scholarly journals Design of a Dual-Purpose Patch Antenna for Magnetic Resonance Imaging and Induced RF Heating for Small Animal Hyperthermia

2021 ◽  
Vol 11 (16) ◽  
pp. 7290
Author(s):  
Donghyuk Kim ◽  
Daniel Hernandez ◽  
Kyoung-Nam Kim
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Patch Antenna ◽  
Larmor Frequency ◽  
Magnetic Flux Density ◽  
Cancer Tissue ◽  
Patch Antennas ◽  
Rf Heating ◽  
Resonance Imaging ◽  
B1 Field

The popularity of patch antennas in magnetic resonance imaging (MRI) has reduced because of the large size required for patch antennae to resonate. Since the size of the patch antenna is associated with the wavelength and the wavelengths that are used in MRI are substantially large, large antennas are used. Methods of reducing patch antenna sizes have been proposed; however, these methods reduce the penetration depth and uniformity. In this study, we reduced the area of the patch antenna by 30% by folding the ground and patch planes in a zigzag pattern. The patch antenna produced two main resonant modes. The first mode produced a uniform magnetic field that was used for MRI. The second mode produced a strong and focused electric (|E|)-field, which was used for radiofrequency (RF) heating. Furthermore, we explored the use of a combination of two patch antennas aligned along the z-axis to provide a circular uniform magnetic flux density (|B1|) field at 300 MHz, which corresponds to the Larmor frequency in the 7T MRI system. In addition, the patch antenna configuration will be used for RF heating hyperthermia operating at 1.06 GHz. The target object was a small rat with insertion of colon cancer. Using the proposed configuration, we achieved |B1|-field uniformity with a standard deviation of 3% and a temperature increment of 1 °C in the mimic cancer tissue.

Magnetic Resonance Imaging Conditionally Safe Neurostimulation Leads

Neurosurgery ◽  
2013 ◽  
Vol 74 (2) ◽  
pp. 215-225 ◽  
Author(s):  
Robert J. Coffey ◽  
Ron Kalin ◽  
James M. Olsen
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Resonance ◽  
Animal Studies ◽  
Thermal Damage ◽  
Neurological Injury ◽  
Cns Injury ◽  
Thermal Dose ◽  
Rf Heating ◽  
Resonance Imaging ◽  
Increased Risk

Abstract BACKGROUND: Magnetic resonance imaging (MRI) is preferred for imaging the central nervous system (CNS). An important hazard for neurostimulation patients is heating at the electrode interface induced, for example, by 64-MHz radiofrequency (RF) magnetic fields of a 1.5T scanner. OBJECTIVE: We performed studies to define the thermal dose (time and temperature) that would not cause symptomatic neurological injury. METHODS: Approaches included animal studies where leads with temperature probes were implanted in the brain or spine of sheep and exposed to RF-induced temperatures of 37°C to 49°C for 30 minutes. Histopathological examinations were performed 7 days after recovery. We also reviewed the threshold for RF lesions in the CNS, and for CNS injury from cancer hyperthermia. Cumulative equivalent minutes at 43°C was used to normalize the data to exposure times and temperatures expected during MRI. RESULTS: Deep brain and spinal RF heating up to 43°C for 30 minutes produced indistinguishable effects compared with 37°C controls. Exposures greater than 43°C for 30 minutes produced temperature-dependent, localized thermal damage. These results are consistent with limits on hyperthermia exposure to 41.8°C for 60 minutes in patients who have cancer and with the reversibility of low-temperature and short-duration trial heating during RF lesion procedures. CONCLUSION: A safe temperature for induced lead heating is 43°C for 30 minutes. MRI-related RF heating above 43°C or longer than 30 minutes may be associated with increased risk of clinically evident thermal damage to neural structures immediately surrounding implanted leads. The establishment of a thermal dose limit is a first step toward making specific neurostimulation systems conditionally safe during MRI procedures.

scholarly journals Design and Testing of Radiofrequency Spherical four Coils

2016 ◽  
Vol 10 (5) ◽  
pp. 186 ◽  
Author(s):  
Sidi Mohamed Ahmed Ghaly ◽  
Khalid. A. Al-snaie ◽  
Sulaiman S. Al-Sowayan
Keyword(s):  
Magnetic Resonance Imaging ◽  
Magnetic Field ◽  
Magnetic Resonance ◽  
Spherical Surface ◽  
Resonance Imaging ◽  
Rf Systems ◽  
Magnetic Field Homogeneity ◽  
Field Homogeneity ◽  
B1 Field ◽  
Design And Testing

<p>In this paper, we present the design and testing of a radiofrequency prototype coil with good performances in terms of B<sub>1</sub> magnetic field homogeneity and can be utilized for Magnetic Resonance Imaging. It is constituted of four coaxial separately tuned rings of wire and symmetrically located on a spherical surface. Compared to standard Helmholtz pair, which has 2nd-order magnetic field homogeneity, it yields to improvement in field homogeneity, while preserving the simplicity of design. The four coils of proposed structure are tuned to the same frequency. The proposed structure gets at 4th-order magnetic field homogeneity by optimizing the distance between rings and the diameters of outer loops. An electrical modeling of the four-coil system taking into account the coupling effects between all rings permits to determine the resonance frequency in the homogenous mode. Measurements of B1 field homogeneity were introduced in free space. Compared to the Helmholtz coil, the proposed structure presents good performances in terms of B1 homogeneity, quality factor and sensitivity. The design of proposed coil has been optimized for best SNR performances. Globally, this work claims to be a contribution to the study of the four-coil RF systems derived from the Helmholtz pairs.</p>

scholarly journals Application of Deep Learning to Predict RF Heating of Cardiac Leads During Magnetic Resonance Imaging at 1.5 T and 3 T

2021 ◽  
Author(s):  
Xinlu Chen ◽  
Can Zheng ◽  
Bach Thanh Nguyen ◽  
Pia Sanpitak ◽  
Kelvin Chow ◽  
...  
Keyword(s):  
Magnetic Resonance Imaging ◽  
Machine Learning ◽  
Deep Learning ◽  
Magnetic Resonance ◽  
Ground Truth ◽  
Orthopedic Implants ◽  
Rf Heating ◽  
Rf Coil ◽  
Resonance Imaging ◽  
Electromagnetic Simulations

Abstract Purpose: Predicting magnetic resonance imaging (MRI)-induced heating of elongated conductive implants such as leads in cardiovascular implantable electronic devices (CIEDs) is essential to assessing patient safety. Phantom experiments and electromagnetic simulations have been traditionally used to estimate radiofrequency (RF) heating of implants, but they are notably time-consuming. Recently, machine learning has shown promise for fast prediction of RF heating of orthopedic implants, when the position of the implant within the MRI RF coil was predetermined. Here we explored whether deep learning could be applied to predict RF heating of conductive leads with variable positions/orientations during MRI at 1.5 T and 3 T.Methods: Models of 600 cardiac leads with clinically relevant trajectories were generated and electromagnetic simulations were performed to calculate the maximum of 1g-averaged SAR at the tips of lead models during MRI at 1.5 T and 3 T. Deep learning algorithms were trained to predict the maximum SAR at the lead’s tip from the knowledge of coordinates of points along the lead’s trajectory.Results: Despite the large range of SAR values (~200 W/kg-~3300 W/kg), the RMSE of predicted vs ground truth SAR remained at 223W/kg and 206 W/kg, with the R2 scores of 0.89 and 0.85 on the testing set for 1.5 T and 3 T models, respectively.Conclusion: Machine learning shows promise for fast assessment of RF heating of lead-like implants with only the knowledge of the lead’s geometry and MRI RF coil features.

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