High-Resolution Cardiovascular MRI by Integrating Parallel Imaging With Low-Rank and Sparse Modeling

Abstract

Magnetic resonance imaging (MRI) has long been recognized as a powerful tool for cardiovascular imaging because of its unique potential to measure blood flow, cardiac wall motion, and tissue properties jointly. However, many clinical applications of cardiac MRI have been limited by low imaging speed. In this paper, we present a novel method to accelerate cardiovascular MRI through the integration of parallel imaging, low-rank modeling, and sparse modeling. This method consists of a novel image model and specialized data acquisition. Of particular novelty is the proposed low-rank model component, which is specially adapted to the particular low-rank structure of cardiovascular signals. Simulations and in vivo experiments were performed to evaluate the method, as well as an analysis of the low-rank structure of a numerical cardiovascular phantom. Cardiac imaging experiments were carried out on both human and rat subjects without the use of ECG or respiratory gating and without breath holds. The proposed method reconstructed 2-D human cardiac images up to 22 fps and 1.0 mm × 1.0 mm spatial resolution and 3-D rat cardiac images at 67 fps and 0.65 mm × 0.65 mm × 0.31 mm spatial resolution. These capabilities will enhance the practical utility of cardiovascular MRI.

High‐resolution 1H‐MRSI of the brain using SPICE: Data acquisition and image reconstruction

“…This choice is motivated by the advantages of such edgepreserving (or non-quadratic) penalties shown in recent developments for sparse sampling and denoising. Similar forms of regularization can also be found in the context of dynamic imaging (36)(37)(38)(39). For the nuisance signal component, an ℓ 2 regularization was used as in (29) for better conditioning and faster computation, although more sophisticated schemes can also be considered in the future.…”

Improved Subspace Estimation for Low-Rank Model-Based Accelerated Cardiac Imaging

“…Imaging data were collected with FA = 18°, FOV = 40 mm × 40 mm, matrix size = 256 × 256, spatial resolution = 0.16 mm × 0.16 mm, slice thickness = 2 mm, and imaging time = 5 min. Parallel acceleration was performed as in [16] with N ACS = 32 and P = 2. The timing parameters for each imaging method were selected for maximum speed: the Cartesian- and spiral-navigated images were collected with T E = 3.0 ms and T R = 6.8 ms, for a frame rate of 74 frames per second (fps); the self-navigated images were collected with T E = 4.9 ms and T R = 10.5 ms, for a frame rate of 95 fps; and the IntraGate images were collected with T E = 3.6 ms, T R = 7.3 ms, and 10 frames per cardiac cycle (analogous to 67 fps for a 400 bpm heart rate).…”

“…For the purposes of this paper, we will consider the regularization function from [16]: $$Gfalse(\mathrm{bold\Psi }false)={\lambda }_1{false\Vert Rfalse\{\mathrm{bold\Psi }false\}false\Vert }_{1,2}+{\lambda }_2{false\Vert \text{vec}false(\mathrm{bold\Psi }\mathrm{bold\Phi }{\mathcal{F}}_{t}false)false\Vert }_1,$$ where || R { Ψ }|| 1 , 2 is a group sparsity penalty that imposes a spatially-varying model order constraint (allowing model order L 1 over the non-cardiac region and model order L 2 ≥ L 1 over the cardiac region), and ||vec( ΨΦ )|| 1 is the spatial-spectral sparsity contraint widely used in compressed sensing cardiac MRI (e.g., [4]–[6], [8], [13], [14]). The final reconstructed image is calculated as Ĉ ( ρ ) = Ψ̂Φ̂ .…”

“…In recent years, significant effort has been made to accelerate cardiovascular MRI using sparse sampling data acquisition strategies. Early examples of sparse sampling for cardiac MRI include DIME [1], UNFOLD [2], and k - t BLAST [3]; more recent methods use sparsity models (often known as compressed sensing, e.g., [4]–[8]), low-rank models (e.g., [9]–[12]), or hybrids of both (e.g., [13]–[16]). It has been demonstrated that these methods are effective for accelerating cardiac MRI.…”

Spatiotemporal-atlas-based dynamic speech imaging

“…Significant efforts have been invested in improving the speed and resolution of DS-MRI, including: a) fast-scanning methods that employ specialized pulse sequences 11 and parallel imaging techniques; 12,13 and b) model-based imaging methods that are based on support constraints, 14,15 subspace constraints 16,17 and sparsity constraints. [18][19][20] The integration of multiple of these approaches have also led to a variety of imaging protocols. 21 A practical DS-MRI method should also allow quantitative characterization of the reconstructed articulatory motion.…”