Transmit and receive RF fields determination from a single low-tip-angle gradient-echo scan by scaling of SVD data

Array-compressed parallel transmit pulse design

PURPOSE

To design array-compressed parallel transmit radiofrequency (RF) pulses and compare them to pulses designed with existing transmit array compression strategies.

THEORY AND METHODS

Array-compressed parallel RF pulse design is proposed as the joint optimization of a matrix of complex-valued compression weights that relate a full-channel physical array to a reduced-channel virtual array, along with a set of RF pulses for the virtual array. In this way, the physics of the RF pulse application determine the coil combination weights. Array-compressed pulse design algorithms are described for four parallel transmit applications: accelerated two-dimensional spiral excitation, multislice RF shimming, small-tip-angle kT -points excitation, and slice-selective spokes refocusing. Array-compressed designs are compared in simulations and an experiment to pulses designed using four existing array compression strategies.

RESULTS

In all cases, array-compressed pulses achieved the lowest root-mean-square excitation error among the array compression approaches. Low errors were generally achieved without increasing root-mean-square RF amplitudes or maximum local 10-gram specific absorption rate. Leave-one-out multisubject shimming simulations demonstrated that array-compressed RF shimming can identify useful fixed coil combination weights that perform well across a population.

CONCLUSION

Array-compressed pulse design jointly identifies the transmit coil array compression weights and RF pulses that perform best for a specific parallel excitation application. Magn Reson Med 76:1158-1169, 2016. © 2015 Wiley Periodicals, Inc.

Large dynamic range relative B1+ mapping

Purpose

Parallel transmission (PTx) requires knowledge of the B1+ produced by each element. However, B1+ mapping can be challenging when transmit fields exhibit large dynamic range. This study presents a method to produce high quality relative B1+ maps when this is the case.

Theory and Methods

The proposed technique involves the acquisition of spoiled gradient echo (SPGR) images at multiple radiofrequency drive levels for each transmitter. The images are combined using knowledge of the SPGR signal equation using maximum likelihood estimation, yielding an image for each channel whose signal is proportional to the B1+ field strength. Relative B1+ maps are then obtained by taking image ratios. The method was tested using numerical simulations, phantom imaging, and through in vivo experiments.

Results

The numerical simulations demonstrated that the proposed method can reconstruct relative transmit sensitivities over a wide range of B1+ amplitudes and at several SNR levels. The method was validated at 3 Tesla (T) by comparing it with an alternative B1+ mapping method, and demonstrated in vivo at 7T.

Conclusion

Relative B1+ mapping in the presence of large dynamic range has been demonstrated through numerical simulations, phantom imaging at 3T and experimentally at 7T. The method will enable PTx to be applied in challenging imaging scenarios at ultrahigh field. Magn Reson Med 76:490–499, 2016. © 2015 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals, Inc. on behalf of International Society for Magnetic Resonance in Medicine. This is an open access article under the terms of the Creative Commons Attribution License, which permits use, distribution and reproduction in any medium, provided the original work is properly cited.

Dynamic multi-coil tailored excitation for transmit B1 correction at 7 Tesla

PURPOSE

Tailored excitation (TEx) based on interspersing multiple radio frequency pulses with linear gradient and higher-order shim pulses can be used to obtain uniform flip angle in the presence of large radio frequency transmission (B 1+) inhomogeneity. Here, an implementation of dynamic, multislice tailored excitation using the recently developed multi-coil nonlinear shim hardware (MC-DTEx) is reported.

METHODS

MC-DTEx was developed and tested both in a phantom and in vivo at 7 T, and its efficacy was quantitatively assessed. Predicted outcomes of MC-DTEx and DTEx based on spherical harmonic shims (SH-DTEx) were also compared.

RESULTS

For a planned 30 ° flip angle, in a phantom, the standard deviation in excitation improved from 28% (regular excitation) to 12% with MC-DTEx. The SD in in vivo excitation improved from 22 to 12%. The improvements achieved with experimental MC-DTEx closely matched the theoretical predictions. Simulations further showed that MC-DTEx outperforms SH-DTEx for both scenarios.

CONCLUSION

Successful implementation of multislice MC-DTEx is presented and is shown to be capable of homogenizing excitation over more than twofold B 1+ variations. Its benefits over SH-DTEx are also demonstrated. A distinct advantage of MC hardware over SH shim hardware is the absence of significant eddy current effects, which allows for a straightforward, multislice implementation of MC-DTEx. Magn Reson Med 76:83-93, 2016. © 2015 Wiley Periodicals, Inc.

SVD compression for magnetic resonance fingerprinting in the time domain

Magnetic resonance (MR) fingerprinting is a technique for acquiring and processing MR data that simultaneously provides quantitative maps of different tissue parameters through a pattern recognition algorithm. A predefined dictionary models the possible signal evolutions simulated using the Bloch equations with different combinations of various MR parameters and pattern recognition is completed by computing the inner product between the observed signal and each of the predicted signals within the dictionary. Though this matching algorithm has been shown to accurately predict the MR parameters of interest, one desires a more efficient method to obtain the quantitative images. We propose to compress the dictionary using the singular value decomposition, which will provide a low-rank approximation. By compressing the size of the dictionary in the time domain, we are able to speed up the pattern recognition algorithm, by a factor of between 3.4-4.8, without sacrificing the high signal-to-noise ratio of the original scheme presented previously.