Purpose
To develop a subspace learning method for the recently proposed subspace‐based MRSI approach known as SPICE, and achieve ultrafast 1H‐MRSI of the brain.
Theory and Methods
A novel strategy is ...formulated to learn a low‐dimensional subspace representation of MR spectra from specially acquired training data and use the learned subspace for general MRSI experiments. Specifically, the subspace learning problem is formulated as learning “empirical” distributions of molecule‐specific spectral parameters (e.g., concentrations, lineshapes, and frequency shifts) by integrating physics‐based model and the training data. The learned spectral parameters and quantum mechanical simulation basis can then be combined to construct acquisition‐specific subspace for spatiospectral encoding and processing. High‐resolution MRSI acquisitions combining ultrashort‐TE/short‐TR excitation, sparse sampling, and the elimination of water suppression have been performed to evaluate the feasibility of the proposed method.
Results
The accuracy of the learned subspace and the capability of the proposed method in producing high‐resolution 3D 1H metabolite maps and high‐quality spatially resolved spectra (with a nominal resolution of ∼2.4 × 2.4 × 3 mm3 in 5 minutes) were demonstrated using phantom and in vivo studies. By eliminating water suppression, we are also able to extract valuable information from the water signals for data processing (B0 map, frequency drift, and coil sensitivity) as well as for mapping tissue susceptibility and relaxation parameters.
Conclusions
The proposed method enables ultrafast 1H‐MRSI of the brain using a learned subspace, eliminating the need of acquiring subject‐dependent navigator data (known as D1) in the original SPICE technique. It represents a new way to perform MRSI experiments and an important step toward practical applications of high‐resolution MRSI.
Purpose
To improve the estimation of coil sensitivity functions from limited auto‐calibration signals (ACS) in SENSE‐based reconstruction for brain imaging.
Methods
We propose to use deep learning to ...estimate coil sensitivity functions by leveraging information from previous scans obtained using the same RF receiver system. Specifically, deep convolutional neural networks were designed to learn an end‐to‐end mapping from the initial sensitivity to the high‐resolution counterpart. Sensitivity alignment was further proposed to reduce the geometric variation caused by different subject positions and imaging FOVs. Cross‐validation with a small set of datasets was performed to validate the learned neural network. Iterative SENSE reconstruction was adopted to evaluate the utility of the sensitivity functions from the proposed and conventional methods.
Results
The proposed method produced improved sensitivity estimates and SENSE reconstructions compared to the conventional methods in terms of aliasing and noise suppression with very limited ACS data. Cross‐validation with a small set of data demonstrated the feasibility of learning coil sensitivity functions for brain imaging. The network learned on the spoiled GRE data can be applied to predict sensitivity functions for spin‐echo and MPRAGE datasets.
Conclusion
A deep learning‐based method has been proposed for improving the estimation of coil sensitivity functions. Experimental results have demonstrated the feasibility and potential of the proposed method for improving SENSE‐based reconstructions especially when the ACS data are limited.
High-dimensional MR imaging often requires long data acquisition time, thereby limiting its practical applications. This paper presents a low-rank tensor based method for accelerated high-dimensional ...MR imaging using sparse sampling. This method represents high-dimensional images as low-rank tensors (or partially separable functions) and uses this mathematical structure for sparse sampling of the data space and for image reconstruction from highly undersampled data. More specifically, the proposed method acquires two datasets with complementary sampling patterns, one for subspace estimation and the other for image reconstruction; image reconstruction from highly undersampled data is accomplished by fitting the measured data with a sparsity constraint on the core tensor and a group sparsity constraint on the spatial coefficients jointly using the alternating direction method of multipliers. The usefulness of the proposed method is demonstrated in MRI applications; it may also have applications beyond MRI.
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.
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.
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.
Magnetic resonance parameter mapping (e.g., T 1 mapping, T 2 mapping, T 2 * mapping) is a valuable tool for tissue characterization. However, its practical utility has been limited due to long data ...acquisition time. This paper addresses this problem with a new model-based parameter mapping method. The proposed method utilizes a formulation that integrates the explicit signal model with sparsity constraints on the model parameters, enabling direct estimation of the parameters of interest from highly undersampled, noisy k-space data. An efficient greedy-pursuit algorithm is described to solve the resulting constrained parameter estimation problem. Estimation-theoretic bounds are also derived to analyze the benefits of incorporating sparsity constraints and benchmark the performance of the proposed method. The theoretical properties and empirical performance of the proposed method are illustrated in a T 2 mapping application example using computer simulations.
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