As magnetic field strength in Magnetic Resonance Imaging (MRI) technology increases, maintaining the specific absorption rate (SAR) within safe limits across human head tissues becomes challenging due to the formation of standing waves at a shortened wavelength. Compounding this challenge is the uncertainty in the dielectric properties of head tissues, which notably affects the SAR induced by the radiofrequency (RF) coils in an ultra-high-field (UHF) MRI system. To this end, this study introduces a computational framework to quantify the impacts of uncertainties in head tissues’ dielectric properties on the induced SAR. The framework employs a surrogate model-assisted Monte Carlo (MC) technique, efficiently generating surrogate models of MRI observables (electric fields and SAR) and utilizing them to compute SAR statistics. Particularly, the framework leverages a high-dimensional model representation technique, which constructs the surrogate models of the MRI observables via univariate and bivariate component functions, approximated through generalized polynomial chaos expansions. The numerical results demonstrate the efficiency of the proposed technique, requiring significantly fewer deterministic simulations compared with traditional MC methods and other surrogate model-assisted MC techniques utilizing machine learning algorithms, all while maintaining high accuracy in SAR statistics. Specifically, the proposed framework constructs surrogate models of a local SAR with an average relative error of 0.28% using 289 simulations, outperforming the machine learning-based surrogate modeling techniques considered in this study. Furthermore, the SAR statistics obtained by the proposed framework reveal fluctuations of up to 30% in SAR values within specific head regions. These findings highlight the critical importance of considering dielectric property uncertainties to ensure MRI safety, particularly in 7 T MRI systems.

The insulation resistance (IR) test has been widely conducted by electricity companies to assess cable insulation health status due to its ease of applicability. However, there exist cases in which medium-voltage power cables pass the IR test but fail in service after re-energization. So far, historical IR data obtained via measurements have been recorded, but have never been systematically studied to improve the accuracy of detecting unhealthy cables. This paper proposes a data analytic approach using historical IR data to identify patterns for distinguishing between healthy and unhealthy cables and assess the cable insulation condition. The proposed approach first leverages a two-parameter Weibull analysis to link the failure probability of the cables to their ages. Such analysis sheds light on the classification of the cables with respect to their age and material. Next, a diminishing method (DM) is used to set the critical IR values and provide maximum detection of the unhealthy cables with minimum misclassification of the healthy cables as unhealthy. Finally, a self-organizing-map-based support vector machine (SOM-SVM) is used to classify the cables as healthy or unhealthy. The hybrid DM-SOM-SVM approach is applied to the historical IR data of 22kV and 6.6kV cross-linked polyethylene (XLPE) cables. Compared to current industrial IR criteria for insulation condition diagnosis, the proposed approach allows detecting 18.5x and 1.8x more unhealthy 22kV and 6.6kV XLPE distribution cables, respectively.

Objective: As magnetic resonance imaging (MRI) technologies advance, predicting local Specific Absorption Rate (SAR) distributions becomes more challenging. This difficulty arises from the unique anatomical structures and dielectric properties of individual subjects, coupled with the inherent non-uniformity of energy deposition within tissues during scanning. To rapidly estimate SAR values induced by ultra-high-field (UHF) MRI birdcage RF coil in near real-time, this paper proposes a deep learning-based framework. Methods: The proposed framework consists of two stages. During the dataset generation stage, high-dimensional model representation, a polynomial-based surrogate modeling technique, is used to generate a large and diverse dataset, thereby reducing the reliance on resource-intensive deterministic simulations performed by physics-based simulators. During the inference stage, the framework employs 3D Attention U-Net, processing relative permittivity and conductivity maps of head models along with incident electric fields to predict SAR distributions. Results: The 3D Attention U-Net outperforms all other 3D U-Net variants and demonstrates remarkable accuracy, with mean relative errors of 7.57% for voxel SAR, 5.63% for 10g-averaged SAR, and 2.60% for peak spatial SAR. Each prediction can be performed in less than half a second, outperforming traditional physics-based simulators by at least three orders of magnitude. Conclusion: The framework provides a significant computational advantage over traditional physics-based simulators while maintaining satisfactory accuracy. Significance: The computational framework, available on GitHub, enables real-time SAR predictions on permittivity and conductivity distributions on any unseen MRI head models. The framework will allow ultra-fast optimization and uncertainty quantification studies to be performed while designing new UHF MRI coils.

Neural Radiance Fields (NeRFs) have revolutionized scene novel view synthesis, offering visually realistic, precise, and robust implicit reconstructions. While recent approaches enable NeRF editing, such as object removal, 3D shape modification, or material property manipulation, the manual annotation prior to such edits makes the process tedious. Additionally, traditional 2D interaction tools lack an accurate sense of 3D space, preventing precise manipulation and editing of scenes. In this paper, we introduce a novel approach, called Blueprint Neural Field (BluNF), to address these editing issues. BluNF provides a robust and user-friendly 2D blueprint, enabling intuitive scene editing. By leveraging implicit neural representation, BluNF constructs a blueprint of a scene using prior semantic and depth information. The generated blueprint allows effortless editing and manipulation of NeRF representations. We demonstrate BluNF’s editability through an intuitive click-and-change mechanism, enabling 3D manipulations, such as masking, appearance modification, and object removal. Our approach significantly contributes to visual content creation, paving the way for further research in this area.