Ultra-high field MRI: parallel-transmit arrays and RF pulse design

Exploring In Vivo Human Brain Metabolism at 10.5 T: Initial Insights from MR Spectroscopic Imaging

INTRODUCTION

Ultra-high-field magnetic resonance (MR) systems (7 T and 9.4 T) offer the ability to probe human brain metabolism with enhanced precision. Here, we present the preliminary findings from 3D MR spectroscopic imaging (MRSI) of the human brain conducted with the world's first 10.5 T whole-body MR system.

METHODS

Employing a custom-built 16-channel transmit and 80-channel receive MR coil at 10.5 T, we conducted MRSI acquisitions in six healthy volunteers to map metabolic compounds in the human cerebrum in vivo. Three MRSI protocols with different matrix sizes and scan times (4.4×4.4×4.4 mm³: 10 min, 3.4×3.4×3.4 mm³: 15 min, and 2.75×2.75×2.75 mm³: 25 min) were tested. Concentric ring trajectories were utilized for time-efficient encoding of a spherical 3D k-space with ∼4 kHz spectral bandwidth. B0/B1 shimming was performed based on respective field mapping sequences and anatomical T1-weighted MRI were obtained.

RESULTS

By combining the benefits of an ultra-high-field system with the advantages of free-induction-decay (FID-)MRSI, we present the first metabolic maps acquired at 10.5 T in the healthy human brain at both high (voxel size of 4.4³ mm³) and ultra-high (voxel size of 2.75³ mm³) isotropic spatial resolutions. Maps of 13 metabolic compounds (aspartate, choline compounds and creatine + phosphocreatine, γ-aminobutyric acid (GABA), glucose, glutamine, glutamate, glutathione, myo-inositol, scyllo-inositol, N-acetylaspartate (NAA), N-acetylaspartylglutamate (NAAG), taurine) and macromolecules were obtained individually. The spectral quality was outstanding in the parietal and occipital lobes, but lower in other brain regions such as the temporal and frontal lobes. The average total NAA (tNAA = NAA + NAAG) signal-to-noise ratio over the whole volume of interest was 12.1±8.9 and the full width at half maximum of tNAA was 24.7±9.6 Hz for the 2.75×2.75×2.75 mm³ resolution. The need for an increased spectral bandwidth in combination with spatio-spectral encoding imposed significant challenges on the gradient system, but the FID approach proved very robust to field inhomogeneities of ∆B0 = 45±38 Hz (frequency offset ± spatial STD) and B1+ = 65±11° within the MRSI volume of interest.

DISCUSSION

These preliminary findings highlight the potential of 10.5 T MRSI as a powerful imaging tool for probing cerebral metabolism. By providing unprecedented spatial and spectral resolution, this technology could offer a unique view into the metabolic intricacies of the human brain, but further technical developments will be necessary to optimize data quality and fully leverage the capabilities of 10.5 T MRSI.

How to Implement Clinical 7T MRI-Practical Considerations and Experience with Ultra-High-Field MRI

The implementation of clinical 7T MRI presents both opportunities and challenges for advanced medical imaging. This tutorial provides practical considerations and experiences with 7T MRI in clinical settings. We first explore the history and evolution of MRI technology, highlighting the benefits of increased signal-to-noise ratio (SNR), contrast-to-noise ratio (CNR), and susceptibility at 7T. Technical challenges such as increased susceptibility artifacts and RF inhomogeneity are also discussed, along with innovative adaptations. This review also discusses hardware and software considerations, including new parallel transmission head coils and advanced image processing techniques to optimize image quality. Safety considerations, such as managing tissue heating and susceptibility to artifacts, are also discussed. Additionally, clinical applications of 7T MRI are examined, focusing on neurological conditions such as epilepsy, multiple sclerosis, and vascular imaging. Emerging trends in the use of 7T MRI for spectroscopy, perfusion imaging, and multinuclear imaging are explored, with insights into the future of ultra-high-field MRI in clinical practice. This review aims to provide clinicians, technologists, and researchers with a roadmap for successfully implementing 7T MRI in both research and clinical environments.

Deep learning methods for 3D magnetic resonance image denoising, bias field and motion artifact correction: a comprehensive review

Magnetic resonance imaging (MRI) provides detailed structural information of the internal body organs and soft tissue regions of a patient in clinical diagnosis for disease detection, localization, and progress monitoring. MRI scanner hardware manufacturers incorporate various post-acquisition image-processing techniques into the scanner's computer software tools for different post-processing tasks. These tools provide a final image of adequate quality and essential features for accurate clinical reporting and predictive interpretation for better treatment planning. Different post-acquisition image-processing tasks for MRI quality enhancement include noise removal, motion artifact reduction, magnetic bias field correction, and eddy electric current effect removal. Recently, deep learning (DL) methods have shown great success in many research fields, including image and video applications. DL-based data-driven feature-learning approaches have great potential for MR image denoising and image-quality-degrading artifact correction. Recent studies have demonstrated significant improvements in image-analysis tasks using DL-based convolutional neural network techniques. The promising capabilities and performance of DL techniques in various problem-solving domains have motivated researchers to adapt DL methods to medical image analysis and quality enhancement tasks. This paper presents a comprehensive review of DL-based state-of-the-art MRI quality enhancement and artifact removal methods for regenerating high-quality images while preserving essential anatomical and physiological feature maps without destroying important image information. Existing research gaps and future directions have also been provided by highlighting potential research areas for future developments, along with their importance and advantages in medical imaging.

An array of paired folded-end dipoles for whole-brain imaging at 9.4 T

PURPOSE

To improve transmit B1+ field homogeneity and longitudinal coverage of a human head RF array, we developed a novel eight-element transceiver (TxRx) array using composite elements based on paired folded-end dipoles.

METHODS

The developed array consisted of eight pairs of coupled folded-end dipoles. Only one dipole in each pair was driven during transmission, while the other was passively coupled with the active one. The distribution of the transmit B1+ field was numerically optimized by changing the overlap between the dipoles and the value of the reactive lumped element placed in the middle of the passive dipole.

RESULTS

The proposed array of paired folded-end dipoles substantially improved the B1+ homogeneity and longitudinal coverage over the entire brain including the brain stem compared to a single-row folded-end dipole array. The improved whole brain coverage was demonstrated both numerically and experimentally.

CONCLUSION

As a proof of concept, we developed and characterized both numerically and experimentally a prototype of a single-row eight-element 9.4 T array for human brain imaging consisting of composite array elements based on paired passively-coupled folded-end dipoles. The array improved the transmit magnetic field distribution due to the laterally elongated field pattern created by one active and one passive dipole per channel. As a result, the provided coverage was substantially better than that of an 8-element dipole array consisting of long folded-end dipoles. For the first time, an image of the entire human brain at 9.4 T, covering the brain stem up to the fourth vertebra, was obtained using a simple single row eight-element array.

A 128-channel receive array with enhanced SNR performance for 10.5 tesla brain imaging

Purpose

To develop and characterize the performance of a 128-channel head array for brain imaging at 10.5 tesla and evaluate the potential of brain imaging at this unique, >10 tesla magnetic field.

Methods

The coil is composed of a 16-channel self-decoupled loop transmit/receive array with a 112-loop receive-only (Rx) insert. Interactions between the outer transmitter and the inner 112Rx insert were mitigated using coaxial cable traps placed every 1/16 of a wavelength on each feed cable, locating most preamplifier boards outside the transmitter field and miniaturizing those placed directly on individual coils.

Results

The 128-channel array described herein achieved 77% of ultimate intrinsic SNR in the center of the brain. Transmit field maps obtained experimentally on a phantom with and without the receive array were similar and matched EM simulations, leading to FDA approval for human imaging. Anatomical and functional data, including with power demanding sequences, were acquired successfully on human volunteers.

Conclusions

Counterintuitive to expectations based on magnetic fields ≤7T, the higher channel counts provided SNR gains centrally, capturing ∼80% uiSNR. Fraction of uiSNR achieved centrally in 64Rx, 80Rx, and 128Rx arrays suggested that a plateau was being reached at 80%. At this plateau, linear to approximately quadratic B 0 dependent SNR gains for the periphery and the center, respectively, were observed for 10.5T relative 7T.

RF coil design strategies for improving SNR at the ultrahigh magnetic field of 10.5 Tesla

Purpose

To develop multichannel transmit and receive arrays towards capturing the ultimate-intrinsic-SNR (uiSNR) at 10.5 Tesla (T) and to demonstrate the feasibility and potential of whole-brain, high-resolution human brain imaging at this high field strength.

Methods

A dual row 16-channel self-decoupled transmit (Tx) array was converted to a 16Tx/Rx transceiver using custom transmit/receive switches. A 64-channel receive-only (64Rx) array was built to fit into the 16Tx/Rx array. Electromagnetic modeling and experiments were employed to define safe operation limits of the resulting 16Tx/80Rx array and obtain FDA approval for human use.

Results

The 64Rx array alone captured approximately 50% of the central uiSNR at 10.5T while the identical 7T 64Rx array captured ∼76% of uiSNR at this lower field strength. The 16Tx/80Rx configuration brought the fraction of uiSNR captured at 10.5T to levels comparable to the performance of the 64Rx array at 7T. SNR data obtained at the two field strengths with these arrays displayed dependent increases over a large central region. Whole-brain high resolution T 2 * and T 1 weighted anatomical and gradient-recalled echo EPI BOLD fMRI images were obtained at 10.5T for the first time with such an advanced array, illustrating the promise of >10T fields in studying the human brain.

Conclusion

We demonstrated the ability to approach the uiSNR at 10.5T over the human brain with a novel, high channel count array, achieving large SNR gains over 7T, currently the most commonly employed ultrahigh field platform, and demonstrate high resolution and high contrast anatomical and functional imaging at 10.5T.

A Review of Parallel Transmit Arrays for Ultra-High Field MR Imaging

Parallel transmission (pTX) techniques are required to tackle a number of challenges, e.g., the inhomogeneous distribution of the transmit field and elevated specific absorption rate (SAR), in ultra-high field (UHF) MR imaging. Additionally, they offer multiple degrees of freedom to create temporally- and spatially-tailored transverse magnetization. Given the increasing availability of MRI systems at 7 T and above, it is anticipated that interest in pTX applications will grow accordingly. One of the key components in MR systems capable of pTX is the design of the transmit array, as this has a major impact on performance in terms of power requirements, SAR and RF pulse design. While several reviews on pTX pulse design and the clinical applicability of UHF exist, there is currently no systematic review of pTX transmit/transceiver coils and their associated performance. In this article, we analyze transmit array concepts to determine the strengths and weaknesses of different types of design. We systematically review the different types of individual antennas employed for UHF, their combination into pTX arrays, and methods to decouple the individual elements. We also reiterate figures-of-merit (FoMs) frequently employed to describe the performance of pTX arrays and summarize published array designs in terms of these FoMs.

Exploring the Intersection of Geophysics and Diagnostic Imaging in the Health Sciences

To develop diagnostic imaging approaches, this paper emphasizes the transformational potential of merging geophysics with health sciences. Diagnostic imaging technology improvements have transformed the health sciences by enabling earlier and more precise disease identification, individualized therapy, and improved patient care. This review article examines the connection between geophysics and diagnostic imaging in the field of health sciences. Geophysics, which is typically used to explore Earth's subsurface, has provided new uses of its methodology in the medical field, providing innovative solutions to pressing medical problems. The article examines the different geophysical techniques like electrical imaging, seismic imaging, and geophysics and their corresponding imaging techniques used in health sciences like tomography, magnetic resonance imaging, ultrasound imaging, etc. The examination includes the description, similarities, differences, and challenges associated with these techniques and how modified geophysical techniques can be used in imaging methods in health sciences. Examining the progression of each method from geophysics to medical imaging and its contributions to illness diagnosis, treatment planning, and monitoring are highlighted. Also, the utilization of geophysical data analysis techniques like signal processing and inversion techniques in image processing in health sciences has been briefly explained, along with different mathematical and computational tools in geophysics and how they can be implemented for image processing in health sciences. The key findings include the development of machine learning and artificial intelligence in geophysics-driven medical imaging, demonstrating the revolutionary effects of data-driven methods on precision, speed, and predictive modeling.