Novel Non-local Total Variation Regularization for Constrained Mr Reconstruction
نویسندگان
چکیده
NOVEL NON-LOCAL TOTAL VARIATION REGULARIZATION FOR CONSTRAINED MR RECONSTRUCTION Andres Saucedo, Stamatios Lefkimmiatis, Stanley Osher, and Kyunghyun Sung Department of Radiological Sciences, David Geffen School of Medicine, University of California Los Angeles, Los Angeles, California, United States, Biomedical Physics Interdepartmental Graduate Program, University of California Los Angeles, Los Angeles, California, United States, Department of Mathematics, University of California Los Angeles, Los Angeles, California, United States
منابع مشابه
Adaptive Frequency-domain Regularization for Sparse-data Tomography
A novel reconstruction technique, called Wiener Filtered Reconstruction Technique (WIRT), for sparse-data tomographic imaging is introduced. This six-step method applies a spatially varying constrained leastsquares filter combined with a regularization method based on total variation. The WIRT reconstruction is implemented in the frequency domain, where the information based on measurements and...
متن کاملParallel Mri Reconstruction Using Svd-and- Laplacian Transform Based Sparsity Regularization
The SENSE model with sparsity regularization acts as an unconstrained minimization problem to reconstruct the MRI, which obtain better reconstruction results than the traditional SENSE. To implement the sparsity constraints, discrete wavelet transform (DWT) and total variation (TV) are common exploited together to sparsify the MR image. In this paper, a novel sparsifying transform based on the ...
متن کاملInfimal convolution of total generalized variation functionals for dynamic MRI.
PURPOSE To accelerate dynamic MR applications using infimal convolution of total generalized variation functionals (ICTGV) as spatio-temporal regularization for image reconstruction. THEORY AND METHODS ICTGV comprises a new image prior tailored to dynamic data that achieves regularization via optimal local balancing between spatial and temporal regularity. Here it is applied for the first tim...
متن کاملLow-Rank Modeling of Local $k$-Space Neighborhoods (LORAKS) for Constrained MRI
Recent theoretical results on low-rank matrix reconstruction have inspired significant interest in low-rank modeling of MRI images. Existing approaches have focused on higher-dimensional scenarios with data available from multiple channels, timepoints, or image contrasts. The present work demonstrates that single-channel, single-contrast, single-timepoint k-space data can also be mapped to low-...
متن کاملMR Image Reconstruction Based on Iterative Split Bregman Algorithm and Nonlocal Total Variation
This paper introduces an efficient algorithm for magnetic resonance (MR) image reconstruction. The proposed method minimizes a linear combination of nonlocal total variation and least-square data-fitting term to reconstruct the MR images from undersampled k-space data. The nonlocal total variation is taken as the L 1-regularization functional and solved using Split Bregman iteration. The propos...
متن کامل