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We introduce an regularized reconstruction scheme to recover dynamic imaging datasets with significant inter frame motion from undersampled Fourier data. The proposed nonlocal regularization penalty is an unweighted sum of distances between image patch pairs in the 3-D dataset. We use robust distance metrics to compute the distance between image patches; these metrics encourage the smoothing between similar patches, while discouraging the averaging of dissimilar patches. Hence, this algorithm is capable of exploiting the similarities between patch pairs in adjacent frames even when they are well separated due to motion, eventhough it does not perform explicit motion estimation. Unlike current non-local regularization schemes, the proposed penalty does not need good initial guesses to estimate the weights. Hence, this approach is readily applicable to accelerated dynamic imaging problems, where good initial guesses are challenging to obtain. The validation of the proposed scheme on numerical phantoms and dynamic MRI datasets demonstrate the superior performance of the proposed scheme over current dynamic imaging schemes. [ABSTRACT FROM PUBLISHER]

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. [ABSTRACT FROM PUBLISHER]

Multi-shot sequences are often combined with techniques such as parallel imaging to achieve high fidelity images. For non-Cartesian sampling schemes, the reconstruction of such data becomes extremely computationally intensive. The problem of motion compensated SENSE reconstruction of non-Cartesian diffusion weighted images falls into a similar setting, when phase corrections are involved. Here we propose a new pipeline for the fast reconstruction of such data. The large array of composite sensitivity functions are replaced by a low-dimensional set of virtual sensitivity functions using a principal component analysis, thus enabling the evaluation of very few Fourier transforms. The time consuming gridding steps are replaced by a more efficient multiplication in the k-space, enabling further simplifications. Significant acceleration of reconstruction time is shown to be achieved with the new scheme. The algorithm in the general setting can accelerate SENSE reconstruction for large coil arrays. [ABSTRACT FROM PUBLISHER]