Evaluation of Chinese populational exposure to environmental electromagnetic field based on stochastic dosimetry and parametric human modelling.

This study aimed to estimate the distribution of the whole-body averaged specific absorption rate (WBSAR) using several measurable physique parameters for Chinese adult population exposed to environmental electromagnetic fields (EMFs) of current wireless communication frequencies, and to discuss the effects of these physique parameters in the frequency-dependent dosimetric results. The physique distribution of Chinese adults was obtained from the National Physical Fitness and Health Database comprising 81,490 adult samples. The number of physique parameters used to construct the surrogate model was reduced to three via mutual information analysis. A stochastic method with 40 deterministic simulations was used to generate frequency-dependent and gender-specific surrogate models for WBSAR via polynomial chaos expansion. In the simulations, we constructed anatomically correct models conforming to the targeted physique parameters via deformable human modelling technique, which was based on deep learning from the image database including 767 Chinese adults. Thereafter, we analysed the sensitivity of the physique parameters to WBSAR by covariance-based Sobol decomposition. The results indicated that the generated models were consistent with the targeted physique parameters. The estimated dosimetric results were validated using finite-difference time-domain simulations (the error was < 6% across all the investigated frequencies for WBSAR). The novelty of the study included that it demonstrated the feasibility of estimating the individual WBSAR using a limited number of physique parameters with the aid of surrogate modelling. In addition, the population-based distribution of the WBSAR in Chinese adults was firstly presented in the manuscript. The results also indicated that the different combinations of physique parameter, dependent on genders and frequencies, significantly influenced the WBSAR, although the general conservativeness of the guidelines of the International Commission on Non-Ionizing Radiation and Protection can be confirmed in the surveyed population.

Sophisticated Structural Tuning of NiMoO4@MnCo2O4 Nanomaterials for High Performance Hybrid Capacitors.

NiMoO4 is an excellent candidate for supercapacitor electrodes, but poor cycle life, low electrical conductivity, and small practical capacitance limit its further development. Therefore, in this paper, we fabricate NiMoO4@MnCo2O4 composites based on a two-step hydrothermal method. As a supercapacitor electrode, the sample can reach 3000 mF/cm2 at 1 mA/cm2. The asymmetric supercapacitor (ASC), NiMoO4@MnCo2O4//AC, can be constructed with activated carbon (AC) as the negative electrode, the device can reach a maximum energy density of 90.89 mWh/cm3 at a power density of 3726.7 mW/cm3 and the capacitance retention can achieve 78.4% after 10,000 cycles.

Lung CT Image Registration through Landmark-constrained Learning with Convolutional Neural Network.

Accurate registration of lung computed tomography (CT) image is a significant task in thorax image analysis. Recently deep learning-based medical image registration methods develop fast and achieve promising performance on accuracy and speed. However, most of them learned the deformation field through intensity similarity but ignored the importance of aligning anatomical landmarks (e.g., the branch points of airway and vessels). Accurate alignment of anatomical landmarks is essential for obtaining anatomically correct registration. In this work, we propose landmark constrained learning with a convolutional neural network (CNN) for lung CT registration. Experimental results of 40 lung 3D CT images show that our method achieves 0.93 in terms of Dice index and 3.54 mm of landmark Euclidean distance on lung CT registration task, which outperforms state-of-the-art methods in registration accuracy.

Registration-based Construction of a Whole-body Human Phantom Library for Anthropometric Modeling.

Various computational human phantoms have been proposed in the past decades, but few of them include delicate anthropometric variations. In this study, we build a whole-body phantom library including 145 anthropometric parameters. This library is constructed by registration-based pipeline, which transfers a standard whole-body anatomy template to an anthropometry-adjustable body shape library (MakeHuman™). Therefore, internal anatomical structures are created for body shapes of different anthropometric parameters. Based on the constructed library, we can generate individualized whole-body phantoms according to given arbitrary anthropometric parameters. Moreover, the proposed phantom library can also be converted to voxel-based and tetrahedron-based model for further personalized simulation. We hope this phantom library will serve as a computational tool in research community.

Deformable torso phantoms of Chinese adults for personalized anatomy modelling.

In recent years, there has been increasing demand for personalized anatomy modelling for medical and industrial applications, such as ergonomics device development, clinical radiological exposure simulation, biomechanics analysis, and 3D animation character design. In this study, we constructed deformable torso phantoms that can be deformed to match the personal anatomy of Chinese male and female adults. The phantoms were created based on a training set of 79 trunk computed tomography (CT) images (41 males and 38 females) from normal Chinese subjects. Major torso organs were segmented from the CT images, and the statistical shape model (SSM) approach was used to learn the inter-subject anatomical variations. To match the personal anatomy, the phantoms were registered to individual body surface scans or medical images using the active shape model method. The constructed SSM demonstrated anatomical variations in body height, fat quantity, respiratory status, organ geometry, male muscle size, and female breast size. The masses of the deformed phantom organs were consistent with Chinese population organ mass ranges. To validate the performance of personal anatomy modelling, the phantoms were registered to the body surface scan and CT images. The registration accuracy measured from 22 test CT images showed a median Dice coefficient over 0.85, a median volume recovery coefficient (RCvlm ) between 0.85 and 1.1, and a median averaged surface distance (ASD) < 1.5 mm. We hope these phantoms can serve as computational tools for personalized anatomy modelling for the research community.