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Transcranial magnetic stimulation (TMS) is a non-invasive neurostimulation technique that is increasingly used in the treatment of neuropsychiatric disorders and neuroscience research. Due to the complex structure of the brain and the electrical conductivity variation across subjects, identification of subject-specific brain regions for TMS is important to improve the treatment efficacy and understand the mechanism of treatment response. Numerical computations have been used to estimate the stimulated electric field (E-field) by TMS in brain tissue. But the relative long computation time limits the application of this approach. In this paper, we propose a deep-neural-network based approach to expedite the estimation of whole-brain E-field by using a neural network architecture, named 3D-MSResUnet and multimodal imaging data. The 3D-MSResUnet network integrates the 3D U-net architecture, residual modules and a mechanism to combine multi-scale feature maps. It is trained using a large dataset with finite element method (FEM) based E-field and diffusion magnetic resonance imaging (MRI) based anisotropic volume conductivity or anatomical images. The performance of 3D-MSResUnet is evaluated using several evaluation metrics and different combinations of imaging modalities and coils. The experimental results show that the output E-field of 3D-MSResUnet provides reliable estimation of the E-field estimated by the state-of-the-art FEM method with significant reduction in prediction time to about 0.24 second. Thus, this study demonstrates that neural networks are potentially useful tools to accelerate the prediction of E-field for TMS targeting. [ABSTRACT FROM AUTHOR]

Purpose: The derivation of quantitative information from medical images in a practical manner is essential for quantitative radiology (QR) to become a clinical reality, but still faces a major hurdle because of image segmentation challenges. With the goal of performing disease quantification in lymph node (LN) stations without explicit nodal delineation, this paper presents a novel approach for disease quantification (DQ) by automatic recognition of LN zones and detection of malignant lymph nodes within thoracic LN zones via positron emission tomography/computed tomography (PET/CT) images. Named AAR‐LN‐DQ, this approach decouples DQ methods from explicit nodal segmentation via an LN recognition strategy involving a novel globular filter and a deep neural network called SegNet. Method: The methodology consists of four main steps: (a) Building lymph node zone models by automatic anatomy recognition (AAR) method. It incorporates novel aspects of model building that relate to finding an optimal hierarchy for organs and lymph node zones in the thorax. (b) Recognizing lymph node zones by the built lymph node models. (c) Detecting pathologic LNs in the recognized zones by using a novel globular filter (g‐filter) and a multi‐level support vector machine (SVM) classifier. Here, we make use of the general globular shape of LNs to first localize them and then use a multi‐level SVM classifier to identify pathologic LNs from among the LNs localized by the g‐filter. Alternatively, we designed a deep neural network called SegNet which is trained to directly recognize pathologic nodes within AAR localized LN zones. (d) Disease quantification based on identified pathologic LNs within localized zones. A fuzzy disease map is devised to express the degree of disease burden at each voxel within the identified LNs to simultaneously handle several uncertain phenomena such as PET partial volume effects, uncertainty in localization of LNs, and gradation of disease content at the voxel level. We focused on the task of disease quantification in patients with lymphoma based on PET/CT acquisitions and devised a method of evaluation. Model building was carried out using 42 near‐normal patient datasets via contrast‐enhanced CT examinations of their thorax. PET/CT datasets from an additional 63 lymphoma patients were utilized for evaluating the AAR‐LN‐DQ methodology. We assess the accuracy of the three main processes involved in AAR‐LN‐DQ via fivefold cross validation: lymph node zone recognition, abnormal lymph node localization, and disease quantification. Results: The recognition and scale error for LN zones were 12.28 mm ± 1.99 and 0.94 ± 0.02, respectively, on normal CT datasets. On abnormal PET/CT datasets, the sensitivity and specificity of pathologic LN recognition were 84.1% ± 0.115 and 98.5% ± 0.003, respectively, for the g‐filter‐SVM strategy, and 91.3% ± 0.110 and 96.1% ± 0.016, respectively, for the SegNet method. Finally, the mean absolute percent errors for disease quantification of the recognized abnormal LNs were 8% ± 0.09 and 14% ± 0.10 for the g‐filter‐SVM method and the best SegNet strategy, respectively. Conclusions: Accurate disease quantification on PET/CT images without performing explicit delineation of lymph nodes is feasible following lymph node zone and pathologic LN localization. It is very useful to perform LN zone recognition by AAR as this step can cover most (95.8%) of the abnormal LNs and drastically reduce the regions to search for abnormal LNs. This also improves the specificity of deep networks such as SegNet significantly. It is possible to utilize general shape information about LNs such as their globular nature via g‐filter and to arrive at high recognition rates for abnormal LNs in conjunction with a traditional classifier such as SVM. Finally, the disease map concept is effective for estimating disease burden, irrespective of how the LNs are identified, to handle various uncertainties without having to address them explicitly one by one. [ABSTRACT FROM AUTHOR]

In this study, the authors propose a multi‐task learning with deconvolution network (MTL‐DN) method for the multi‐label classification of multiple power quality disturbances (MPQDs). First, the labels of MPQDs are assigned to three groups corresponding to three learning tasks and the label correlations among various PQDs are utilised in the joint learning of interrelated tasks. A weighted joint loss function is adopted to balance multiple tasks and to ensure that all of the tasks achieve the global optimum. Second, considering the effect of the pooling operation on transient disturbances, a deconvolution network is employed to reconstruct the erased feature and to merge them into high‐level feature for the final classification. Finally, the authors employed two sets of evaluation metrics to verify the validity of the MTL‐DN method and compared it with three state‐of‐the‐art multi‐label classification methods. Extensive experiments based on simulated and real‐world datasets demonstrated that their method performed better and it greatly improved the accuracy rate for identifying MPQDs under different signal‐to‐noise ratio conditions. [ABSTRACT FROM AUTHOR]

This paper presents an advance on image interpolation based on ant colony algorithm (AACA) for high resolution image scaling. The difference between the proposed algorithm and the previously proposed optimization of bilinear interpolation based on ant colony algorithm (OBACA) is that AACA uses global weighting, whereas OBACA uses local weighting scheme. The strength of the proposed global weighting of AACA algorithm depends on employing solely the pheromone matrix information present on any group of four adjacent pixels to decide which case deserves a maximum global weight value or not. Experimental results are further provided to show the higher performance of the proposed AACA algorithm with reference to the algorithms mentioned in this paper. [ABSTRACT FROM AUTHOR]

In many communication environments for distributed MIMO (D-MIMO) systems, insufficient angular spread and dominant nonfading paths exist simultaneously. In such situations, a spatially correlated Rician/Lognormal channel model is appropriate. In this letter, analytic upper bound of ergodic capacity is presented for D-MIMO systems operating on Rician/Lognormal fading channels. In the analysis, it is assumed that receive correlation and transmit correlation exist simultaneously, and the rank of channel mean matrix is arbitrary. To avoid the appearance of Hayakawa polynomials, which cannot be simplified, the determinant in the expression of ergodic capacity is expanded by use of the linear sum of determinants of noncentral wishart matrices. The results of computer simulation show that the bound remains tight across the entire signal-to-noise ratio (SNR) regime. [ABSTRACT FROM PUBLISHER]

In this letter, both analytic upper bound and lower bound of ergodic capacity are presented for distributed multiple-input multiple-output (D-MIMO) systems operating on Rayleigh/Lognormal fading channels. In the analysis, it is assumed that receive correlation and transmit correlation exist simultaneously, and the numbers of receive and transmit antennas both are arbitrary. The determinant in the expression of ergodic capacity is expanded as a linear sum of determinants of quadratic forms. Then the moments of the determinants are determined. Thereby upper and lower bounds on the ergodic capacity are obtained. The bounds remain tight across the entire Signal-to-Noise ratio (SNR) regime. The validity of above conclusion is verified by computer simulation. [ABSTRACT FROM PUBLISHER]

It is found that the pore microstructures of textile fabrics, widely used in the manufacture of fiber-reinforced composites, exhibit the fractal characters. The fractal behaviors are described by the proposed analytical method and measured by the box-counting method for the three different types of textile fabrics: plain woven, four-harness, bidirectional-stitched fiberglass mats. The pore area fractal dimension is derived analytically and found to be the function of the porosity and architectural parameters of fabrics. The results indicate that the fractal characters are isotropic although the fabrics are rothotropic in structures. The theoretical predictions by the proposed analytical model are in good agreement with those from the boxcounting method, and this verifies the proposed fractal dimension model. The present fractal analysis may have the potential and significance on fractal analysis of transport properties (such as the permeability, dispersion, thermal and mechanical properties) in porous media. [ABSTRACT FROM AUTHOR]