a single-layer cylindrical conductivity anisotropic model, the acoustic source strength (ASS) of MAT-MI is derived in explicit formula and the influence of the anisotropic conductivity tensor is also analyzed.

RESULTS

Theoretical and numerical studies demonstrate that the ASS generated at the tissue boundary is composed of an alternating current (AC) fluctuation and a direct current (DC) bias, where the distribution of the AC fluctuation with respect to the spatial angle exhibits a double-period cosine function, and the DC bias remains constant at each angle. The dependences of the AC fluctuation and the DC bias on the anisotropic component ratio (ACR) and the conductivity tensor are proved by numerical results, which are also verified by the special cases of the zero AC fluctuation for the conductivity isotropic medium and the negative DC bias of the low-conductivity medium.

CONCLUSION

With the measurements of the ASS around the model, the anisotropic conductivity tensor can be reconstructed by the amplitudes of the AC fluctuation and the DC bias with the conductivity of the isotropic surrounding medium.

SIGNIFICANCE

The favorable results provide a new method for anisotropic conductivity measurement, and suggest the application potential of MAT-MI in biomedical imaging and nondestructive testing for conductivity anisotropic tissues.