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 required to reconstruct undersampled data. Incoherence between L and S is required for robust separation of background and dynamic components. Multicoil L+S reconstruction is formulated using a convex optimization approach, where the nuclear norm is used to enforce low rank in L and the l₁ norm is used to enforce sparsity in S. Feasibility of the L+S reconstruction was tested in several dynamic MRI experiments with true acceleration, including cardiac perfusion, cardiac cine, time‐resolved angiography, and abdominal and breast perfusion using Cartesian and radial sampling. Results The L+S model increased compressibility of dynamic MRI data and thus enabled high‐acceleration factors. The inherent background separation improved background suppression performance compared to conventional data subtraction, which is sensitive to motion. Conclusion The high acceleration and background separation enabled by L+S promises to enhance spatial and temporal resolution and to enable background suppression without the need of subtraction or modeling. Magn Reson Med 73:1125–1136, 2015.

Purpose: To develop a fast and flexible free‐breathing dynamic volumetric MRI technique, iterative Golden‐angle RAdial Sparse Parallel MRI (iGRASP), that combines compressed sensing, parallel imaging, and golden‐angle radial sampling.Methods: Radial k‐space data are acquired continuously using the golden‐angle scheme and sorted into time series by grouping an arbitrary number of consecutive spokes into temporal frames. An iterative reconstruction procedure is then performed on the undersampled time series where joint multicoil sparsity is enforced by applying a total‐variation constraint along the temporal dimension. Required coil‐sensitivity profiles are obtained from the time‐averaged data.Results: iGRASP achieved higher acceleration capability than either parallel imaging or coil‐by‐coil compressed sensing alone. It enabled dynamic volumetric imaging with high spatial and temporal resolution for various clinical applications, including free‐breathing dynamic contrast‐enhanced imaging in the abdomen of both adult and pediatric patients, and in the breast and neck of adult patients.Conclusion: The high performance and flexibility provided by iGRASP can improve clinical studies that require robustness to motion and simultaneous high spatial and temporal resolution. Magn Reson Med 72:707–717, 2014.

First‐pass cardiac perfusion MRI is a natural candidate for compressed sensing acceleration since its representation in the combined temporal Fourier and spatial domain is sparse and the required incoherence can be effectively accomplished by k‐t random undersampling. However, the required number of samples in practice (three to five times the number of sparse coefficients) limits the acceleration for compressed sensing alone. Parallel imaging may also be used to accelerate cardiac perfusion MRI, with acceleration factors ultimately limited by noise amplification. In this work, compressed sensing and parallel imaging are combined by merging the k‐t SPARSE technique with sensitivity encoding (SENSE) reconstruction to substantially increase the acceleration rate for perfusion imaging. We also present a new theoretical framework for understanding the combination of k‐t SPARSE with SENSE based on distributed compressed sensing theory. This framework, which identifies parallel imaging as a distributed multisensor implementation of compressed sensing, enables an estimate of feasible acceleration for the combined approach. We demonstrate feasibility of 8‐fold acceleration in vivo with whole‐heart coverage and high spatial and temporal resolution using standard coil arrays. The method is relatively insensitive to respiratory motion artifacts and presents similar temporal fidelity and image quality when compared to Generalized autocalibrating partially parallel acquisitions (GRAPPA) with 2‐fold acceleration. Magn Reson Med, 2010.

For patients with impaired breath‐hold capacity and/or arrhythmias, real‐time cine MRI may be more clinically useful than breath‐hold cine MRI. However, commercially available real‐time cine MRI methods using parallel imaging typically yield relatively poor spatio‐temporal resolution due to their low image acquisition speed. We sought to achieve relatively high spatial resolution (∼2.5 × 2.5 mm²) and temporal resolution (∼40 ms), to produce high‐quality real‐time cine MR images that could be applied clinically for wall motion assessment and measurement of left ventricular function. In this work, we present an eightfold accelerated real‐time cardiac cine MRI pulse sequence using a combination of compressed sensing and parallel imaging (k‐t SPARSE‐SENSE). Compared with reference, breath‐hold cine MRI, our eightfold accelerated real‐time cine MRI produced significantly worse qualitative grades (1–5 scale), but its image quality and temporal fidelity scores were above 3.0 (adequate) and artifacts and noise scores were below 3.0 (moderate), suggesting that acceptable diagnostic image quality can be achieved. Additionally, both eightfold accelerated real‐time cine and breath‐hold cine MRI yielded comparable left ventricular function measurements, with coefficient of variation <10% for left ventricular volumes. Our proposed eightfold accelerated real‐time cine MRI with k–t SPARSE‐SENSE is a promising modality for rapid imaging of myocardial function. J. Magn. Reson. Imaging 2013.

Objective The objectives of this study were to develop a new method for free-breathing contrast-enhanced multiphase liver magnetic resonance imaging (MRI) using a combination of compressed sensing, parallel imaging, and radial k-space sampling and to demonstrate the feasibility of this method by performing image quality comparison with breath-hold cartesian T1-weighted (conventional) postcontrast acquisitions in healthy participants. Materials and Methods This Health Insurance Portability and Accountability Act–compliant prospective study received approval from the institutional review board. Eight participants underwent 3 separate contrast-enhanced fat-saturated T1-weighted gradient-echo MRI examinations with matching imaging parameters: conventional breath-hold examination with cartesian k-space sampling volumetric interpolate breath hold examination (BH-VIBE) and free-breathing acquisitions with interleaved angle-bisection and continuous golden-angle radial sampling schemes. Interleaved angle-bisection and golden-angle data from each 100 consecutive spokes were reconstructed using a combination of compressed sensing and parallel imaging (interleaved-angle radial sparse parallel [IARASP] and golden-angle radial sparse parallel [GRASP]) to generate multiple postcontrast phases. Arterial- and venous-phase BH-VIBE, IARASP, and GRASP reconstructions were evaluated by 2 radiologists in a blinded fashion. The readers independently assessed quality of enhancement (QE), overall image quality (IQ), and other parameters of image quality on a 5-point scale, with the highest score indicating the most desirable examination. Mixed model analysis of variance was used to compare each measure of image quality. Results Images of BH-VIBE and GRASP had significantly higher QE and IQ values compared with IARASP for both phases (P < 0.05). The differences in QE between BH-VIBE and GRASP for the arterial and venous phases were not significant (P > 0.05). Although GRASP had lower IQ score compared with BH-VIBE for the arterial (3.9 vs 4.8; P < 0.0001) and venous (4.2 vs 4.8; P = 0.005) phases, GRASP received IQ scores of 3 or more in all participants, which was consistent with acceptable or better diagnostic image quality. Conclusion Contrast-enhanced multiphase liver MRI of diagnostic quality can be performed during free breathing using a combination of compressed sensing, parallel imaging, and golden-angle radial sampling.