Mapping intracellular NAD content in entire human brain using phosphorus-31 MR spectroscopic imaging at 7 Tesla.

Introduction: Nicotinamide adenine dinucleotide (NAD) is a crucial molecule in cellular metabolism and signaling. Mapping intracellular NAD content of human brain has long been of interest. However, the sub-millimolar level of cerebral NAD concentration poses significant challenges for in vivo measurement and imaging. Methods: In this study, we demonstrated the feasibility of non-invasively mapping NAD contents in entire human brain by employing a phosphorus-31 magnetic resonance spectroscopic imaging (31P-MRSI)-based NAD assay at ultrahigh field (7 Tesla), in combination with a probabilistic subspace-based processing method. Results: The processing method achieved about a 10-fold reduction in noise over raw measurements, resulting in remarkably reduced estimation errors of NAD. Quantified NAD levels, observed at approximately 0.4 mM, exhibited good reproducibility within repeated scans on the same subject and good consistency across subjects in group data (2.3 cc nominal resolution). One set of higher-resolution data (1.0 cc nominal resolution) unveiled potential for assessing tissue metabolic heterogeneity, showing similar NAD distributions in white and gray matter. Preliminary analysis of age dependence suggested that the NAD level decreases with age. Discussion: These results illustrate favorable outcomes of our first attempt to use ultrahigh field 31P-MRSI and advanced processing techniques to generate a whole-brain map of low-concentration intracellular NAD content in the human brain.

Accelerated 3D metabolite T1 mapping of the brain using variable-flip-angle SPICE.

PURPOSE: To develop a practical method to enable 3D T1 mapping of brain metabolites. THEORY AND METHODS: Due to the high dimensionality of the imaging problem underlying metabolite T1 mapping, measurement of metabolite T1 values has been currently limited to a single voxel or slice. This work achieved 3D metabolite T1 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 T1 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 T1 maps could be obtained and used for correction of T1 saturation effects. In vivo experimental results showed metabolite T1 maps with a large spatial coverage of 240 × 240 × 72 mm3 and good reproducibility coefficients (< 11%) in a 14.5-min scan. The metabolite T1 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 T1 mapping within a clinically acceptable scan time. The proposed method may prove useful for both T1 mapping of brain metabolites and correcting the T1-weighting effects in quantitative metabolic imaging.

Learning-Assisted Fast Determination of Regularization Parameter in Constrained Image Reconstruction.

OBJECTIVE: To leverage machine learning (ML) for fast selection of optimal regularization parameter in constrained image reconstruction. METHODS: Constrained image reconstruction is often formulated as a regularization problem and selecting a good regularization parameter value is an essential step. We solved this problem using an ML-based approach by leveraging the finding that for a specific constrained reconstruction problem defined for a fixed class of image functions, the optimal regularization parameter value is weakly subject-dependent and the dependence can be captured using few experimental data. The proposed method has four key steps: a) solution of a given constrained reconstruction problem for a few (say, 3) pre-selected regularization parameter values, b) extraction of multiple approximated quality metrics from the initial reconstructions, c) predicting the true quality metrics values from the approximated values using pre-trained neural networks, and d) determination of the optimal regularization parameter by fusing the predicted quality metrics. RESULTS: The effectiveness of the proposed method was demonstrated in two constrained reconstruction problems. Compared with L-curve-based method, the proposed method determined the regularization parameters much faster and produced substantially improved reconstructions. Our method also outperformed state-of-the-art learning-based methods when trained with limited experimental data. CONCLUSION: This paper demonstrates the feasibility and improved reconstruction quality by using machine learning to determine the regularization parameter in constrained reconstruction. SIGNIFICANCE: The proposed method substantially reduces the computational burden of the traditional methods (e.g., L-curve) or relaxes the requirement of large training data by modern learning-based methods, thus enhancing the practical utility of constrained reconstruction.

Simultaneous high-resolution whole-brain MR spectroscopy and [18F]FDG PET for temporal lobe epilepsy.

PURPOSE: Precise lateralizing the epileptogenic zone in patients with drug-resistant mesial temporal lobe epilepsy (mTLE) remains challenging, particularly when routine MRI scans are inconclusive (MRI-negative). This study aimed to investigate the synergy of fast, high-resolution, whole-brain MRSI in conjunction with simultaneous [18F]FDG PET for the lateralization of mTLE. METHODS: Forty-eight drug-resistant mTLE patients (M/F 31/17, age 12-58) underwent MRSI and [18F]FDG PET on a hybrid PET/MR scanner. Lateralization of mTLE was evaluated by visual inspection and statistical classifiers of metabolic mappings against routine MRI. Additionally, this study explored how disease status influences the associations between altered N-acetyl aspartate (NAA) and FDG uptake using hierarchical moderated multiple regression. RESULTS: The high-resolution whole-brain MRSI data offers metabolite maps at comparable resolution to [18F]FDG PET. Visual examinations of combined MRSI and [18F]FDG PET showed an mTLE lateralization accuracy rate of 91.7% in a 48-patient cohort, surpassing routine MRI (52.1%). Notably, out of 23 MRI-negative mTLE, combined MRSI and [18F]FDG PET helped detect 19 cases. Logistical regression models combining hippocampal NAA level and FDG uptake improved lateralization performance (AUC=0.856), while further incorporating extrahippocampal regions such as amygdala, thalamus, and superior temporal gyrus increased the AUC to 0.939. Concurrent MRSI/PET revealed a moderating influence of disease duration and hippocampal atrophy on the association between hippocampal NAA and glucose uptake, providing significant new insights into the disease's trajectory. CONCLUSION: This paper reports the first metabolic imaging study using simultaneous high-resolution MRSI and [18F]FDG PET, which help visualize MRI-unidentifiable lesions and may thus advance diagnostic tools and management strategies for drug-resistant mTLE.

Optimization of 3D dynamic speech MRI: Poisson-disc undersampling and locally higher-rank reconstruction through partial separability model with regional optimized temporal basis.

PURPOSE: To improve the spatiotemporal qualities of images and dynamics of speech MRI through an improved data sampling and image reconstruction approach. METHODS: For data acquisition, we used a Poisson-disc random under sampling scheme that reduced the undersampling coherence. For image reconstruction, we proposed a novel locally higher-rank partial separability model. This reconstruction model represented the oral and static regions using separate low-rank subspaces, therefore, preserving their distinct temporal signal characteristics. Regional optimized temporal basis was determined from the regional-optimized virtual coil approach. Overall, we achieved a better spatiotemporal image reconstruction quality with the potential of reducing total acquisition time by 50%. RESULTS: The proposed method was demonstrated through several 2-mm isotropic, 64 mm total thickness, dynamic acquisitions with 40 frames per second and compared to the previous approach using a global subspace model along with other k-space sampling patterns. Individual timeframe images and temporal profiles of speech samples were shown to illustrate the ability of the Poisson-disc under sampling pattern in reducing total acquisition time. Temporal information of sagittal and coronal directions was also shown to illustrate the effectiveness of the locally higher-rank operator and regional optimized temporal basis. To compare the reconstruction qualities of different regions, voxel-wise temporal SNR analysis were performed. CONCLUSION: Poisson-disc sampling combined with a locally higher-rank model and a regional-optimized temporal basis can drastically improve the spatiotemporal image quality and provide a 50% reduction in overall acquisition time.

Subspace Model-Assisted Deep Learning for Improved Image Reconstruction.

Image reconstruction from limited and/or sparse data is known to be an ill-posed problem and a priori information/constraints have played an important role in solving the problem. Early constrained image reconstruction methods utilize image priors based on general image properties such as sparsity, low-rank structures, spatial support bound, etc. Recent deep learning-based reconstruction methods promise to produce even higher quality reconstructions by utilizing more specific image priors learned from training data. However, learning high-dimensional image priors requires huge amounts of training data that are currently not available in medical imaging applications. As a result, deep learning-based reconstructions often suffer from two known practical issues: a) sensitivity to data perturbations (e.g., changes in data sampling scheme), and b) limited generalization capability (e.g., biased reconstruction of lesions). This paper proposes a new method to address these issues. The proposed method synergistically integrates model-based and data-driven learning in three key components. The first component uses the linear vector space framework to capture global dependence of image features; the second exploits a deep network to learn the mapping from a linear vector space to a nonlinear manifold; the third is an unrolling-based deep network that captures local residual features with the aid of a sparsity model. The proposed method has been evaluated with magnetic resonance imaging data, demonstrating improved reconstruction in the presence of data perturbation and/or novel image features. The method may enhance the practical utility of deep learning-based image reconstruction.

B1 mapping using pre-learned subspaces for quantitative brain imaging.

PURPOSE: To develop a machine learning-based method for estimation of both transmitter and receiver B1 fields desired for correction of the B1 inhomogeneity effects in quantitative brain imaging. THEORY AND METHODS: A subspace model-based machine learning method was proposed for estimation of B1t and B1r fields. Probabilistic subspace models were used to capture scan-dependent variations in the B1 fields; the subspace basis and coefficient distributions were learned from pre-scanned training data. Estimation of the B1 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 B1 inhomogeneity correction in quantitative brain imaging scenarios, including T1 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 B1 maps. B1 correction on SPGR data using the estimated B1 maps produced significantly improved T1 and PD maps. In brain tumor patients, the proposed method produced more accurate and robust B1 estimation and correction results than conventional methods. The B1 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 B1 variations using probabilistic subspace models and machine learning. The proposed method may make correction of B1 inhomogeneity effects more robust in practical applications.

Prediction of Stroke Onset Time With Combined Fast High-Resolution Magnetic Resonance Spectroscopic and Quantitative T2 Mapping.

OBJECTIVE: The purpose of this work is to develop a multispectral imaging approach that combines fast high-resolution 3D magnetic resonance spectroscopic imaging (MRSI) and fast quantitative T2 mapping to capture the multifactorial biochemical changes within stroke lesions and evaluate its potentials for stroke onset time prediction. METHODS: Special imaging sequences combining fast trajectories and sparse sampling were used to obtain whole-brain maps of both neurometabolites (2.0 × 3.0 × 3.0 mm3) and quantitative T2 values (1.9 × 1.9 × 3.0 mm3) within a 9-minute scan. Participants with ischemic stroke at hyperacute (0-24 h, n = 23) or acute (24 h-7d, n = 33) phase were recruited in this study. Lesion N-acetylaspartate (NAA), lactate, choline, creatine, and T2 signals were compared between groups and correlated with patient symptomatic duration. Bayesian regression analyses were employed to compare the predictive models of symptomatic duration using multispectral signals. RESULTS: In both groups, increased T2 and lactate levels, as well as decreased NAA and choline levels were detected within the lesion (all p < 0.001). Changes in T2, NAA, choline, and creatine signals were correlated with symptomatic duration for all patients (all p < 0.005). Predictive models of stroke onset time combining signals from MRSI and T2 mapping achieved the best performance (hyperacute: R2 = 0.438; all: R2 = 0.548). CONCLUSION: The proposed multispectral imaging approach provides a combination of biomarkers that index early pathological changes after stroke in a clinical-feasible time and improves the assessment of the duration of cerebral infarction. SIGNIFICANCE: Developing accurate and efficient neuroimaging techniques to provide sensitive biomarkers for prediction of stroke onset time is of great importance for maximizing the proportion of patients eligible for therapeutic intervention. The proposed method provides a clinically feasible tool for the assessment of symptom onset time post ischemic stroke, which will help guide time-sensitive clinical management.

Predicting the Onset of Ischemic Stroke With Fast High-Resolution 3D MR Spectroscopic Imaging.

BACKGROUND: Neurometabolite concentrations provide a direct index of infarction progression in stroke. However, their relationship with stroke onset time remains unclear. PURPOSE: To assess the temporal dynamics of N-acetylaspartate (NAA), creatine, choline, and lactate and estimate their value in predicting early (<6 hours) vs. late (6-24 hours) hyperacute stroke groups. STUDY TYPE: Cross-sectional cohort. POPULATION: A total of 73 ischemic stroke patients scanned at 1.8-302.5 hours after symptom onset, including 25 patients with follow-up scans. FIELD STRENGTH/SEQUENCE: A 3 T/magnetization-prepared rapid acquisition gradient echo sequence for anatomical imaging, diffusion-weighted imaging and fluid-attenuated inversion recovery imaging for lesion delineation, and 3D MR spectroscopic imaging (MRSI) for neurometabolic mapping. ASSESSMENT: Patients were divided into hyperacute (0-24 hours), acute (24 hours to 1 week), and subacute (1-2 weeks) groups, and into early (<6 hours) and late (6-24 hours) hyperacute groups. Bayesian logistic regression was used to compare classification performance between early and late hyperacute groups by using different combinations of neurometabolites as inputs. STATISTICAL TESTS: Linear mixed effects modeling was applied for group-wise comparisons between NAA, creatine, choline, and lactate. Pearson's correlation analysis was used for neurometabolites vs. time. P < 0.05 was considered statistically significant. RESULTS: Lesional NAA and creatine were significantly lower in subacute than in acute stroke. The main effects of time were shown on NAA (F = 14.321) and creatine (F = 12.261). NAA was significantly lower in late than early hyperacute patients, and was inversely related to time from symptom onset across both groups (r = -0.440). The decrease of NAA and increase of lactate were correlated with lesion volume (NAA: r = -0.472; lactate: r = 0.366) in hyperacute stroke. Discrimination was improved by combining NAA, creatine, and choline signals (area under the curve [AUC] = 0.90). DATA CONCLUSION: High-resolution 3D MRSI effectively assessed the neurometabolite changes and discriminated early and late hyperacute stroke lesions. EVIDENCE LEVEL: 1. TECHNICAL EFFICACY: Stage 2.

Simultaneous Mapping of Water Diffusion Coefficients and Metabolite Distributions of the Brain Using MR Spectroscopic Imaging Without Water Suppression.

OBJECTIVE: To simultaneously map water diffusion coefficients and metabolite distributions of the brain in magnetic resonance spectroscopic imaging (MRSI) experiments within a clinically feasible time. METHODS: A diffusion-preparation module was introduced in water-unsuppressed MRSI acquisition sequence to generate diffusion weighting of the water signals. Fast spatiospectral encodings were achieved using echo-planar spectroscopic imaging readouts with blipped phase encodings for sparse sampling. Navigator signals were embedded in the data acquisition sequence, which were used for detection of data corrupted by physiological motion in the diffusion preparation period. In data processing, a novel model-based method was developed to effectively use sparse (k, t)-space spectroscopic signals for reconstruction of the spatial distributions of water diffusion coefficients and metabolite concentrations. RESULTS: Both phantom experiments and in vivo experiments were carried out to evaluate the feasibility and performance of the proposed method. In an 8-minute scan, diffusion weighted images and apparent diffusion coefficients map at 2.0×1.0×1.0 mm3 were obtained simultaneously with metabolite maps at 2.0×3.0×3.0 mm3 nominal resolution. CONCLUSION: We demonstrated the feasibility of using the unsuppressed water signals from MRSI experiments to map the water diffusion coefficients of brain tissues and proposed a novel method to achieve simultaneous mapping of water diffusion coefficients and metabolite distributions. SIGNIFICANCE: The proposed method provides a unique imaging tool for simultaneous diffusion and metabolic imaging. This method is expected to be useful for various brain imaging applications.

JSENSE-Pro: Joint sensitivity estimation and image reconstruction in parallel imaging using pre-learned subspaces of coil sensitivity functions.

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 T2 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.

T 2 ' mapping of the brain from water-unsuppressed 1 H-MRSI and turbo spin-echo data.

PURPOSE: To obtain high-quality T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ maps of brain tissues from water-unsuppressed magnetic resonance spectroscopic imaging (MRSI) and turbo spin-echo (TSE) data. METHODS: T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ mapping can be achieved using T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping from water-unsuppressed MRSI data and T 2 $$ {\mathrm{T}}_2 $$ mapping from TSE data. However, T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping often suffers from signal dephasing and distortions caused by B 0 $$ {\mathrm{B}}_0 $$ field inhomogeneity; T 2 $$ {\mathrm{T}}_2 $$ measurements may be biased due to system imperfections, especially for T 2 $$ {\mathrm{T}}_2 $$ -weighted image with small number of TEs. In this work, we corrected the B 0 $$ {\mathrm{B}}_0 $$ field inhomogeneity effect on T 2 * $$ {\mathrm{T}}_2^{\ast } $$ mapping using a subspace model-based method, incorporating pre-learned spectral basis functions of the water signals. T 2 $$ {\mathrm{T}}_2 $$ estimation bias was corrected using a TE-adjustment method, which modeled the deviation between measured and reference T 2 $$ {\mathrm{T}}_2 $$ decays as TE shifts. RESULTS: In vivo experiments were performed to evaluate the performance of the proposed method. High-quality T 2 * $$ {\mathrm{T}}_2^{\ast } $$ maps were obtained in the presence of large field inhomogeneity in the prefrontal cortex. Bias in T 2 $$ {\mathrm{T}}_2 $$ measurements obtained from TSE data was effectively reduced. Based on the T 2 * $$ {\mathrm{T}}_2^{\ast } $$ and T 2 $$ {\mathrm{T}}_2 $$ measurements produced by the proposed method, high-quality T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ maps were obtained, along with neurometabolite maps, from MRSI and TSE data that were acquired in about 9 min. The results obtained from acute stroke and glioma patients demonstrated the feasibility of the proposed method in the clinical setting. CONCLUSIONS: High-quality T 2 ' $$ {\mathrm{T}}_2^{\prime } $$ maps can be obtained from water-unsuppressed 1 H-MRSI and TSE data using the proposed method. With further development, this method may lay a foundation for simultaneously imaging oxygenation and neurometabolic alterations of brain disorders.

High-Resolution Label-Free Molecular Imaging of Brain Tumor.

Molecular imaging has long been recognized as an important tool for diagnosis, characterization, and monitoring of treatment responses of brain tumors. Magnetic resonance spectroscopic imaging (MRSI) is a label-free molecular imaging technique capable of mapping metabolite distributions non-invasively. Several metabolites detectable by MRSI, including Choline, Lactate and N-Acetyl Aspartate, have been proved useful biomarkers for brain tumor characterization. However, clinical application of MRSI has been limited by poor resolution, small spatial coverage, low signal-to-noise ratio and long scan time. This work presents a novel MRSI method for fast, high-resolution metabolic imaging of brain tumor. This method synergistically integrates fast acquisition sequence, sparse sampling, subspace modeling and machine learning to enable 3D mapping of brain metabolites with a spatial resolution of 2.0×3.0×3.0 mm3 in a 7-minute scan. Experimental results obtained from patients with diagnosed brain tumor showed great promise for capturing small-size tumors and revealing intra-tumor metabolic heterogeneities.Clinical Relevance - This paper presents a novel technique for label-free molecular imaging of brain tumor. With further development, this technology may enable many potential clinical applications, from tumor detection, characterization, to assessment of treatment efficacy.

Improved estimation of myelin water fractions with learned parameter distributions.

PURPOSE: To improve estimation of myelin water fraction (MWF) in the brain from multi-echo gradient-echo imaging data. METHODS: A systematic sensitivity analysis was first conducted to characterize the conventional exponential models used for MWF estimation. A new estimation method was then proposed for improved estimation of MWF from practical gradient-echo imaging data. The proposed method uses an extended signal model that includes a finite impulse response filter to compensate for practical signal variations. This new model also enables the use of prelearned parameter distributions as well as low-rank signal structures to improve parameter estimation. The resulting parameter estimation problem was solved optimally in the Bayesian sense. RESULTS: Our sensitivity analysis results showed that the conventional exponential models were very sensitive to measurement noise and modeling errors. Our simulation and experimental results showed that our proposed method provided a substantial improvement in reliability, reproducibility, and robustness of MWF estimates over the conventional methods. Clinical results obtained from stroke patients indicated that the proposed method, with its improved capability, could reveal the loss of myelin in lesions, demonstrating its translational potentials. CONCLUSION: This paper addressed the problem of robust MWF estimation from gradient-echo imaging data. A new method was proposed to provide improved MWF estimation in the presence of significant noise and modeling errors. The performance of the proposed method has been evaluated using both simulated and experimental data, showing significantly improved robustness over the existing methods. The proposed method may prove useful for quantitative myelin imaging in clinical applications.

Machine Learning-Enabled High-Resolution Dynamic Deuterium MR Spectroscopic Imaging.

Deuterium magnetic resonance spectroscopic imaging (DMRSI) has recently been recognized as a potentially powerful tool for noninvasive imaging of brain energy metabolism and tumor. However, the low sensitivity of DMRSI has significantly limited its utility for both research and clinical applications. This work presents a novel machine learning-based method to address this limitation. The proposed method synergistically integrates physics-based subspace modeling and data-driven deep learning for effective denoising, making high-resolution dynamic DMRSI possible. Specifically, a novel subspace model was used to represent the dynamic DMRSI signals; deep neural networks were trained to capture the low-dimensional manifolds of the spectral and temporal distributions of practical dynamic DMRSI data. The learned subspace and manifold structures were integrated via a regularization formulation to remove measurement noise. Theoretical analysis, computer simulations, and in vivo experiments have been conducted to demonstrate the denoising efficacy of the proposed method which enabled high-resolution imaging capability. The translational potential was demonstrated in tumor-bearing rats, where the Warburg effect associated with cancer metabolism and tumor heterogeneity were successfully captured. The new method may not only provide an effective tool to enhance the sensitivity of DMRSI for basic research and clinical applications but also provide a framework for denoising other spatiospectral data.

High-resolution sodium imaging using anatomical and sparsity constraints for denoising and recovery of novel features.

PURPOSE: To develop and evaluate a novel method for reconstruction of high-quality sodium MR images from noisy, limited k-space data. THEORY AND METHODS: A novel reconstruction method was developed for reconstruction of high-quality sodium images from noisy, limited k-space data. This method is based on a novel image model that contains a motion-compensated generalized series model and a sparse model. The motion-compensated generalized series model enables effective use of anatomical information from a proton image for denoising and resolution enhancement of sodium data, whereas the sparse model enables high-resolution reconstruction of sodium-dependent novel features. The underlying model estimation problems were solved efficiently using convex optimization algorithms. RESULTS: The proposed method has been evaluated using both simulation and experimental data obtained from phantoms, healthy human volunteers, and tumor patients. Results showed a substantial improvement in spatial resolution and SNR over state-of-the-art reconstruction methods, including compressed sensing and anatomically constrained reconstruction methods. Quantitative tissue sodium concentration maps were obtained from both healthy volunteers and brain tumor patients. These tissue sodium concentration maps showed improved lesion fidelity and allowed accurate interrogation of small targets. CONCLUSION: A new method has been developed to obtain high-resolution sodium images with good SNR at 3 T. The proposed method makes effective use of anatomical prior information for denoising, while using a sparse model synergistically to recover sodium-dependent novel features. Experimental results have been obtained to demonstrate the feasibility of achieving high-quality tissue sodium concentration maps and their potential for improved detection of spatially heterogeneous responses of tumor to treatment.

Accelerated J-resolved 1 H-MRSI with limited and sparse sampling of ( k,t1,t2 -space.

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,t1,t2 -space in variable density, where t1 and t2 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 mm3 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,t1,t2 -space and subspace modeling. With further development, the proposed method may enable high-resolution mapping of brain metabolites and neurotransmitters in clinical applications.

Accelerating T2 mapping of the brain by integrating deep learning priors with low-rank and sparse modeling.

PURPOSE: To accelerate T2 mapping with highly sparse sampling by integrating deep learning image priors with low-rank and sparse modeling. METHODS: The proposed method achieves high-speed T2 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 T2 -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 T2 maps at the resolution of 0.9 × 0.9 × 3.0 mm3 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 T2 -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 T2 mapping.

Simultaneous QSM and metabolic imaging of the brain using SPICE: Further improvements in data acquisition and processing.

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 mm3 ). 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 mm3 for metabolic imaging, and 3) sampling outer k-space sparsely to achieve spatial resolution of 1.0 × 1.0 × 1.9 mm3 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 mm3 resolution and metabolic maps at 3.0 × 3.0 × 3.0 mm3 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.

Fast high-resolution metabolic imaging of acute stroke with 3D magnetic resonance spectroscopy.

Impaired oxygen and cellular metabolism is a hallmark of ischaemic injury in acute stroke. Magnetic resonance spectroscopic imaging (MRSI) has long been recognized as a potentially powerful tool for non-invasive metabolic imaging. Nonetheless, long acquisition time, poor spatial resolution, and narrow coverage have limited its clinical application. Here we investigated the feasibility and potential clinical utility of rapid, high spatial resolution, near whole-brain 3D metabolic imaging based on a novel MRSI technology. In an 8-min scan, we simultaneously obtained 3D maps of N-acetylaspartate and lactate at a nominal spatial resolution of 2.0 × 3.0 × 3.0 mm3 with near whole-brain coverage from a cohort of 18 patients with acute ischaemic stroke. Serial structural and perfusion MRI was used to define detailed spatial maps of tissue-level outcomes against which high-resolution metabolic changes were evaluated. Within hypoperfused tissue, the lactate signal was higher in areas that ultimately infarcted compared with those that recovered (P < 0.0001). Both lactate (P < 0.0001) and N-acetylaspartate (P < 0.001) differed between infarcted and other regions. Within the areas of diffusion-weighted abnormality, lactate was lower where recovery was observed compared with elsewhere (P < 0.001). This feasibility study supports further investigation of fast high-resolution MRSI in acute stroke.