Sensitivity enhancement of traveling wave MRI using free local resonators: an experimental demonstration

Detunable wireless Litzcage coil for human head MRI at 1.5 T

Inductively coupled radiofrequency (RF) coils are an inexpensive and simple method to realize wireless RF coils in magnetic resonance imaging (MRI), which can significantly ease the MRI scan setup and improve patient comfort because they do not require bulky components such as cables, baluns, preamplifiers, and connectors. However, volume-type wireless coils are typically operated in transmit/receive mode because detuning such coils is much more challenging due to their complex structure and multiple resonant modes. Meanwhile, adding too many detuning circuits to a wireless coil would decrease the coil's quality factor, impair the signal-to-noise ratio, and increase the cost. In this work, we proposed, constructed, and tested a novel wireless volume coil based on the Litzcage design for 1.5-T head imaging. Being an inductively coupled coil, it has a much simpler structure, resulting in a lighter weight and less bulky design. Despite its simpler structure, it exhibits comparable imaging performance with a commercial receive array, providing an alternative to conventional wired coils with a high cost and complex structure. The unique figure-of-8 conductor pattern within the rungs ensures that the proposed wireless Litzcage can be efficiently detuned with minimal detuning circuits.

Conductor Losses in Radiofrequency Coils for Magnetic Resonance below 3T: Estimation Methods and Minimization Strategies

The design of optimized radiofrequency (RF) coils is a fundamental task for maximizing the signal-to-noise ratio (SNR) in Magnetic Resonance Imaging (MRI) and Magnetic Resonance Spectroscopy (MRS) applications. An efficient coil should be designed by minimizing the coil noise with respect to the sample noise, since coil conductor resistance affects data quality by reducing the SNR, especially for coils tuned to a low frequency. Such conductor losses strongly depend on the frequency (due to the skin effect) and on the conductor cross-sectional shape (strip or wire). This paper reviews the different methods for estimating conductor losses in RF coils for MRI/MRS applications, comprising analytical formulations, theoretical/experimental hybrid approaches and full-wave simulations. Moreover, the different strategies for minimizing such losses, including the use of Litz wire, cooled and superconducting coils, are described. Finally, recent emerging technologies in RF coil design are briefly reviewed.

Low-cost inductively coupled stacked wireless RF coil for MRI at 3 T

Inductively coupled RF coils are an inexpensive and simple method to realize wireless RF coils in MRI. They are low cost and can greatly ease the MR scan setup and improve patient comfort, since they do not require bulky components such as cables, baluns, preamplifiers, and connectors. Previous works have typically used single-layer loops as wireless coils. In this work, we present a novel wireless coil, where two loops are stacked and decoupled with a shared capacitor. We found that such a stacked structure could increase the coil efficiency and SNR. Compared with the single-layer wireless coil, both electromagnetic simulation and MR experiment results demonstrate that the stacked wireless coil has a considerable SNR improvement of approximately 35%.

Evaluation of magnetic resonance image segmentation in brain low-grade gliomas using support vector machine and convolutional neural network

BACKGROUND

Image segmentation of brain low-grade glioma (LGG) magnetic resonance imaging (MRI) contributes tremendously to diagnosis, classification and treatment of the disease. A tangible, accurate, reliable and fast image segmentation technique is demanded in clinical diagnosis and research.

METHODS

The emerging machine learning technique has been demonstrated its unique capability in the field of medical image processing, including medical image segmentation. Support vector machine (SVM) and convolutional neural network (CNN) are two widely used machine learning methods. In this work, image segmentation tools based on SVM and CNN are developed and evaluated for brain LGG MR image segmentation studies. The segmentation performance in terms of accuracy and cost is quantitatively analyzed and compared between the SVM and CNN techniques developed.

RESULTS

Computed on the Google CoLab, each of the 109 SVM models represents an individual patient, is trained using a single image of that patient and takes a few seconds to complete. The CNN model is trained on a drastically larger dataset of 19,760 data augmented images and takes approximately 2 hours to obtain the most optimal result. The SVM models achieved an average and median accuracy of 0.937 and 0.976 respectively, precision of 0.456 and 0.535 respectively, recall of 0.878 and 0.906 respectively, and F1 score of 0.546 and 0.662 respectively. Although the CNN model required a significantly longer calculation time, it surpassed the SVM models in performance in LGG MR image segmentation, achieving an accuracy of 0.998, a precision of 0.999, a recall of 0.999 and an F1 score of 0.999.

CONCLUSIONS

This study shows that SVM with appropriate filtering techniques is capable of obtaining reliable and fast segmentation of brain LGG MR images with sufficient accuracy and limited image data. CNN technique outperforms SVM in the accuracy of segmentation with requirements of significantly enlarged data set, long computation time and high-performance computer.

Signal Sensitivity Enhancement of High-Spatial-Resolution MR Imaging with a Concatenated Cylindrical Parametric RF-Resonator

Phased array MRI coils can increase sensitivity of superficial tissues owing to their proximity to the detection region. Deep-lying tissues, on the other hand, do not benefit to the same degree. Here we investigate the use of a localized cylindrically symmetric quadruple frequency resonator concatenated with a double frequency resonator to increase the longitudinal field-of-view (FOV) without compromising the spatial-resolution and detection sensitivity. These concatenated array coils work on the principle of a parametric amplification to provide wireless amplification of the locally detected NMR signal prior to inductively coupling the coil to an external pick-up loop with connection to the system receiver. When both the detectors are activated together, the effective range of both overlay to create a larger FOV enabling better identification of detectable regions. Furthermore, the in-vivo test of the concatenated detector provides a worst-case 5-fold SNR gain in regions separated from the cylindrical surface larger than its own diameter. This proposed approach of concatenated detector realization can be individually activated and manipulated to enlarge the sensitivity-enhanced region without sacrificing their individual performance. Compared to double frequency detectors, quadruple frequency detectors offer more flexibility in the choice of detector dimension, enabling multi-element concatenation over an extended FOV.

Improved traveling-wave efficiency in 7T human MRI using passive local loop and dipole arrays

Traveling-wave MRI, which uses relatively small and simple RF antennae, has robust matching performance and capability for large field-of-view (FOV) imaging. However, the power efficiency of traveling-wave MRI is much lower than conventional methods, which limits its application. One simple approach to improve the power efficiency is to place passive resonators around the subject being imaged. The feasibility of this approach has been demonstrated in previous works using a single small resonant loop. In this work, we aim to explore how much the improvements can be maintained in human imaging using an array design, and whether electric dipoles can be used as local elements. First, a series of electromagnetic (EM) simulations were performed on a human model. Then RF coils were constructed and the simulation results using the best setup for head imaging were validated in MR experiments. By using the passive local loop and transverse dipole arrays, respectively, the transmit efficiency (B₁⁺) of traveling-wave MRI can be improved by 3-fold in the brain and 2-fold in the knee. The types of passive elements (loops or dipoles) should be carefully chosen for brain or knee imaging to maximize the improvement, and the enhancement depends on the local body configuration.