Dark band artifact in transcranial MR-guided focused ultrasound: Mechanism and mitigation with passive crossed wire antennas

Current FDA-approved transcranial MR-guided focused ultrasound (tcMRgFUS) transducers cause a curved dark band in 3 T brain images that runs through midbrain targets of ablative treatments for essential tremor and other applications, and signal is reduced by at least 25% elsewhere in the brain. This limits the set of scans that can be performed to guide and assess the effects of treatment. An electromagnetic simulation study was performed to elucidate the mechanisms causing the dark band. Based on the results, a pair of passive antennas in a "propeller-beanie" configuration were designed to manipulate the reflected waves to avoid signal cancellation within the brain. The antennas were optimized and validated with in-vivo experiments and hydrophone measurements. The simulation study revealed that the dark band is caused by RF waves reflected from the transducer's ground plane, which cancel with incoming waves from the scanner's body coil. The passive antennas shifted the dark band out of the brain and increased transmit efficiency in the center of brain 2.3 times while improving field homogeneity by 50%. They also increased receive sensitivity and SNR in anatomic and temperature imaging. They caused no detectable distortion in hydrophone-measured focal pressure profiles. The conductive ground planes and coupling media used in tcMRgFUS and other piezoelectric FUS transducers interact with a 3 T scanner's RF fields to reduce transmit efficiency and SNR. For tcMRgFUS scenario, "propeller beanie" passive reflecting antennas alleviated these effects. This could make a broader set of imaging sequences available to guide tcMRgFUS treatment.

Correcting time-intensity curves in dynamic contrast-enhanced breast MRI for inhomogeneous excitation fields at 7T

Purpose

Inhomogeneous excitation at ultrahigh field strengths (7T and above) compromises the reliability of quantified dynamic contrast‐enhanced breast MRI. This can hamper the introduction of ultrahigh field MRI into the clinic. Compensation for this non‐uniformity effect can consist of both hardware improvements and post‐acquisition corrections. This paper investigated the correctable radiofrequency transmit (B1+) range post‐acquisition in both simulations and patient data for 7T MRI.

Methods

Simulations were conducted to determine the minimum B1+ level at which corrections were still beneficial because of noise amplification. Two correction strategies leading to differences in noise amplification were tested. The effect of the corrections on a 7T patient data set (N = 38) with a wide range of B1+ levels was investigated in terms of time‐intensity curve types as well as washin, washout and peak enhancement values.

Results

In simulations assuming a common amount of T₁ saturation, the lowest B1+ level at which the SNR of the corrected images was at least that of the original precontrast image was 43% of the nominal angle. After correction, time‐intensity curve types changed in 24% of included patients, and the distribution of curve types corresponded better to the distribution found in literature. Additionally, the overlap between the distributions of washin, washout, and peak enhancement values for grade 1 and grade 2 tumors was slightly reduced.

Conclusion

Although the correctable range varies with the amount of T₁ saturation, post‐acquisition correction for inhomogeneous excitation was feasible down to B1+ levels of 43% of the nominal angle in vivo.

Intensity Inhomogeneity Correction of Magnetic Resonance Images using Patches

This paper presents a patch-based non-parametric approach to the correction of intensity inhomogeneity from magnetic resonance (MR) images of the human brain. During image acquisition, the inhomogeneity present in the radio-frequency coil, is usually manifested on the reconstructed MR image as a smooth shading effect. This artifact can significantly deteriorate the performance of any kind of image processing algorithm that uses intensities as a feature. Most of the current inhomogeneity correction techniques use explicit smoothness assumptions on the inhomogeneity field, which sometimes limit their performance if the actual inhomogeneity is not smooth, a problem that becomes prevalent in high fields. The proposed patch-based inhomogeneity correction method does not assume any parametric smoothness model, instead, it uses patches from an atlas of an inhomogeneity-free image to do the correction. Preliminary results show that the proposed method is comparable to N3, a current state of the art method, when the inhomogeneity is smooth, and outperforms N3 when the inhomogeneity contains non-smooth elements.