Concepts in Magnetic Resonance Part B, Magnetic Resonance Engineering
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Development of Electron Paramagnetic Resonance Magnet System for In Vivo Tooth Dosimetry

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Concepts in Magnetic Resonance Part B publishes research concerning the design and development of hardware and software employed in magnetic resonance techniques, including magnetic resonance imaging (MRI) and nuclear magnetic resonance (NMR).

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Concepts in Magnetic Resonance Part B maintains an Editorial Board of practicing researchers from around the world, to ensure manuscripts are handled by editors expert and up-to-date in the field of study.

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Research Article

Diagnosis of Alzheimer’s Disease with Extreme Learning Machine on Whole-Brain Functional Connectivity

The analysis of human brain fMRI subjects can research neuro-related diseases and explore the related rules of human brain activity. In this paper, we proposed an algorithm framework to analyze the functional connectivity network of the whole brain and to distinguish Alzheimer’s disease (AD), mild cognitive impairment (MCI), and cognitively normal (CN). In other studies, they use algorithms to select features or extract abstract features, or even manually select features based on prior information. Then, a classifier is constructed to classify the selected features. We designed a concise algorithm framework that uses whole-brain functional connectivity for classification without feature selection. The algorithm framework is a two-hidden-layer neural network based on extreme learning machine (ELM), which overcomes the instability of classical ELM in high-dimensional data scenarios. We use this method to conduct experiments for AD, MCI, and CN data and perform 10-fold cross-validation. We found that it has several advantages: (1) the proposed method has excellent classification accuracy with high speed. The classification accuracy of AD vs. CN is 96.85%, and the accuracy of MCI vs. CN is 95.05%. Their AUC (area under curve) of ROC (receiver operating characteristic curve) reached 0.9891 and 0.9888, respectively. Their sensitivities are 97.1% and 94.7%, and specificities are 96.3% and 95.3%, respectively. (2) Compared with other studies, the proposed method is concise. Construction of a two-hidden-layer neural network is to learn features of the whole brain for the diagnosis of AD and MCI, without the feature screening. It avoids the negative effects of feature screening by algorithm or prior information. (3) The proposed method is suitable for small sample and high-dimensional data. It meets the requirements of medical image analysis. In other studies, its classifiers usually deal with several to dozens of feature dimensions. The proposed method deals with 4005 feature dimensions.

Research Article

Study of Correlation between MRI Morphology of Primary Tumor and Extramural Vascular Invasion in Rectal Cancer

Introduction. In clinical settings, nodular projection (NP) and cord sign (CS) at the tumor edge and irregular nodules (IN) in the mesorectum often appeared together with extramural vascular invasion (EMVI). We aim to evaluate the diagnostic efficiency of the MRI morphology of primary tumor in predicting EMVI in patients with rectal cancer (RC). Methods. This retrospective study included 156 patients with RC. Clinical and imaging factors including NP at the primary tumor’s edge, CS at the primary tumor’s edge, maximal extramural depth (EMD), IN in the mesorectum, growth pattern, tumor length, range of rectal wall invaded (RRWI) by tumor, peritoneal reflex invasion by surgery, and pathology-proven local node involvement (PLN) were evaluated. Then, ROC curve was drawn to depict the meaningful indicators in multivariate analyses. Results. There were 53 (34%) patients with pathological extramural venous invasion (pEMVI). Among the clinical and imaging factors evaluated, NP, CS, IN, EMD, PLN, differentiation, and peritoneal reflex invasion were significantly associated with pEMVI. NP and PLN were independent predictors of EMVI. Areas under the ROC curve (AUC) of NP for prediction of EMVI was 0.82 (95% CI, 0.74–0.90), with a sensitivity of 73.58%, a specificity of 90.29%, a positive predictive value (PPV) of 75.59%, a negative predictive value (NPV) of 86.92%, and an accuracy of 84.62%, respectively. Conclusions. Patients with primary tumor with EMVI usually showed NP and CS. NP was an independent predictor of EMVI and helpful for the diagnosis of EMVI in RC patients.

Research Article

Study of Endogenous Paramagnetic Centers in Biological Systems from Different Areas

Plant leaves (Eldar pine (Pinus eldarica M.), fig (Ficus carica L.), and olive (Olea europaea L.)), collected in territories with different ecological conditions, of the Absheron Peninsula (Azerbaijan Republic) were studied by electron paramagnetic resonance spectroscopy (EPR). The generation of nanophase iron oxide magnetic particles in biological systems under the influence of stress factors was revealed. It was found that the process of biomineralization plays a role in the formation of biogenic iron oxide magnetic nanoparticles in plants and the generation of magnetite crystals in biological tissues, and stress factors have a stimulating effect on this phenomenon.

Research Article

Electromagnetic Simulation of Signal Distribution of Various RF Endoluminal Loop Geometries with Coil Orientation: Towards a Reconfigurable Design

With the objective of improving MR endoluminal imaging of the colonic wall, electromagnetic simulations of different configurations of single-layer and double-layer, and double-turn endoluminal coil geometries were run. Indeed, during colon navigation, variations in coil orientation with respect to B0 are bound to occur, leading to impaired image acquisition due to a loss of signal uniformity. In this work, three typical coil orientations encountered during navigation were chosen and the resulting signal uniformity of the different geometries was investigated through the simulated values. Sampling this quantity over a circle of radius r enabled us to calculate the coefficient of variation (= standard deviation/mean) for this given distance. This procedure was repeated for  mm, which represents the region of interest in the colon. Our results show that single-loop and double-layer geometries could provide complementary solutions for improved signal uniformity. Finally, using four microelectromechanical system switches, we proposed the design of a reconfigurable endoluminal coil able to switch between those two geometries while also ensuring the active decoupling of the endoluminal coil during the RF transmission of an MR experiment.

Research Article

Novel Method to Improve the Uniformity of 7T Body MR Images

When using ultrahigh-field MR systems (7T), the variations in the RF magnetic field can lead to significant loss in image uniformity. To optimize the overall MR image quality, the image region is divided into multiple smaller regions of interest (the ROIs), which can be independently optimized using transmit array optimization techniques including RF shimming, to improve RF magnetic fields and image intensity. Electromagnetic numerical simulations and corresponding transverse magnetization (|Mt|) acquired using the Bloch equation-based MRI simulator are used to evaluate the proposed method. Compared to the simulation results of quadrature driving method, mean and standard deviation (SD) of |Mt| in the full image (an inner diameter of 500 mm) were improved 47% (mean) and 48% (SD), whereas 94% (max) and 97% (mean) improved in the unaveraged SAR using the proposed method. The uniformity of |Mt| acquired using the method was especially improved in the peripheral region of the selected phantom image compared to that of other methods. The proposed method using multiple independently optimized ROIs and numerical simulations significantly improved the uniformity of |Mt| body images at 7T. This technique would be generally applicable to any high-field strength MR systems, which generate short RF wavelengths compared to the field of view.

Research Article

Quasistatic Solutions versus Full-Wave Solutions of Single-Channel Circular RF Receive Coils on Phantoms of Varying Conductivities at 3 Tesla

Purpose. Although full-wave simulations could be used to aid in RF coil design, the algorithms may be too slow for an iterative optimization algorithm. If quasistatic simulations are accurate within the design tolerance, then their use could reduce simulation time by orders of magnitude compared to full-wave simulations. This paper examines the accuracy of quasistatic and full-wave simulations at 3 Tesla. Methods. Three sets of eight coils ranging from 3–10 cm (24 total) were used to measure SNR on three phantoms with conductivities of 0.3, 0.6, and 0.9 S/m. The phantom conductivities were chosen to represent those typically found in human tissues. The range of coil element sizes represents the sizes of coil elements seen in typical coil designs. SNR was determined using the magnetic and electric fields calculated by quasistatic and full-wave simulations. Each simulated SNR dataset was scaled to minimize the root mean squared error (RMSE) when compared against measured SNR data. In addition, the noise values calculated by each simulation were compared against benchtop measured noise values. Results. The RMSE was 0.285 and 0.087 for the quasistatic and full-wave simulations, respectively. The maximum and minimum quotient values, when taking the ratio of simulated to measured SNR values, were 1.69 and 0.20 for the quasistatic simulations and 1.29 and 0.75 for the full-wave simulations, respectively. The ratio ranges, for the calculated quasistatic and full-wave total noise values compared to benchtop measured noise values, were 0.83–1.06 and 0.27–3.02, respectively. Conclusions. Full-wave simulations were on average 3x more accurate than the quasistatic simulations. Full-wave simulations were more accurate in characterizing the wave effects within the sample, though they were not able to fully account for the skin effect when calculating coil noise.

Concepts in Magnetic Resonance Part B, Magnetic Resonance Engineering
Publishing Collaboration
More info
Wiley Hindawi logo
 Journal metrics
See full report
Acceptance rate-
Submission to final decision-
Acceptance to publication-
CiteScore0.780
Journal Citation Indicator0.220
Impact Factor1.200
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