An RF coil design to enable quintuple nuclear whole-brain MRI.

PURPOSE: To bring metabolic imaging based on multi-NMR toward practical use from the RF hardware perspective. METHODS: A highly integrated RF coil is designed for whole-brain MRI and MRS targeted to five nuclear species: 1 H, 19 F, 31 P, 23 Na, and 13 C. Dipole antennas and closely loaded local receiver loops are combined in this setup. RESULTS: High-quality in vivo scan results of 1 H, 31 P, 23 Na, and 13 C on healthy volunteers have been achieved. For 1 H, the transmit efficiency is 77% of a single-tuned commercial head coil (NOVA 8-transmit [Tx]/32-receive [Rx]; NOVA Medical, Wilmington, MA, USA). For 31 P, 110% SNR of a dual-tuned close-fit head-birdcage was achieved at the center of the subject, based on MR experiments on a phantom. For 31 P, 23 Na, and 13 C, bench measurements indicate SNR loss of 15%, 27%, and 30% compared with single-tuned conditions. 19 F performance has been proven to be similar to that of 1 H through bench tests and electromagnetic simulations. CONCLUSION: With this device, 1 H-based anatomic images that are expected to meet clinical requirements, as well as high-quality multi-NMR images and spectra, can be acquired within one scan session without hardware replacement or patient repositioning, enabling morphologic and metabolic MRI within acceptable scan time.

Tier-based formalism for safety assessment of custom-built radio-frequency transmit coils.

The purpose of this work is to propose a tier-based formalism for safety assessment of custom-built radio-frequency (RF) coils that balances validation effort with the effort put in determinating the safety factor. The formalism has three tier levels. Higher tiers require increased effort when validating electromagnetic simulation results but allow for less conservative safety factors. In addition, we propose a new method to calculate modeling uncertainty between simulations and measurements and a new method to propagate uncertainties in the simulation into a safety factor that minimizes the risk of underestimating the peak specific absorption rate (SAR). The new safety assessment procedure was completed for all tier levels for an eight-channel dipole array for prostate imaging at 7 T and an eight-channel dipole array for head imaging at 10.5 T, using data from two different research sites. For the 7 T body array, the validation procedure resulted in a modeling uncertainty of 77% between measured and simulated local SAR distributions. For a situation where RF shimming is performed on the prostate, average power limits of 2.4 and 4.5 W/channel were found for tiers 2 and 3, respectively. When the worst-case peak SAR among all phase settings was calculated, power limits of 1.4 and 2.7 W/channel were found for tiers 2 and 3, respectively. For the 10.5 T head array, a modeling uncertainty of 21% was found based on B1 + mapping. For the tier 2 validation, a power limit of 2.6 W/channel was calculated. The demonstrated tier system provides a strategy for evaluating modeling inaccuracy, allowing for the rapid translation of novel coil designs with conservative safety factors and the implementation of less conservative safety factors for frequently used coil arrays at the expense of increased validation effort.

Real-time assessment of potential peak local specific absorption rate value without phase monitoring: Trigonometric maximization method for worst-case local specific absorption rate determination.

PURPOSE: Multi-transmit MRI systems are typically equipped with dedicated hardware to sample the reflected/lost power in the transmit channels. After extensive calibration, the amplitude and phase of the signal at the feed of each array element can be accurately determined. However, determining the phase is more difficult and monitoring errors can lead to a hazardous peak local specific absorption rate (pSAR10g ) underestimation. For this purpose, methods were published for online maximum potential pSAR10g estimation without relying on phase monitoring, but these methods produce considerable overestimation. We present a trigonometric maximization method to determine the actual worst-case pSAR10g without any overestimation. THEORY AND METHOD: The proposed method takes advantage of the sinusoidal relation between the SAR10g in each voxel and the phases of input signals, to return the maximum achievable SAR10g in a few iterations. The method is applied to determine the worst-case pSAR10g for three multi-transmit array configurations at 7T: (1) body array with eight fractionated dipoles; (2) head array with eight fractionated dipoles; (3) head array with eight rectangular loops. The obtained worst-case pSAR10g values are compared with the pSAR10g values determined with a commonly used method and with a more efficient method based on reference-phases. RESULTS: For each voxel, the maximum achievable SAR10g is determined in less than 0.1 ms. Compared to the reference-phases-based method, the proposed method reduces the mean overestimation of the actual pSAR10g up to 52%, while never underestimating the true pSAR10g . CONCLUSION: The proposed method can widely improve the performance of parallel transmission MRI systems without phase monitoring.

Conditional safety margins for less conservative peak local SAR assessment: A probabilistic approach.

PURPOSE: The introduction of a linear safety factor to address peak local specific absorption rate (pSAR10g ) uncertainties (eg, intersubject variation, modeling inaccuracies) bears one considerable drawback: It often results in over-conservative scanning constraints. We present a more efficient approach to define a variable safety margin based on the conditional probability density function of the effectively obtained pSAR10g value, given the estimated pSAR10g value. METHODS: The conditional probability density function can be estimated from previously simulated data. A representative set of true and estimated pSAR10g samples was generated by means of our database of 23 subject-specific models with an 8-fractionated dipole array for prostate imaging at 7 T. The conditional probability density function was calculated for each possible estimated pSAR10g value and used to determine the corresponding safety margin with an arbitrary low probability of underestimation. This approach was applied to five state-of-the-art local SAR estimation methods, namely: (1) using just the generic body model "Duke"; (2) using our model library to assess the maximum pSAR10g value over all models; (3) using the most representative "local SAR model"; (4) using the five most representative local SAR models; and (5) using a recently developed deep learning-based method. RESULTS: Compared with the more conventional safety factor, the conditional safety-margin approach results in lower (up to 30%) mean overestimation for all investigated local SAR estimation methods. CONCLUSION: The proposed probabilistic approach for pSAR10g correction allows more accurate local SAR assessment with much lower overestimation, while a predefined level of underestimation is accepted (eg, 0.1%).