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Purpose: To develop a practical method to enable 3D T 1 mapping of brain metabolites., Theory and Methods: Due to the high dimensionality of the imaging problem underlying metabolite T 1 mapping, measurement of metabolite T 1 values has been currently limited to a single voxel or slice. This work achieved 3D metabolite T 1 mapping by leveraging a recent ultrafast MRSI technique called SPICE (spectroscopic imaging by exploiting spatiospectral correlation). The Ernst-angle FID MRSI data acquisition used in SPICE was extended to variable flip angles, with variable-density sparse sampling for efficient encoding of metabolite T 1 information. In data processing, a novel generalized series model was used to remove water and subcutaneous lipid signals; a low-rank tensor model with prelearned subspaces was used to reconstruct the variable-flip-angle metabolite signals jointly from the noisy data., Results: The proposed method was evaluated using both phantom and healthy subject data. Phantom experimental results demonstrated that high-quality 3D metabolite T 1 maps could be obtained and used for correction of T 1 saturation effects. In vivo experimental results showed metabolite T 1 maps with a large spatial coverage of 240 × 240 × 72 mm 3 and good reproducibility coefficients (< 11%) in a 14.5-min scan. The metabolite T 1 times obtained ranged from 0.99 to 1.44 s in gray matter and from 1.00 to 1.35 s in white matter., Conclusion: We successfully demonstrated the feasibility of 3D metabolite T 1 mapping within a clinically acceptable scan time. The proposed method may prove useful for both T 1 mapping of brain metabolites and correcting the T 1 -weighting effects in quantitative metabolic imaging., (© 2024 The Author(s). Magnetic Resonance in Medicine published by Wiley Periodicals LLC on behalf of International Society for Magnetic Resonance in Medicine.)

Purpose: To develop a machine learning-based method for estimation of both transmitter and receiver B 1 fields desired for correction of the B 1 inhomogeneity effects in quantitative brain imaging., Theory and Methods: A subspace model-based machine learning method was proposed for estimation of B 1t and B 1r fields. Probabilistic subspace models were used to capture scan-dependent variations in the B 1 fields; the subspace basis and coefficient distributions were learned from pre-scanned training data. Estimation of the B 1 fields for new experimental data was achieved by solving a linear optimization problem with prior distribution constraints. We evaluated the performance of the proposed method for B 1 inhomogeneity correction in quantitative brain imaging scenarios, including T 1 and proton density (PD) mapping from variable-flip-angle spoiled gradient-echo (SPGR) data as well as neurometabolic mapping from MRSI data, using phantom, healthy subject and brain tumor patient data., Results: In both phantom and healthy subject data, the proposed method produced high-quality B 1 maps. B 1 correction on SPGR data using the estimated B 1 maps produced significantly improved T 1 and PD maps. In brain tumor patients, the proposed method produced more accurate and robust B 1 estimation and correction results than conventional methods. The B 1 maps were also applied to MRSI data from tumor patients and produced improved neurometabolite maps, with better separation between pathological and normal tissues., Conclusion: This work presents a novel method to estimate B 1 variations using probabilistic subspace models and machine learning. The proposed method may make correction of B 1 inhomogeneity effects more robust in practical applications., (© 2023 International Society for Magnetic Resonance in Medicine.)

Purpose: To improve calibrationless parallel imaging using pre-learned subspaces of coil sensitivity functions., Theory and Methods: A subspace-based joint sensitivity estimation and image reconstruction method was developed for improved parallel imaging with no calibration data. Specifically, we proposed to use a probabilistic subspace model to capture prior information of the coil sensitivity functions from previous scans acquired using the same receiver system. Both the subspace basis and coefficient distributions were learned from a small set of training data. The learned subspace model was then incorporated into the regularized reconstruction formalism that includes a sparsity prior. The nonlinear optimization problem was solved using alternating minimization algorithm. Public fastMRI brain dataset was retrospectively undersampled by different schemes for performance evaluation of the proposed method., Results: With no calibration data, the proposed method consistently produced the most accurate coil sensitivity estimation and highest quality image reconstructions at all acceleration factors tested in comparison with state-of-the-art methods including JSENSE, DeepSENSE, P-LORAKS, and Sparse BLIP. Our results are comparable to or even better than those from SparseSENSE, which used calibration data for sensitivity estimation. The work also demonstrated that the probabilistic subspace model learned from T 2 w data can be generalized to aiding the reconstruction of FLAIR data acquired from the same receiver system., Conclusion: A subspace-based method named JSENSE-Pro has been proposed for accelerated parallel imaging without the acquisition of companion calibration data. The method is expected to further enhance the practical utility of parallel imaging, especially in applications where calibration data acquisition is not desirable or limited., (© 2022 International Society for Magnetic Resonance in Medicine.)

Purpose: To accelerate T 2 mapping with highly sparse sampling by integrating deep learning image priors with low-rank and sparse modeling., Methods: The proposed method achieves high-speed T 2 mapping by highly sparsely sampling (k, TE)-space. Image reconstruction from the undersampled data was done by exploiting the low-rank structure and sparsity in the T 2 -weighted image sequence and image priors learned from training data. The image priors for a single TE were generated from the public Human Connectome Project data using a tissue-based deep learning method; the image priors were then transferred to other TEs using a generalized series-based method. With these image priors, the proposed reconstruction method used a low-rank model and a sparse model to capture subject-dependent novel features., Results: The proposed method was evaluated using experimental data obtained from both healthy subjects and tumor patients using a turbo spin-echo sequence. High-quality T 2 maps at the resolution of 0.9 × 0.9 × 3.0 mm 3 were obtained successfully from highly undersampled data with an acceleration factor of 8. Compared with the existing compressed sensing-based methods, the proposed method produced significantly reduced reconstruction errors. Compared with the deep learning-based methods, the proposed method recovered novel features better., Conclusion: This paper demonstrates the feasibility of learning T 2 -weighted image priors for multiple TEs using tissue-based deep learning and generalized series-based learning. A new method was proposed to effectively integrate these image priors with low-rank and sparse modeling to reconstruct high-quality images from highly undersampled data. The proposed method will supplement other acquisition-based methods to achieve high-speed T 2 mapping., (© 2020 International Society for Magnetic Resonance in Medicine.)

Purpose: To achieve high-resolution mapping of brain tissue susceptibility in simultaneous QSM and metabolic imaging., Methods: Simultaneous QSM and metabolic imaging was first achieved using SPICE (spectroscopic imaging by exploiting spatiospectral correlation), but the QSM maps thus obtained were at relatively low-resolution (2.0 × 3.0 × 3.0 mm 3 ). We overcome this limitation using an improved SPICE data acquisition method with the following novel features: 1) sampling (k, t)-space in dual densities, 2) sampling central k-space fully to achieve nominal spatial resolution of 3.0 × 3.0 × 3.0 mm 3 for metabolic imaging, and 3) sampling outer k-space sparsely to achieve spatial resolution of 1.0 × 1.0 × 1.9 mm 3 for QSM. To keep the scan time short, we acquired spatiospectral encodings in echo-planar spectroscopic imaging trajectories in central k-space but in CAIPIRINHA (controlled aliasing in parallel imaging results in higher acceleration) trajectories in outer k-space using blipped phase encodings. For data processing and image reconstruction, a union-of-subspaces model was used, effectively incorporating sensitivity encoding, spatial priors, and spectral priors of individual molecules., Results: In vivo experiments were carried out to evaluate the feasibility and potential of the proposed method. In a 6-min scan, QSM maps at 1.0 × 1.0 × 1.9 mm 3 resolution and metabolic maps at 3.0 × 3.0 × 3.0 mm 3 nominal resolution were obtained simultaneously. Compared with the original method, the QSM maps obtained using the new method reveal fine-scale brain structures more clearly., Conclusion: We demonstrated the feasibility of achieving high-resolution QSM simultaneously with metabolic imaging using a modified SPICE acquisition method. The improved capability of SPICE may further enhance its practical utility in brain mapping., (© 2020 International Society for Magnetic Resonance in Medicine.)

Purpose: To accelerate the acquisition of J-resolved proton magnetic resonance spectroscopic imaging ( 1 H-MRSI) data for high-resolution mapping of brain metabolites and neurotransmitters., Methods: The proposed method used a subspace model to represent multidimensional spatiospectral functions, which significantly reduced the number of parameters to be determined from J-resolved 1 H-MRSI data. A semi-LASER-based (Localization by Adiabatic SElective Refocusing) echo-planar spectroscopic imaging (EPSI) sequence was used for data acquisition. The proposed data acquisition scheme sampled k , t 1 , t 2 -space in variable density, where t 1 and t 2 specify the J-coupling and chemical-shift encoding times, respectively. Selection of the J-coupling encoding times (or, echo time values) was based on a Cramer-Rao lower bound analysis, which were optimized for gamma-aminobutyric acid (GABA) detection. In image reconstruction, parameters of the subspace-based spatiospectral model were determined by solving a constrained optimization problem., Results: Feasibility of the proposed method was evaluated using both simulated and experimental data from a spectroscopic phantom. The phantom experimental results showed that the proposed method, with a factor of 12 acceleration in data acquisition, could determine the distribution of J-coupled molecules with expected accuracy. In vivo study with healthy human subjects also showed that 3D maps of brain metabolites and neurotransmitters can be obtained with a nominal spatial resolution of 3.0 × 3.0 × 4.8 mm 3 from J-resolved 1 H-MRSI data acquired in 19.4 min., Conclusions: This work demonstrated the feasibility of highly accelerated J-resolved 1 H-MRSI using limited and sparse sampling of k , t 1 , t 2 -space and subspace modeling. With further development, the proposed method may enable high-resolution mapping of brain metabolites and neurotransmitters in clinical applications., (© 2020 International Society for Magnetic Resonance in Medicine.)