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Objective: We propose a method for the reconstruction of parameter-maps in Quantitative Magnetic Resonance Imaging (QMRI)., Methods: Because different quantitative parameter-maps differ from each other in terms of local features, we propose a method where the employed dictionary learning (DL) and sparse coding (SC) algorithms automatically estimate the optimal dictionary-size and sparsity level separately for each parameter-map. We evaluated the method on a T 1 -mapping QMRI problem in the brain using the BrainWeb data as well as in-vivo brain images acquired on an ultra-high field 7 T scanner. We compared it to a model-based acceleration for parameter mapping (MAP) approach, other sparsity-based methods using total variation (TV), Wavelets (Wl), and Shearlets (Sh) to a method which uses DL and SC to reconstruct qualitative images, followed by a non-linear (DL+Fit)., Results: Our algorithm surpasses MAP, TV, Wl, and Sh in terms of RMSE and PSNR. It yields better or comparable results to DL+Fit by additionally significantly accelerating the reconstruction by a factor of approximately seven., Conclusion: The proposed method outperforms the reported methods of comparison and yields accurate T 1 -maps. Although presented for T 1 -mapping in the brain, our method's structure is general and thus most probably also applicable for the the reconstruction of other quantitative parameters in other organs., Significance: From a clinical perspective, the obtained T 1 -maps could be utilized to differentiate between healthy subjects and patients with Alzheimer's disease. From a technical perspective, the proposed unsupervised method could be employed to obtain ground-truth data for the development of data-driven methods based on supervised learning.

Purpose: To simultaneously acquire spectroscopic signals from two MRS voxels using a multi-banded 2 spin-echo, full-intensity acquired localized (2SPECIAL) sequence, and to decompose the signal to their respective regions by a novel voxel-GRAPPA (vGRAPPA) decomposition approach for in vivo brain applications at 7 T., Methods: A wideband, uniform rate, smooth truncation (WURST) multi-banded pulse was incorporated into SPECIAL to implement 2SPECIAL for simultaneous multi-voxel spectroscopy (sMVS). To decompose the acquired data, the voxel-GRAPPA decomposition algorithm is introduced, and its performance is compared to the SENSE-based decomposition. Furthermore, the limitations of two-voxel excitation concerning the multi-banded adiabatic inversion pulse, as well as of the combined B 0 shim and B 1 + adjustments, are evaluated., Results: It was successfully shown that the 2SPECIAL sequence enables sMVS without a significant loss in SNR while reducing the total scan time by 21.6% compared to two consecutive acquisitions. The proposed voxel-GRAPPA algorithm properly reassigns the signal components to their respective origin region and shows no significant differences to the well-established SENSE-based algorithm in terms of leakage (both <10%) or Cramér-Rao lower bounds (CRLB) for in vivo applications, while not requiring the acquisition of additional sensitivity maps and thus decreasing motion sensitivity., Conclusion: The use of 2SPECIAL in combination with the novel voxel-GRAPPA decomposition technique allows a substantial reduction of measurement time compared to the consecutive acquisition of two single voxels without a significant decrease in spectral quality or metabolite quantification accuracy and thus provides a new option for multiple-voxel applications., (© 2022 The Authors. Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: To compare several different optimization algorithms currently used for localized in vivo B 0 shimming, and to introduce a novel, fast, and robust constrained regularized algorithm (ConsTru) for this purpose., Methods: Ten different optimization algorithms (including samples from both generic and dedicated least-squares solvers, and a novel constrained regularized inversion method) were implemented and compared for shimming in five different shimming volumes on 66 in vivo data sets from both 7 T and 9.4 T. The best algorithm was chosen to perform single-voxel spectroscopy at 9.4 T in the frontal cortex of the brain on 10 volunteers., Results: The results of the performance tests proved that the shimming algorithm is prone to unstable solutions if it depends on the value of a starting point, and is not regularized to handle ill-conditioned problems. The ConsTru algorithm proved to be the most robust, fast, and efficient algorithm among all of the chosen algorithms. It enabled acquisition of spectra of reproducible high quality in the frontal cortex at 9.4 T., Conclusions: For localized in vivo B 0 shimming, the use of a dedicated linear least-squares solver instead of a generic nonlinear one is highly recommended. Among all of the linear solvers, the constrained regularized method (ConsTru) was found to be both fast and most robust. Magn Reson Med 79:1145-1156, 2018. © 2017 International Society for Magnetic Resonance in Medicine., (© 2017 International Society for Magnetic Resonance in Medicine.)

Purpose: To substantially improve spatial localization in magnetic resonance spectroscopic imaging (MRSI) accelerated by parallel imaging. This is important in order to make MRSI more reliable as a tool for clinical applications., Methods: The sensitivity encoding acceleration technique with spatial overdiscretization is applied for the reconstruction of parallel MRSI. In addition, the spatial response function is optimized by minimizing its deviation from a previously chosen target function. This modified minimum-norm sensitivity encoding-MRSI reconstruction approach is applied in this article for in vivo pulse-acquire MRSI of human brain at 7T with simulated acceleration factors of 2, 4, and 9 as well as actual 4-fold accelerated MRSI., Results: The sidelobes of the spatial response function are significantly suppressed, which reduces far-reaching voxel bleeding. At the same time, the major enlargement of the effective voxel size, which would be introduced by conventional k-space apodization methods, is largely avoided. Regularization allows for a practical trade-off between noise minimization, effective voxel size, and unaliasing. Although not aiming at increasing the nominal spatial resolution, a better spatial specificity is achieved., Conclusion: Simultaneous suppression of short- and far-reaching voxel bleeding in MRSI is analyzed and reconstruction of highly accelerated parallel in vivo MRSI is demonstrated., (© 2014 Wiley Periodicals, Inc.)