Low-rank and Sparse Matrix Decomposition with a-priori Knowledge for Dynamic 3D MRI Reconstruction
نویسندگان
چکیده
It has been recently shown that incorporating priori knowledge significantly improves the performance of basic compressive sensing based approaches. We have managed to successfully exploit this idea for recovering a matrix as a summation of a Low-rank and a Sparse component from compressive measurements. When applied to the problem of construction of 4D Cardiac MR image sequences in real-time from highly under-sampled k−space data, our proposed method achieves superior reconstruction quality compared to the other stateof-the-art methods.
منابع مشابه
Low-Rank and Sparse Matrix Decomposition for Accelerated Dynamic MRI with Separation of Background and Dynamic Components
Purpose: To apply the low-rank plus sparse (L+S) matrix decomposition model to reconstruct undersampled dynamic MRI as a superposition of background and dynamic components in various problems of clinical interest. Theory and Methods: The L+S model is natural to represent dynamic MRI data. Incoherence between k-t space (acquisition) and the singular vectors of L and the sparse domain of S is req...
متن کاملBeyond Low Rank + Sparse: Multi-scale Low Rank Reconstruction for Dynamic Contrast Enhanced Imaging
PURPOSE: Dynamic Contrast Enhanced (DCE) MRI is a powerful method that can provide comprehensive information to characterize lesions. High temporal resolution is often desired for 3D DCE, but at the cost of lower spatial resolution. Low rank / partial separable methods offer an effective way of balancing this tradeoff by exploiting spatio-temporal correlations of dynamic images. However, existi...
متن کاملLow-rank plus sparse matrix decomposition for accelerated dynamic MRI with separation of background and dynamic components.
PURPOSE To apply the low-rank plus sparse (L+S) matrix decomposition model to reconstruct undersampled dynamic MRI as a superposition of background and dynamic components in various problems of clinical interest. THEORY AND METHODS The L+S model is natural to represent dynamic MRI data. Incoherence between k-t space (acquisition) and the singular vectors of L and the sparse domain of S is req...
متن کاملA Unified Tensor Regression Framework for Calibrationless Dynamic, Multi-Channel MRI Reconstruction
TARGET AUDIENCE: Magnetic resonance image (MRI) reconstruction developers. PURPOSE: Advanced image reconstruction strategies often require explicit knowledge about the MRI acquisition system or target signal. For example, the GRAPPA [1] method for parallel MRI requires a kernel model of inter-coil k-space correlations that result from receiver sensitivity modulations; and kt-BLAST [2] requires ...
متن کاملLow-Rank and Sparse Decomposition Model for Accelerating Dynamic MRI Reconstruction
The reconstruction of dynamic magnetic resonance imaging (dMRI) from partially sampled k-space data has to deal with a trade-off between the spatial resolution and temporal resolution. In this paper, a low-rank and sparse decomposition model is introduced to resolve this issue, which is formulated as an inverse problem regularized by robust principal component analysis (RPCA). The inverse probl...
متن کامل