Sparse BLIP: BLind Iterative Parallel imaging reconstruction using compressed sensing
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Summary
To develop a sensitivity‐based parallel imaging reconstruction method to reconstruct iteratively both the coil sensitivities and MR image simultaneously based on their prior information.
- Type
- article
- Published
- 2014-02-01
- Cited by
- 39
- References
- 41
- Access
- Open access
- OpenAlex
- https://openalex.org/W1913250981
- Semantic Scholar
- https://api.semanticscholar.org/CorpusID:17751430
Keywords
Undersampling, Compressed sensing, Iterative reconstruction, Computer science, Iterative method
References
- Image reconstruction by regularized nonlinear inversion—Joint estimation of coil sensitivities and image content
- Compressed sensing parallel Magnetic Resonance Imaging
- Compressed Sensing
- Parallel MRI Using Phased Array Coils
- Convergence of the alternating minimization algorithm for blind deconvolution
- Generalized sampling expansion
- Partially parallel imaging with localized sensitivities (PILS)
- Sensitivity encoding reconstruction with nonlocal total variation regularization
- SPIRiT: Iterative Self-consistent Parallel Imaging Reconstruction from Arbitrary k-Space
- A Unified Approach to Superresolution and Multichannel Blind Deconvolution
- AUTO-SMASH: A self-calibrating technique for SMASH imaging
- On optimality of parallel MRI reconstruction in k‐space
- k-t CSPI: A dynamic MRI reconstruction framework for combining compressed sensing and parallel imaging
- Inverse electrocardiography by simultaneous imposition of multiple constraints
- Sparse MRI: The application of compressed sensing for rapid MR imaging
- Sparsesense: Application of compressed sensing in parallel MRI
- Prior estimate‐based compressed sensing in parallel MRI
- An image space approach to Cartesian based parallel MR imaging with total variation regularization
- Generalized autocalibrating partially parallel acquisitions (GRAPPA)
- Parallel Imaging with Nonlinear Reconstruction using Variational Penalties
Cited by
- Incorporating reference in parallel imaging and compressed sensing
- P-LORAKS: Low-Rank Modeling of Local k-Space Neighborhoods with Parallel Imaging Data
- Multi-contrast magnetic resonance image reconstruction
- An enhanced approach for simultaneous image reconstruction and sensitivity map estimation in partially parallel imaging
- Accelerated magnetic resonance imaging using the sparsity of multi-channel coil images.
- Efficient Compressed Sensing SENSE pMRI Reconstruction With Joint Sparsity Promotion
- Optimization of Regularization Parameters in Compressed Sensing of Magnetic Resonance Angiography: Can Statistical Image Metrics Mimic Radiologists' Perception?
- Compressed Sensing and Parallel Acquisition
- Optimal sparse recovery for multi-sensor measurements
- Efficient compressed sensing SENSE parallel MRI reconstruction with joint sparsity promotion and mutual incoherence enhancement
- Mean Squared Error Based Excitation Pattern Design for Parallel Transmit and Receive SENSE MRI Image Reconstruction
- Advances in magnetic resonance imaging reconstruction methods incorporating prior knowledge
- Compressed sensing MRI reconstruction from 3D multichannel data using GPUs
- Subsampled Multichannel Blind Deconvolution by Sparse Power Factorization
- Learning Joint-Sparse Codes for Calibration-Free Parallel MR Imaging
- Incorporating reference guided priors into calibrationless parallel imaging reconstruction.
- Calibrationless Oscar-Based Image Reconstruction in Compressed Sensing Parallel MRI
- Online MR image reconstruction for compressed sensing acquisition in T2* imaging
- Advanced Image Reconstruction Algorithms in Parallel Magnetic Resonance Imaging
- Compressive imaging with total variation regularization and application to auto-calibration of parallel magnetic resonance imaging
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