Search Results (47)

We propose a method to reconstruct a 3D coordinate system and a vascular map for the operation of magnetic medical robots (MMRs) controlled by a magnetic navigation system (MNS) using two 2D X-ray images and four corners of an MNS. Utilizing the proposed method, we calculated the relative rotation angle of a C-arm considering its rotational precision error. We derived the position information and 3D coordinate system of an MNS workspace in which the magnetic fields are generated and controlled by an MNS. The proposed method can also be utilized to reconstruct vascular maps. Reconstructed vascular maps are in the 3D coordinate system of the C-arm and can be transformed into the 3D coordinate system of an MNS workspace to generate the magnetic flux density with the desired direction and magnitude at the position of the MMR. The proposed method allows us to remotely and precisely control the MMR inserted into the vessel by controlling the external magnetic field. The proposed method was validated through in vitro experiments with an MNS mock-up and a vascular jig. Finally, the proposed method was applied to in vivo experiments where the MMR was inserted into the superficial femoral artery of a mini pig to remotely control the motion of the MMR. This research will enable precise and effective control of MMRs in various medical procedures utilizing an MNS. Full article

Accurate current sensing in rectangular conductors is challenged by mechanical deformations, including eccentricity (X/Y-axis shifts) and inclination (Z-axis tilt), which distort magnetic field distributions and induce measurement errors. To address this, we propose a bio-inspired error compensation strategy integrating an elliptically configured Hall sensor array with a hybrid Grey Wolf Optimizer (GWO)-enhanced backpropagation neural network. The eccentric displacement and tilt angle of the conductor are quantified via a three-dimensional magnetic field reconstruction and current inversion modeling. A dual-stage optimization framework is implemented: first, establishing a BP neural network for real-time conductor state estimations, and second, leveraging the GWO’s swarm intelligence to refine network weights and thresholds, thereby avoiding local optima and enhancing the robustness against asymmetric field patterns. The experimental validation under extreme mechanical deformations (X/Y-eccentricity: ±8 mm; Z-tilt: ±15°) demonstrates the strategy’s efficacy, achieving a 65.07%, 45.74%, and 76.15% error suppression for X-, Y-, and Z-axis deviations. The elliptical configuration reduces the installation footprint by 72.4% compared with conventional circular sensor arrays while maintaining a robust suppression of eccentricity- and tilt-induced errors, proving critical for space-constrained applications, such as electric vehicle powertrains and miniaturized industrial inverters. This work bridges bio-inspired algorithms and adaptive sensing hardware, offering a systematic solution to mechanical deformation-induced errors in high-density power systems. Full article

Background/Objectives: Cranial reconstruction (cranioplasty) is a surgical procedure performed to restore skull function and aesthetics following trauma, oncological conditions, or congenital defects. Magnetic resonance imaging (MRI) is commonly used for the postoperative monitoring and diagnosis of patients with cranial implants. However, MRI artifacts caused by these implants can compromise imaging accuracy and diagnostic precision. This study aims to evaluate the extent of MRI artifacts caused by titanium and polyether ether ketone (PEEK) cranial implants and to identify optimal imaging sequences to minimize these artifacts. Methods: Phantom skull models with cranial defects of varying sizes (one-quarter, one-third, and one-half of the skull) were used to simulate real-world clinical conditions. The defects were filled with a water-based medium containing simulated brain tissue and tumor models. Custom 3D-printed titanium and PEEK cranial implants were fixed onto the phantom skulls and scanned using 1.5 T and 3 T MRI scanners. Various imaging sequences were tested, with a focus on optimizing parameters to reduce artifact formation. Turbo Spin Echo (TSE) sequences with fat saturation were implemented to assess their effectiveness in artifact reduction. Results: The study found that MRI artifacts varied based on the implant material, defect size, and magnetic field strength. A higher field strength (3 T) resulted in more pronounced artifacts. However, the use of TSE sequences with fat saturation significantly reduced artifacts and improved lesion visualization, enhancing diagnostic accuracy. Conclusions: This research highlights the importance of optimized MRI protocols when imaging patients with cranial implants. Proper selection of imaging sequences, particularly TSE with fat saturation, can mitigate artifacts and improve diagnostic precision, ultimately benefiting patient outcomes in clinical radiology. Full article

Background: Rupture of the ACL is a common injury among men and women athletes. While planning the surgical ACL reconstruction procedure, the eventual graft’s diameter is extremely important. Many parameters are therefore evaluated pre-surgery to ensure access to reliable data for estimating the graft diameter. Considering this, magnetic resonance imaging (MRI), particularly qualitative analyses of the hamstring tendons, offers a promising approach. Methods: In a retrospective analysis, we carried out 3D segmentation of the gracilis (GT) and semitendinosus tendon (ST) utilizing MRI with varying slice thicknesses and field strengths. The cross-sectional area (CSA) was calculated on different levels (by relying on the models we had thus created) to generate a mean of CSA with six specific segments. We then correlated the mean CSA with the diameter of the graft measured during surgery. Results: A total of 32 patients were included (12 female, 20 male) in this retrospective analysis. We observed the largest CSA in segment 10 mm–0 (16.8 ± 6.1) with differences between men and women. The graft size and tendon diameter correlated significantly in all segments throughout our study cohort. The strongest correlation was apparent in the segment 10 mm–0 (r = 0.552). Conclusions: MRI-based 3D segmentation and the STGT CSA represent a reliable method for estimating preoperatively a quadrupled hamstring graft diameter. The 10 mm–0 mm segment above the joint line showed a strong correlation, making it an ideal reference for graft planning. Full article

The porosity structure of rocks is an important research topic in fields such as civil engineering, geology, and petroleum engineering, with significant implications for groundwater flow, oil and gas reservoir exploitation, and geological hazard prediction. This paper systematically explores the research progress and knowledge graph construction methods for rock porosity structure, aiming to provide scientific foundations for a multidimensional understanding and application of rock porosity structure. It outlines the basic concepts and classifications of rock porosity, including the definitions and characteristics of macropores, micropores, and nanopores. This paper provides a comprehensive overview of the main technical methods employed in recent research on rock porosity structure, including X-ray computed tomography, scanning electron microscopy, nuclear magnetic resonance, and 3D reconstruction technologies. It explores the relationship between porosity structure and the physical and mechanical properties of rocks, focusing on the impact of porosity, permeability, and pore morphology on rock mechanical behavior. A knowledge graph of rock porosity structure is constructed to highlight key research areas, core technologies, and emerging applications in this field. The study utilizes extensive literature review and data mining techniques, analyzing 4807 papers published over the past 20 years, sourced from the Web of Science database. Bibliometric and knowledge graph analyses were performed, examining trends such as annual publication volume, country/region distribution, institutional affiliations, journal sources, subject categories, and research databases, as well as research hotspots and frontier developments. This analysis offers valuable insights into the current state of rock porosity structure research, shedding light on its progress and providing references for further advancing research in this area. Full article

The general-purpose Compact Muon Solenoid (CMS) detector at the Large Hadron Collider (LHC) at CERN is used to study the production of new particles in proton–proton collisions at an LHC center of mass energy of 13.6 TeV. The detector includes a magnet based on a 6 m diameter superconducting solenoid coil operating at a current of 18.164 kA. This current creates a central magnetic flux density of 3.8 T that allows for the high-precision measurement of the momenta of the produced charged particles using tracking and muon subdetectors. The CMS magnet contains a 10,000 ton flux-return yoke of dodecagonal shape made from the assembly of construction steel blocks distributed in several layers. These steel blocks are magnetized with the solenoid returned magnetic flux and wrap the muons escaping the hadronic calorimeters of total absorption. To reconstruct the muon trajectories, and thus to measure the muon momenta, the drift tube and cathode strip chambers are located between the layers of the steel blocks. To describe the distribution of the magnetic flux in the magnet yoke layers, a three-dimensional computer model of the CMS magnet is used. To validate the calculations, special measurements are performed, with the flux loops wound in 22 cross-sections of the flux-return yoke blocks. The measured voltages induced in the flux loops during the CMS magnet ramp-ups and -downs, as well as during the superconducting coil fast discharges, are integrated over time to obtain the initial magnetic flux densities in the flux loop cross-sections. The measurements obtained during the seven standard ramp-downs of the magnet were analyzed in 2018. From that time, three fast discharges occurred during the standard ramp-downs of the magnet. This allows us to single out the contributions of the eddy currents, induced in steel, to the flux loop voltages registered during the fast discharges of the coil. Accounting for these contributions to the flux loop measurements during intentionally triggered fast discharges in 2006 allows us to perform the validation of the CMS magnet computer model with better precision. The technique for the flux loop measurements and the obtained results are presented and discussed. The method for measuring magnetic flux density in steel blocks described in this study is innovative. The experience of 3D modeling and measuring the magnetic field in steel blocks of the magnet yoke, as part of a muon detector system, has good prospects for use in the construction and operation of particle detectors for the Future Circular Electron–Positron Collider and the Circular Electron–Positron Collider. Full article

Background: The invasion of glioblastoma cells beyond the visible tumor margin depicted by conventional neuroimaging is believed to mediate recurrence and predict poor survival. Radiomic biomarkers that are associated with the direction and extent of tumor infiltration are, however, non-existent. Methods: Patients from a single center with newly diagnosed glioblastoma (n = 7) underwent preoperative Q-space magnetic resonance imaging (QSI; 3T, 64 gradient directions, b = 1000 s/mm²) between 2018 and 2019. Tumors were manually segmented, and patterns of inter-voxel coherence spatially intersecting each segmentation were generated to represent tumor-associated tractography. One patient additionally underwent regional biopsy of diffusion tract- versus non-tract-associated tissue during tumor resection for RNA sequencing. Imaging data from this cohort were compared with a historical cohort of n = 66 glioblastoma patients who underwent similar QSI scans. Associations of tractography-derived metrics with survival were assessed using t-tests, linear regression, and Kaplan–Meier statistics. Patient-derived glioblastoma xenograft (PDX) mice generated with the sub-hippocampal injection of human-derived glioblastoma stem cells (GSCs) were scanned under high-field conditions (QSI, 7T, 512 gradient directions), and tumor-associated tractography was compared with the 3D microscopic reconstruction of immunostained GSCs. Results: In the principal enrollment cohort of patients with glioblastoma, all cases displayed tractography patterns with tumor-intersecting tract bundles extending into brain parenchyma, a phenotype which was reproduced in PDX mice as well as in a larger comparison cohort of glioblastoma patients (n = 66), when applying similar methods. Reconstructed spatial patterns of GSCs in PDX mice closely mirrored tumor-associated tractography. On a Kaplan–Meier survival analysis of n = 66 patients, the calculated intra-tumoral mean diffusivity predicted the overall survival (p = 0.037), as did tractography-associated features including mean tract length (p = 0.039) and mean projecting tract length (p = 0.022). The RNA sequencing of human tissue samples (n = 13 tumor samples from a single patient) revealed the overexpression of transcripts which regulate cell motility in tract-associated samples. Conclusions: QSI discriminates tumor-specific patterns of inter-voxel coherence believed to represent white matter pathways which may be susceptible to glioblastoma invasion. These findings may lay the groundwork for future work on therapeutic targeting, patient stratification, and prognosis in glioblastoma. Full article

In this study, we describe a deep learning (DL)-based workflow for the three-dimensional (3D) geophysical inversion of magnetotelluric (MT) data. We derived a mathematical connection between a 3D resistivity model and the surface-observed electric/magnetic field response by using a fully connected neural network framework (U-Net). Limited by computer hardware functionality, the resistivity models were generated by using a random walk technique to enlarge the generalization coverage of the neural network model, and 15,000 paired datasets were utilized to train and validate it. Grid search was used to select the optimal configuration parameters. With the optimal model framework from the parameter tuning phase, the metrics showed stable convergence during model training/validation. In the test period, the trained model was applied to predict the resistivity distribution by using both the simulated synthetic and the real MT data from the Mount Meager area, British Columbia. The reliability of the model prediction was verified with noised input data from the synthetic model. The calculated results can be used to reconstruct the position and shape trends of bodies with anomalous resistivity, which verifies the stability and performance of the DL-based 3D inversion algorithm and showcases its potential practical applications. Full article

In this work an innovative approach was developed to address a significant challenge in the field of radiation dosimetry: the accurate measurement of spatial dose distributions using Fricke gel dosimeters. Hydrogels are widely used in radiation dosimetry due to their ability to simulate the tissue-equivalent properties of human tissue, making them ideal for measuring and mapping radiation dose distributions. Among the various gel dosimeters, Fricke gels exploit the radiation-induced oxidation of ferrous ions to ferric ions and are particularly notable due to their sensitivity. The concentration of ferric ions can be measured using various techniques, including magnetic resonance imaging (MRI) or spectrophotometry. While Fricke gels offer several advantages, a significant hurdle to their widespread application is the diffusion of ferric ions within the gel matrix. This phenomenon leads to a blurring of the dose distribution over time, compromising the accuracy of dose measurements. To mitigate the issue of ferric ion diffusion, researchers have explored various strategies such as the incorporation of additives or modification of the gel composition to either reduce the mobility of ferric ions or stabilize the gel matrix. The computational method proposed leverages the power of artificial intelligence, particularly deep learning, to mitigate the effects of ferric ion diffusion that can compromise measurement precision. By employing Physics Informed Neural Networks (PINNs), the method introduces a novel way to apply physical laws directly within the learning process, optimizing the network to adhere to the principles governing ion diffusion. This is particularly advantageous for solving the partial differential equations that describe the diffusion process in 2D and 3D. By inputting the spatial distribution of ferric ions at a given time, along with boundary conditions and the diffusion coefficient, the model can backtrack to accurately reconstruct the original ion distribution. This capability is crucial for enhancing the fidelity of 3D spatial dose measurements, ensuring that the data reflect the true dose distribution without the artifacts introduced by ion migration. Here, multidimensional models able to handle 2D and 3D data were developed and tested against dose distributions numerically evolved in time from 20 to 100 h. The results in terms of various metrics show a significant agreement in both 2D and 3D dose distributions. In particular, the mean square error of the prediction spans the range $1\times {10}^{-6}$–$1\times {10}^{-4}$, while the gamma analysis results in a 90–100% passing rate with 3%/2 mm, depending on the elapsed time, the type of distribution modeled and the dimensionality. This method could expand the applicability of Fricke gel dosimeters to a wider range of measurement tasks, from simple planar dose assessments to intricate volumetric analyses. The proposed technique holds great promise for overcoming the limitations imposed by ion diffusion in Fricke gel dosimeters. Full article

The Fermi bubbles and the eROSITA bubbles around the Milky Way Galaxy are speculated to be the aftermaths of past jet eruptions from a supermassive black hole in the galactic center. In this work, a 2.5D axisymmetric relativistic magnetohydrodynamic (RMHD) model is applied to simulate a jet eruption from our galactic center and to reconstruct the observed Fermi bubbles and eROSITA bubbles. High-energy non-thermal electrons are excited around forward shock and discontinuity transition regions in the simulated plasma distributions. The $\gamma$-ray and X-ray emissions from these electrons manifest patterns on the skymap that match the observed Fermi bubbles and eROSITA bubbles, respectively, in shape, size and radiation intensity. The influence of the background magnetic field, initial mass distribution in the Galaxy, and the jet parameters on the plasma distributions and hence these bubbles is analyzed. Subtle effects on the evolution of plasma distributions attributed to the adoption of a galactic disk model versus a spiral-arm model are also studied. Full article

Single-plane fluoroscopy systems with image intensifiers remain commonly employed in a clinical setting. The imagery they capture is vulnerable to several types of geometric distortions introduced by the system’s components and their assembly as well as interactions with the local and global magnetic fields. In this study, the application of a self-calibrating bundle adjustment is investigated as a method to correct geometric distortions in single-plane fluoroscopic imaging systems. The resulting calibrated imagery is then applied in the quantitative analysis of diaphragmatic motion and potential diagnostic applications to hemidiaphragm paralysis. The calibrated imagery is further explored and discussed in its potential impact on areas of surgical navigation. This work was accomplished through the application of a controlled experiment with three separate Philips Easy Diagnost R/F Systems. A highly redundant (~2500 to 3500 degrees-of-freedom) and geometrically strong network of 18 to 22 images of a low-cost target field was collected. The target field comprised 121 pre-surveyed tantalum beads embedded on a 25.4 mm × 25.4 mm acrylic base plate. The modeling process resulted in the estimation of five to eight distortion coefficients, depending on the system. The addition of these terms resulted in 83–85% improvement in terms of image point precision (model fit) and 85–95% improvement in 3D object reconstruction accuracy after calibration. This study demonstrates significant potential in enhancing the accuracy and reliability of fluoroscopic imaging, thereby improving the overall quality and effectiveness of medical diagnostics and treatments. Full article

The paper presents the results of a geological interpretation using gravity, magnetic, and seismic data to understand the oil and gas potential of pre-Jurassic sedimentary intervals and basement in the central West Siberia basin. The 200 km long Pai-Khoi-Altai strike-slip zone was investigated. Reconstruction based on a data complex indicate the right-lateral kinematics of the principal strike-slip faults and possible fault inversion. The study evaluated the spatial and genetic relationship between the conditions for hydrocarbon trap development and the strike-slip fault systems, such as “flower structures”. Strike-slip geometry and kinematics are confirmed based on 2D and 3D seismic data. Geological and geophysical criteria are used to forecast localization of hydrocarbon fields. Predictive zones are elongated in several different directions and have a different distribution pattern in the blocks separated by principal strike-slip faults, confirming its significance as a controlling factor for the hydrocarbon potential of the region’s structures. Full article

High Temperature Superconducting (HTS) dynamo flux pumps are a promising alternative to metal current leads for energization and the persistent current mode operation of high current DC superconducting magnet systems for applications in rotating machines, such as Magnetic Resonance Imaging (MRI) or fusion systems. The viability of the flux pump concept has been widely proven by laboratory experiments and research is now in progress for the design and optimization of flux pump devices for practical applications. It has been widely established that the dependence of the critical current density (J_c) on the temperature (T), the magnetic field magnitude (B), and the orientation (θ), has a substantial impact on the overall DC voltage obtained at the terminals, as well as on the current limit and the loss of the flux pump. Since HTS tapes produced by different manufacturers, they show different dependencies of J_c with the amplitude and the orientation of the magnetic field. They also give rise to different outputs when employed in flux pumps. In this paper, we evaluate and compare the performance of several commercial HTS tapes used for flux pumping purposes through numerical simulation. We also investigate the dependence of the flux pump ‘s performance on the operating temperatures. A 2D finite element numerical model is first developed and validated against experimental data at 77 K. Afterward, the same HTS dynamo apparatus used for validation is exploited for the comparison. The J_c(B,θ,T) and $n$(B,θ,T) relations, which characterize each different tape in the model, are reconstructed via artificial intelligence techniques based on the open-access database of the Robinson Research Institute. It is shown in the paper that certain tapes are more suitable than others for flux pump applications and that the best overall operating temperature is in the vicinity of 77 K. Full article

As a fundamental part of ground penetrating radar (GPR) data processing, reverse time migration (RTM) can correctly position reflection waves and focusing diffraction waves on the proper spatial position. Least-squares reverse-time migration (LSRTM) is widely used in the seismic field for its ability to suppress artifacts and generate high-resolution images in comparison to conventional RTM. However, in the particular case of GPR detection methods, LSRTM is extremely susceptible to aliasing artifacts caused by under-sampling. In pursuit of enhanced precision in underground structure characterization, this paper presents the development of a new LSRTM based on modified total variation (MTV) regularization to improve imaging resolution. Initially, the objective function of LSRTM is derived by combining the Born approximation in 2-D transversal magnetic mode. Next, the adjoint equations and their gradients are solved using the Lagrange multiplier method. The objective function is then constrained by MTV regularization to ensure the precision and convergence of the LSRTM, which delivers a refined edge with reconstruction details. In the numerical experiments, in comparison to the conventional LSRTM method, the LSRTM-MTV algorithm demonstrated a 30.4% increase in computational speed and a 21.1% reduction in mean squared error (MSE). The outperformance of the proposed method is verified in detail through the image resolution and amplitude preservation in the test of synthetic data and laboratory data. Future research efforts will center on applying the proposed method to models featuring dispersive or anisotropic media that closely mimic real-world conditions and extending the application to various imaging techniques involving objective function minimization. Full article

Breast reconstruction is an essential component in the multidisciplinary management of breast cancer patients. Over the years, preoperative planning has played a pivotal role in assisting surgeons in planning operative decisions prior to the day of surgery. The evolution of preoperative planning can be traced back to the introduction of modalities such as ultrasound and colour duplex ultrasonography, enabling surgeons to evaluate the donor site’s vasculature and thereby plan operations more accurately. However, the limitations of these techniques paved the way for the implementation of modern three-dimensional imaging technologies. With the advancements in 3D imaging, including computed tomography and magnetic resonance imaging, surgeons gained the ability to obtain detailed anatomical information. Moreover, numerous adjuncts have been developed to aid in the planning process. The integration of 3D-printing technologies has made significant contributions, enabling surgeons to create complex haptic models of the underlying anatomy. Direct infrared thermography provides a non-invasive, visual assessment of abdominal wall vascular physiology. Additionally, augmented reality technologies are poised to reshape surgical planning by providing an immersive and interactive environment for surgeons to visualize and manipulate 3D reconstructions. Still, the future of preoperative planning in breast reconstruction holds immense promise. Most recently, artificial intelligence algorithms, utilising machine learning and deep learning techniques, have the potential to automate and enhance preoperative planning processes. This review provides a comprehensive assessment of the history of innovation in preoperative planning for breast reconstruction, while also outlining key future directions, and the impact of artificial intelligence in this field. Full article