Varying kernel-extent gridding reconstruction for undersampled variable-density spirals

Varying kernel-extent gridding reconstruction for undersampled variable-density spirals.

Nonuniform, non-Cartesian k-space trajectories enable fast scanning with reduced motion and flow artifacts. In such cases, the data are usually convolved with a kernel and resampled onto a Cartesian grid before reconstruction. For trajectories such as undersampled variable-density spirals, the mainlobe width of the kernel for undersampled high spatial frequencies has to be larger to limit the a...

Faster Imaging with Randomly Perturbed, Undersampled Spirals and |L|_1 Reconstruction

M. Lustig, J. H. Lee, D. L. Donoho, J. M. Pauly Electrical Engineering, Stanford University, Stanford, CA, United States, Statistics, Stanford University, Stanford, CA, United States Introduction We propose a fast imaging method based on undersampled k-space spiral sampling and non-linear reconstruction. Our approach is inspired by theoretical results in sparse signal recovery [1,2] showing tha...

Varying kernel density estimation on R+

Varying Kernel Density Estimator for a Positive Time Series

This paper analyzes the large sample of a varying kernel density estimator of the marginal density of a nonnegative stationary and ergodic time series that is also strongly mixing. In particular we obtain an approximation for bias, mean square error and establish asymptotic normality of this density estimator.

Deep learning for undersampled MRI reconstruction

This paper presents a deep learning method for faster magnetic resonance imaging (MRI) by reducing k-space data with sub-Nyquist sampling strategies and provides a rationale for why the proposed approach works well. Uniform subsampling is used in the time-consuming phase-encoding direction to capture high-resolution image information, while permitting the image-folding problem dictated by the P...