A pathway towards a two-dimensional, bore-mounted, volume body coil concept for ultra high-field magnetic resonance imaging

Lack of a body-sized, bore-mounted, radiofrequency (RF) body coil for ultrahigh field (UHF) magnetic resonance imaging (MRI) is one of the major drawbacks of UHF, hampering the clinical potential of the technology. Transmit field (B₁ ) nonuniformity and low specific absorption rate (SAR) efficiencies in UHF MRI are two challenges to be overcome. To address these problems, and ultimately provide a pathway for the full clinical potential of the modality, we have designed and simulated two-dimensional cylindrical high-pass ladder (2D c-HPL) architectures for clinical bore-size dimensions, and demonstrated a simplified proof of concept with a head-sized prototype at 7 T. A new dispersion relation has been derived and electromagnetic simulations were used to verify coil modes. The coefficient of variation (CV) for brain, cerebellum, heart, and prostate tissues after B₁ ⁺ shimming in silico is reported and compared with previous works. Three prototypes were designed in simulation: a head-sized, body-sized, and long body-sized coil. The head-sized coil showed a CV of 12.3%, a B₁ ⁺ efficiency of 1.33 μT/√W, and a SAR efficiency of 2.14 μT/√(W/kg) for brain simulations. The body-sized 2D c-HPL coil was compared with same-sized transverse electromagnetic (TEM) and birdcage coils in silico with a four-port circularly polarized mode excitation. Improved B₁ ⁺ uniformity (26.9%) and SAR efficiency (16% and 50% better than birdcage and TEM coils, respectively) in spherical phantoms was observed. We achieved a CV of 12.3%, 4.9%, 16.7%, and 2.8% for the brain, cerebellum, heart, and prostate, respectively. Preliminary imaging results for the head-sized coil show good agreement between simulation and experiment. Extending the 1D birdcage coil concept to 2D c-HPLs provides improved B₁ ⁺ uniformity and SAR efficiency.

Deep Learning for Synthetic CT from Bone MRI in the Head and Neck

BACKGROUND AND PURPOSE

Bone MR imaging techniques enable visualization of cortical bone without the need for ionizing radiation. Automated conversion of bone MR imaging to synthetic CT is highly desirable for downstream image processing and eventual clinical adoption. Given the complex anatomy and pathology of the head and neck, deep learning models are ideally suited for learning such mapping.

MATERIALS AND METHODS

This was a retrospective study of 39 pediatric and adult patients with bone MR imaging and CT examinations of the head and neck. For each patient, MR imaging and CT data sets were spatially coregistered using multiple-point affine transformation. Paired MR imaging and CT slices were generated for model training, using 4-fold cross-validation. We trained 3 different encoder-decoder models: Light_U-Net (2 million parameters) and VGG-16 U-Net (29 million parameters) without and with transfer learning. Loss functions included mean absolute error, mean squared error, and a weighted average. Performance metrics included Pearson R, mean absolute error, mean squared error, bone precision, and bone recall. We investigated model generalizability by training and validating across different conditions.

RESULTS

The Light_U-Net architecture quantitatively outperformed VGG-16 models. Mean absolute error loss resulted in higher bone precision, while mean squared error yielded higher bone recall. Performance metrics decreased when using training data captured only in a different environment but increased when local training data were augmented with those from different hospitals, vendors, or MR imaging techniques.

CONCLUSIONS

We have optimized a robust deep learning model for conversion of bone MR imaging to synthetic CT, which shows good performance and generalizability when trained on different hospitals, vendors, and MR imaging techniques. This approach shows promise for facilitating downstream image processing and adoption into clinical practice.

Neuroimaging at 7 T: are we ready for clinical transition?

In the last 20 years, ultra-high field (UHF) magnetic resonance imaging (MRI) has become an outstanding research tool for the study of the human brain, with 90 of these scanners installed today, worldwide. The recent clearances from regulatory bodies in the USA and Europe to 7-T clinical systems have set the ground for a transition from pure research applications to research and clinical use of these systems. As today, UFH neuroimaging is demonstrating clinical value and, given the importance of this topic for both preclinical scientists and clinical neuroradiologists, European Radiology Experimental is launching a thematic series entitled "7-T neuro MRI: from research to clinic", consisting of peer-reviewed articles, invited or spontaneously submitted, on topics selected by the guest editors, describing the state of the art of UHF MRI neuroimaging across different pathologies, as well as related clinical applications. In this editorial, we discuss some of the challenges related to the clinical use of 7-T scanners and the strengths and weaknesses of clinical imaging at UHF.

Advanced Magnetic Resonance Imaging of the Skull Base

Magnetic resonance (MR) imaging is a crucial tool for evaluation of the skull base, enabling characterization of complex anatomy by utilizing multiple image contrasts. Recent technical MR advances have greatly enhanced radiologists' capability to diagnose skull base pathology and help direct management. In this paper, we will summarize cutting-edge clinical and emerging research MR techniques for the skull base, including high-resolution, phase-contrast, diffusion, perfusion, vascular, zero echo-time, elastography, spectroscopy, chemical exchange saturation transfer, PET/MR, ultra-high-field, and 3D visualization. For each imaging technique, we provide a high-level summary of underlying technical principles accompanied by relevant literature review and clinical imaging examples.

Zero TE MRI for Craniofacial Bone Imaging

Zero TE MR imaging is a novel technique that achieves a near-zero time interval between radiofrequency excitation and data acquisition, enabling visualization of short-T2 materials such as cortical bone. Zero TE offers a promising radiation-free alternative to CT with rapid, high-resolution, silent, and artifact-resistant imaging, as well as the potential for "pseudoCT" reconstructions. In this report, we will discuss our preliminary experience with zero TE, including technical principles and a clinical case series demonstrating emerging applications in neuroradiology.

Radiofrequency magnetic resonance coils and communication antennas: Simulation and design strategies

Coils simulation and design is a fundamental task to maximize Signal-to-Noise Ratio in Magnetic Resonance applications. In the meantime, in the last years the issue of accurate communication antennas analysis has grown. Coil design techniques take advantage of computer simulations in dependence on the magnetic field wavelength and coil sizes. In particular, since at high frequencies coils start to behave as antennas, modern Magnetic Resonance coil development exploits numerical methods typically employed for antennas simulation. This paper reviews coil and antenna performance parameters and focuses on the different simulation approaches in dependence on the near/far field zones and operating frequency.

Spiral MR fingerprinting at 7T with simultaneous B1 estimation

Magnetic resonance fingerprinting is an efficient, new approach for quantitative imaging with MR. We aimed to extend this technique to cases with B1+ inhomogeneities within the imaging volume. Previous approaches have used abrupt changes in flip angles to estimate the B1+ field simultaneously with T1 and T2, using a Cartesian approach in a small-animal scanner at 4.7T. Here, we evaluated whether a similar approach would be suitable for imaging human brains using spiral readouts with a 7T scanner. We found that our modified scheme could significantly reduce the adverse effects of B1+ inhomogeneities even in extreme cases, reducing both the bias and the variance in T2 estimations by an order of magnitude when compared to literature methods. Acquisitions used less than 1.5W/kg SAR and could be performed in 12s per slice. In conclusion, our approach can be used to perform quantitative imaging of the brain at 7T in a short time, simultaneously estimating the B1+ profile.