The ultimate intrinsic signal-to-noise ratio is the highest possible signal-to-noise ratio, and the ultimate intrinsic specific absorption rate provides the lowest limit of the specific absorption rate for a given flip angle distribution. Analytic expressions for ultimate intrinsic signal-to-noise ratio and ultimate intrinsic specific absorption rate are obtained for arbitrary sample geometries. These expressions are valid when the distance between the point of interest and the sample surface is smaller than the wavelength, and the sample is homogeneous. The dependence on the sample permittivity, conductivity, temperature, size, and the static magnetic field strength is given in analytic form, which enables the easy evaluation of the change in signal-to-noise ratio and specific absorption rate when the sample is scaled in size or when any of its geometrical or electrical parameters is altered. Furthermore, it is shown that signal-to-noise ratio and specific absorption rate are independent of the permeability of the sample. As a practical case and a solution example, a uniform, circular cylindrically shaped sample is studied. Magn Reson Med, 2011.

A central limitation of multiple-acquisition magnetic resonance imaging (MRI) is the degradation in scan efficiency as the number of distinct datasets grows. Sparse recovery techniques can alleviate this limitation via randomly undersampled acquisitions. A frequent sampling strategy is to prescribe for each acquisition a different random pattern drawn from a common sampling density. However, naive random patterns often contain gaps or clusters across the acquisition dimension that in turn can degrade reconstruction quality or reduce scan efficiency. To address this problem, a statistically-segregated sampling method is proposed for multiple-acquisition MRI. This method generates multiple patterns sequentially, while adaptively modifying the sampling density to minimize k-space overlap across patterns. As a result, it improves incoherence across acquisitions while still maintaining similar sampling density across the radial dimension of k-space. Comprehensive simulations and in vivo results are presented for phase-cycled balanced steady-state free precession and multi-echo T2-weighted imaging. Segregated sampling achieves significantly improved quality in both Fourier and compressedsensing reconstructions of multiple-acquisition datasets.

Purpose: Tailored parallel‐transmit (pTx) pulses produce uniform excitation profiles at 7 T, but are sensitive to head motion. A potential solution is real‐time pulse redesign. A deep learning framework is proposed to estimate pTx distributions following within‐slice motion, which can then be used for tailored pTx pulse redesign.Methods: Using simulated data, conditional generative adversarial networks were trained to predict distributions in the head following a displacement. Predictions were made for two virtual body models that were not included in training. Predicted maps were compared with ground‐truth (simulated, following motion) B₁ maps. Tailored pTx pulses were designed using B₁ maps at the original position (simulated, no motion) and evaluated using simulated B₁ maps at displaced position (ground‐truth maps) to quantify motion‐related excitation error. A second pulse was designed using predicted maps (also evaluated on ground‐truth maps) to investigate improvement offered by the proposed method.Results: Predicted maps corresponded well with ground‐truth maps. Error in predicted maps was lower than motion‐related error in 99% and 67% of magnitude and phase evaluations, respectively. Worst‐case flip‐angle normalized RMS error due to motion (76% of target flip angle) was reduced by 59% when pulses were redesigned using predicted maps.Conclusion: We propose a framework for predicting maps online with deep neural networks. Predicted maps can then be used for real‐time tailored pulse redesign, helping to overcome head motion–related error in pTx.

Purpose: Turbo spin echo (TSE) imaging reduces imaging time by acquiring multiple echoes per repetition (TR), requiring fewer TRs. O‐space can also require fewer TRs by using a combination of nonlinear magnetic gradient fields and surface coil arrays. Although to date, O‐space has only been demonstrated for gradient echo imaging, it is valuable to combine these two techniques. However, collecting multiple O‐space echoes per TR is difficult because of the different local k‐space trajectories and variable T2‐weighting.Theory and Methods: A practical scheme is demonstrated to combine the benefits of TSE and O‐space for highly accelerated T2‐weighted images. The scheme uses a modified acquisition order and filtered projection reconstruction to reduce artifacts caused by T2 decay, while retaining T2 contrast that corresponds to a specific echo time.Results: The experiments revealed that the proposed method can produce highly accelerated T2‐weighted images. Moreover, the method can generate multiple images with different T2 contrasts from a single dataset.Conclusions: The proposed O‐space TSE imaging method requires fewer echoes than conventional TSE and fewer repetitions than conventional O‐space imaging. It retains resilience to undersampling, clearly outperforming Cartesian SENSE at high levels of undersampling, and can generate undistorted images with a range of T2 contrast from a single acquired dataset. Magn Reson Med 75:1654–1661, 2016. © 2015 Wiley Periodicals, Inc.