This study investigates variations in radiofrequency- (RF-) induced energy absorption by orthopedic plates within the human body during 1.5T and 3T magnetic resonance imaging (MRI) scans, considering diverse postures. Using the poseable Duke model, we developed typical postures (O-posture, X-posture, Y-posture, and Z-posture) and placed anatomically correct representations of various orthopedic plates within these postures. Numerical simulations were conducted to evaluate electromagnetic fields and RF-induced energy absorption in these postures near orthopedic plates during MRI scans. Comparing RF-induced energy absorption (peak spatial averaged SAR over 1 g, pSAR1g) in postured models to the original posture reveals substantial variations. The pSAR1g differences for X-posture, Y-posture, and Z-posture reach 48%, 134%, and 32% at 1.5T, and 36%, 83%, and 101% at 3T, respectively. Changing posture can lead to higher or lower pSAR1g. These findings underscore the impact of patient posture on RF-induced energy absorption in orthopedic plates on the ulna bone. The study recommends considering representative body postures in future evaluations for MR conditional labeling of passive implants. Until then, maintaining a neutral posture during MR scans is advised to mitigate unforeseen RF-induced heating risks.
To derive accurate diffusion metrics, both imaging and diffusion-sensitizing gradient pulses should be accounted for when calculating the diffusion-weighted b-matrix. However, it is complex to derive analytical solutions due to complicated interactions between gradient pulses, including orthogonal directions. This study proposes a general framework to calculate the b-matrix automatically (dubbed as Auto-b). Based on the divide-and-conquer approach, the b-matrix calculation is appropriately segmented, and the symbolic mathematical library is applied to handle integration operations for each interval. If the specifications of all gradient pulses are provided to Auto-b, an accurate b-matrix can be obtained. Three examples are explored to validate the accuracy of Auto-b and to detect b-value errors when using approximate calculations. (1) In the conventional spin-echo example, Auto-b exhibits high accuracy, as indicated by the maximum relative deviation of 1.68‰ between its calculated b-matrices and those obtained from analytical expressions. (2) Auto-b is applied to investigate the contribution of imaging gradients to the b-matrix in an optimized spin-echo echo planar imaging sequence at submillimeter resolution. Specifically, ignoring the contribution of imaging gradients results in a b-value error of 12.16 s/mm2 at the 0.8 × 0.8 × 0.8 mm3 resolution and 22.47 s/mm2 at the 0.6 × 0.6 × 0.8 mm3 resolution, respectively, when nominal b = 0. (3) Auto-b is also utilized to analyze the influence of approximate calculations in the spatiotemporally encoded sequence. The results showed that neglecting the contribution of phase-encoding blips causes large b-value errors up to 11.02 s/mm2. In addition, the rectangularization of trapezoidal waveforms led to a high relative b-value error of 39.91%. This study validates the high accuracy of Auto-b and underscores the importance of accurate b-value calculations in both submillimeter imaging and spatiotemporally encoded sequences. Attributed to its automation, accuracy, and broad applicability, Auto-b is helpful for developers of diffusion sequences.
The utility of interleaved odd-number leg birdcage coils is demonstrated for decoupling in double- and triple-tuned multinuclear applications. The birdcage was designed to geometrically decouple from a planar double-tuned (1H-23Na) array and from a 31P saddle coil insert to create a triple-tuned configuration. Comparisons between an actively detuned coil and a purely geometrically decoupled architecture were used to demonstrate the capabilities of the design. In particular cases, the simplicity and adaptability of the interleaved nine-leg design for multinuclear nuclear magnetic resonance (NMR) offer a straightforward alternative to the often complex and lossy designs currently available for multinuclear birdcages and volume coils.
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.
As part of a homebuilt continuous wave electron paramagnetic resonance (EPR) spectrometer operating at 1.2 GHz, a magnet system for in vivo tooth dosimetry was developed. The magnet was designed by adopting NdFeB permanent magnet (PM) for the main magnetic field generation. For each pole of the magnet, 32 cylindrical PMs were arranged in 2 axially aligned ring arrays. The pole gap was 18 cm, which was wide enough for a human head breadth. The measured magnetic field was compared with the magnetic field distribution calculated in a finite element method (FEM) simulation. EPR spectra of intact human teeth irradiated 5 and 30 Gy were measured for the performance test with the developed magnet system and spectrometer. The measured mean magnetic flux density was estimated to be 44.45 mT with homogeneity of 1,600 ppm in a 2 cm diameter of the spherical volume of the XY plane, which was comparable to the FEM simulation results. The sweep coefficient of the magnetic field sweep coil was 0.35 mT per Ampere in both the measurement and FEM simulation. With ±9 A current, the sweep range was 5.7 mT, which was sufficiently wide to measure the tooth radiation-induced signal (RIS) and reference material. The peak-to-peak amplitude of the measured modulation field was 0.38 mT at the center of the magnet. With the developed magnet fully integrated into an EPR system, the EPR spectra of 5 and 30 Gy irradiated teeth were successfully acquired. The developed magnet system showed sufficiently acceptable performance in terms of magnetic flux density and homogeneity. The EPR spectrum of tooth RIS could be measured ex vivo . The RIS of 5 and 30 Gy irradiated teeth was clearly distinguishable from intact human teeth.
