Sajan Goud Lingala

Accelerated Dynamic MRI Exploiting Sparsity and Low-Rank Structure: k-t SLR

We introduce a novel algorithm to reconstruct dynamic magnetic resonance imaging (MRI) data from under-sampled k-t space data. In contrast to classical model based cine MRI schemes that rely on the sparsity or banded structure in Fourier space, we use the compact representation of the data in the Karhunen Louve transform (KLT) domain to exploit the correlations in the dataset. The use of the data-dependent KL transform makes our approach ideally suited to a range of dynamic imaging problems, even when the motion is not periodic. In comparison to current KLT-based methods that rely on a two-step approach to first estimate the basis functions and then use it for reconstruction, we pose the problem as a spectrally regularized matrix recovery problem. By simultaneously determining the temporal basis functions and its spatial weights from the entire measured data, the proposed scheme is capable of providing high quality reconstructions at a range of accelerations. In addition to using the compact representation in the KLT domain, we also exploit the sparsity of the data to further improve the recovery rate. Validations using numerical phantoms and in vivo cardiac perfusion MRI data demonstrate the significant improvement in performance offered by the proposed scheme over existing methods.

Blind Compressive Sensing Dynamic MRI

We propose a novel blind compressive sensing (BCS) frame work to recover dynamic magnetic resonance images from undersampled measurements. This scheme models the dynamic signal as a sparse linear combination of temporal basis functions, chosen from a large dictionary. In contrast to classical compressed sensing, the BCS scheme simultaneously estimates the dictionary and the sparse coefficients from the undersampled measurements. Apart from the sparsity of the coefficients, the key difference of the BCS scheme with current low rank methods is the nonorthogonal nature of the dictionary basis functions. Since the number of degrees-of-freedom of the BCS model is smaller than that of the low-rank methods, it provides improved reconstructions at high acceleration rates. We formulate the reconstruction as a constrained optimization problem; the objective function is the linear combination of a data consistency term and sparsity promoting l1 prior of the coefficients. The Frobenius norm dictionary constraint is used to avoid scale ambiguity. We introduce a simple and efficient majorize-minimize algorithm, which decouples the original criterion into three simpler subproblems. An alternating minimization strategy is used, where we cycle through the minimization of three simpler problems. This algorithm is seen to be considerably faster than approaches that alternates between sparse coding and dictionary estimation, as well as the extension of K-SVD dictionary learning scheme. The use of the l1 penalty and Frobenius norm dictionary constraint enables the attenuation of insignificant basis functions compared to the l0 norm and column norm constraint assumed in most dictionary learning algorithms; this is especially important since the number of basis functions that can be reliably estimated is restricted by the available measurements. We also observe that the proposed scheme is more robust to local minima compared to K-SVD method, which relies on greedy sparse coding. Our phase transition experiments demonstrate that the BCS scheme provides much better recovery rates than classical Fourier-based CS schemes, while being only marginally worse than the dictionary aware setting. Since the overhead in additionally estimating the dictionary is low, this method can be very useful in dynamic magnetic resonance imaging applications, where the signal is not sparse in known dictionaries. We demonstrate the utility of the BCS scheme in accelerating contrast enhanced dynamic data. We observe superior reconstruction performance with the BCS scheme in comparison to existing low rank and compressed sensing schemes.

A Fast Majorize–Minimize Algorithm for the Recovery of Sparse and Low-Rank Matrices

We introduce a novel algorithm to recover sparse and low-rank matrices from noisy and undersampled measurements. We pose the reconstruction as an optimization problem, where we minimize a linear combination of data consistency error, nonconvex spectral penalty, and nonconvex sparsity penalty. We majorize the nondifferentiable spectral and sparsity penalties in the criterion by quadratic expressions to realize an iterative three-step alternating minimization scheme. Since each of these steps can be evaluated either analytically or using fast schemes, we obtain a computationally efficient algorithm. We demonstrate the utility of the algorithm in the context of dynamic magnetic resonance imaging (MRI) reconstruction from sub-Nyquist sampled measurements. The results show a significant improvement in signal-to-noise ratio and image quality compared with classical dynamic imaging algorithms. We expect the proposed scheme to be useful in a range of applications including video restoration and multidimensional MRI.

Recommendations for real-time speech MRI.

Real‐time magnetic resonance imaging (RT‐MRI) is being increasingly used for speech and vocal production research studies. Several imaging protocols have emerged based on advances in RT‐MRI acquisition, reconstruction, and audio‐processing methods. This review summarizes the state‐of‐the‐art, discusses technical considerations, and provides specific guidance for new groups entering this field. We provide recommendations for performing RT‐MRI of the upper airway. This is a consensus statement stemming from the ISMRM‐endorsed Speech MRI summit held in Los Angeles, February 2014. A major unmet need identified at the summit was the need for consensus on protocols that can be easily adapted by researchers equipped with conventional MRI systems. To this end, we provide a discussion of tradeoffs in RT‐MRI in terms of acquisition requirements, a priori assumptions, artifacts, computational load, and performance for different speech tasks. We provide four recommended protocols and identify appropriate acquisition and reconstruction tools. We list pointers to open‐source software that facilitate implementation. We conclude by discussing current open challenges in the methodological aspects of RT‐MRI of speech. J. MAGN. RESON. IMAGING 2016;43:28–44.

Deformation Corrected Compressed Sensing (DC-CS): A Novel Framework for Accelerated Dynamic MRI

We propose a novel deformation corrected compressed sensing (DC-CS) framework to recover contrast enhanced dynamic magnetic resonance images from undersampled measurements. We introduce a formulation that is capable of handling a wide class of sparsity/compactness priors on the deformation corrected dynamic signal. In this work, we consider example compactness priors such as sparsity in temporal Fourier domain, sparsity in temporal finite difference domain, and nuclear norm penalty to exploit low rank structure. Using variable splitting, we decouple the complex optimization problem to simpler and well understood sub problems; the resulting algorithm alternates between simple steps of shrinkage-based denoising, deformable registration, and a quadratic optimization step. Additionally, we employ efficient continuation strategies to reduce the risk of convergence to local minima. The decoupling enabled by the proposed scheme enables us to apply this scheme to contrast enhanced MRI applications. Through experiments on numerical phantom and in vivo myocardial perfusion MRI datasets, we observe superior image quality of the proposed DC-CS scheme in comparison to the classical k-t FOCUSS with motion estimation/correction scheme, and demonstrate reduced motion artifacts over classical compressed sensing schemes that utilize the compact priors on the original deformation uncorrected signal.

A blind compressive sensing frame work for accelerated dynamic MRI

We propose a novel blind compressive sensing (BCS) frame work to recover dynamic images from under-sampled measurements. This scheme models the the dynamic signal as a sparse linear combination of temporal basis functions, chosen from a large dictionary. The dictionary and the sparse coefficients are simultaneously estimated from the under-sampled measurements. Since the number of degrees of freedom of this model is much smaller than that of current low-rank methods, this scheme is expected to provide improved reconstructions for datasets with considerable inter-frame motion. We develop an efficient majorize-minimize algorithm to solve for the dynamic images. We use a continuation strategy to minimize the convergence of the algorithm to local minima. Numerical comparisons of the BCS scheme with low-rank methods demonstrate the significant improvement in performance in the presence of motion.

GOCART: GOlden-angle CArtesian randomized time-resolved 3D MRI

PURPOSE To develop and evaluate a novel 3D Cartesian sampling scheme which is well suited for time-resolved 3D MRI using parallel imaging and compressed sensing. METHODS The proposed sampling scheme, termed GOlden-angle CArtesian Randomized Time-resolved (GOCART) 3D MRI, is based on golden angle (GA) Cartesian sampling, with random sampling of the ky-kz phase encode locations along each Cartesian radial spoke. This method was evaluated in conjunction with constrained reconstruction of retrospectively and prospectively undersampled in-vivo dynamic contrast enhanced (DCE) MRI data and simulated phantom data. RESULTS In in-vivo retrospective studies and phantom simulations, images reconstructed from phase encodes defined by GOCART were equal to or superior to those with Poisson disc or GA sampling schemes. Typical GOCART sampling tables were generated in <100ms. GOCART has also been successfully utilized prospectively to produce clinically valuable whole-brain DCE-MRI images. CONCLUSION GOCART is a practical and efficient sampling scheme for time-resolved 3D MRI. It shows great potential for highly accelerated DCE-MRI and is well suited to modern reconstruction methods such as parallel imaging and compressed sensing.

Accelerating free breathing myocardial perfusion MRI using multi coil radial k − t SLR

The clinical utility of myocardial perfusion MR imaging (MPI) is often restricted by the inability of current acquisition schemes to simultaneously achieve high spatio-temporal resolution, good volume coverage, and high signal to noise ratio. Moreover, many subjects often find it difficult to hold their breath for sufficiently long durations making it difficult to obtain reliable MPI data. Accelerated acquisition of free breathing MPI data can overcome some of these challenges. Recently, an algorithm termed as k - t SLR has been proposed to accelerate dynamic MRI by exploiting sparsity and low rank properties of dynamic MRI data. The main focus of this paper is to further improve k - t SLR and demonstrate its utility in considerably accelerating free breathing MPI. We extend its previous implementation to account for multi-coil radial MPI acquisitions. We perform k - t sampling experiments to compare different radial trajectories and determine the best sampling pattern. We also introduce a novel augmented Lagrangian framework to considerably improve the algorithms convergence rate. The proposed algorithm is validated using free breathing rest and stress radial perfusion data sets from two normal subjects and one patient with ischemia. k - t SLR was observed to provide faithful reconstructions at high acceleration levels with minimal artifacts compared to existing MPI acceleration schemes such as spatio-temporal constrained reconstruction and k - t SPARSE/SENSE.

Accelerated whole-brain multi-parameter mapping using blind compressed sensing

To introduce a blind compressed sensing (BCS) framework to accelerate multi‐parameter MR mapping, and demonstrate its feasibility in high‐resolution, whole‐brain T1ρ and T2 mapping.

A fast and flexible MRI system for the study of dynamic vocal tract shaping.

The aim of this work was to develop and evaluate an MRI‐based system for study of dynamic vocal tract shaping during speech production, which provides high spatial and temporal resolution.