A method is described to design parallel transmit (PTX) excitation pulses that are compatible with turbo spin echo (TSE) sequences, based on information available from conventional per-channel B1+ ...mapping. The excitation phase of PTX pulses that generate a reduced field of excitation (rFOX) is matched to the phase the quadrature mode of a PTX coil. This enables TSE imaging of a PTX-enabled rFOX excitation combined with standard nonselective refocusing pulses transmitted in the quadrature mode. In-vivo imaging experiments were performed at 7T using a dual channel parallel transmit head coil. In combination with simulations, the CPMG-required excitation phase was confirmed in TSE sequences with refocusing pulses of variable flip angle. Further experiments showed that the same rFOX was generated in TSE and gradient echo sequences, enabling high-resolution imaging with parallel imaging acceleration of the rFOX.
•Phase matching allows for combining personalized excitation pulses with non-selective refocusing pulses in a TSE sequence.•In-vivo experiments at 7T confirmed that the CPMG conditions for the excitation phase were observed.•Phase information from conventional B1+ maps was used, no additional measurements were required.•Phase matched RF pulses created the same 2D selective reduced field of excitation in gradient echo and spin echo sequences.
Purpose Several parallel transmit MRI techniques require knowledge of the transmit radiofrequency field profiles (B sub(1) super(+)). During the past years, various methods have been developed to ...acquire this information. Often, these methods suffer from long measurement times and produce maps exhibiting regions with poor signal-to-noise ratio and artifacts. In this article, a model-based reconstruction procedure is introduced that improves the robustness of B sub(1) super(+) mapping. Theory and Methods The missing information from undersampled B sub(1) super(+) maps and the regions of poor signal to noise ratio are reconstructed through projection into the space of spherical functions that arise naturally from the solution of the Helmholtz equations in the spherical coordinate system. Results As a result, B sub(1) super(+) data over a limited range of the field of view/volume is sufficient to reconstruct the B sub(1) super(+) over the full spatial domain in a fast and robust way. The same model is exploited to filter the noise of the measured maps. Results from simulations and in vivo measurements confirm the validity of the proposed method. Conclusion A spherical functions model can well approximate the magnetic fields inside the body with few basis terms. Exploiting this compression capability, B sub(1) super(+) maps are reconstructed in regions of unknown or corrupted values. Magn Reson Med 71:394-401, 2014. copyright 2013 Wiley Periodicals, Inc.
A purely experimental method for MRI-based transfer function (TF) determination is presented. A TF characterizes the potential for radiofrequency heating of a linear implant by relating the incident ...tangential electric field to a scattered electric field at its tip. We utilize the previously introduced transfer matrix (TM) to determine transfer functions solely from the MR measurable quantities, that is, the
and transceive phase distributions. This technique can extend the current practice of phantom-based TF assessment with dedicated experimental setup toward MR-based methods that have the potential to assess the TF in more realistic situations.
An analytical description of the
magnitude and transceive phase distribution around a wire-like implant was derived based on the TM. In this model, the background field is described using a superposition of spherical and cylindrical harmonics while the transfer matrix is parameterized using a previously introduced attenuated wave model. This analytical description can be used to estimate the transfer matrix and transfer function based on the measured
distribution.
The TF was successfully determined for 2 mock-up implants: a 20-cm bare copper wire and a 20-cm insulated copper wire with 10 mm of insulation stripped at both endings in respectively 4 and 3 different trajectories. The measured TFs show a strong correlation with a reference determined from simulations and between the separate experiments with correlation coefficients above 0.96 between all TFs. Compared to the simulated TF, the maximum deviation in the estimated tip field is 9.4% and 12.2% for the bare and insulated wire, respectively.
A method has been developed to measure the TF of medical implants using MRI experiments. Jointly fitting the incident and scattered
distributions with an analytical description based on the transfer matrix enables accurate determination of the TF of 2 test implants. The presented method no longer needs input from simulated data and can therefore, in principle, be used to measure TF's in test animals or corpses.
Measuring the dynamics and mechanical properties of muscles and joints is important to understand the (patho)physiology of muscles. However, acquiring dynamic time-resolved MRI data is challenging. ...We have previously developed Spectro-Dynamic MRI which allows the characterization of dynamical systems at a high spatial and temporal resolution directly from k-space data. This work presents an extended Spectro-Dynamic MRI framework that reconstructs 1) time-resolved MR images, 2) time-resolved motion fields, 3) dynamical parameters, and 4) an activation force, at a temporal resolution of 11 ms. An iterative algorithm solves a minimization problem containing four terms: a motion model relating the motion to the fully-sampled k-space data, a dynamical model describing the expected type of dynamics, a data consistency term describing the undersampling pattern, and finally a regularization term for the activation force. We acquired MRI data using a dynamic motion phantom programmed to move like an actively driven linear elastic system, from which all dynamic variables could be accurately reconstructed, regardless of the sampling pattern. The proposed method performed better than a two-step approach, where time-resolved images were first reconstructed from the undersampled data without any information about the motion, followed by a motion estimation step.
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