Motion-resolved real-time 4D flow MRI with low-rank and subspace modeling

PURPOSE

To develop a new motion-resolved real-time four-dimensional (4D) flow MRI method, which enables the quantification and visualization of blood flow velocities with three-directional flow encodings and volumetric coverage without electrocardiogram (ECG) synchronization and respiration control.

METHODS

An integrated imaging method is presented for real-time 4D flow MRI, which encompasses data acquisition, image reconstruction, and postprocessing. The proposed method features a specialized continuous ( k , t ) $$ \left(\mathbf{k},t\right) $$ -space acquisition scheme, which collects two sets of data (i.e., training data and imaging data) in an interleaved manner. By exploiting strong spatiotemporal correlation of 4D flow data, it reconstructs time-series images from highly-undersampled ( k , t ) $$ \left(\mathbf{k},t\right) $$ -space measurements with a low-rank and subspace model. Through data-binning-based postprocessing, it constructs a five-dimensional dataset (i.e., x-y-z-cardiac-respiratory), from which respiration-dependent flow information is further analyzed. The proposed method was evaluated in aortic flow imaging experiments with ten healthy subjects and two patients with atrial fibrillation.

RESULTS

The proposed method achieves 2.4 mm isotropic spatial resolution and 34.4 ms temporal resolution for measuring the blood flow of the aorta. For the healthy subjects, it provides flow measurements in good agreement with those from the conventional 4D flow MRI technique. For the patients with atrial fibrillation, it is able to resolve beat-by-beat pathological flow variations, which cannot be obtained from the conventional technique. The postprocessing further provides respiration-dependent flow information.

CONCLUSION

The proposed method enables high-resolution motion-resolved real-time 4D flow imaging without ECG gating and respiration control. It is able to resolve beat-by-beat blood flow variations as well as respiration-dependent flow information.

Dynamic hyperpolarized 13 C MR spectroscopic imaging using SPICE in mouse kidney at 9.4 T

This study aims to investigate the feasibility of dynamic hyperpolarized ¹³ C MR spectroscopic imaging (MRSI) using the SPectroscopic Imaging by exploiting spatiospectral CorrElation (SPICE) technique and an estimation of the spatially resolved conversion constant rate (k_pl ). An acquisition scheme comprising a single training dataset and several imaging datasets was proposed considering hyperpolarized ¹³ C circumstances. The feasibility and advantage of the scheme were investigated in two parts: (a) consistency of spectral basis over time and (b) accuracy of the estimated k_pl . The simulations and in vivo experiments support accurate k_pl estimation with consistent spectral bases. The proposed method was implemented in an enzyme phantom and via in vivo experiments. In the enzyme phantom experiments, spatially resolved homogeneous k_pl maps were observed. In the in vivo experiments, normal diet (ND) mice and high-fat diet (HFD) mice had k_pl (s^-1 ) values of medullar (ND: 0.0119 ± 0.0022, HFD: 0.0195 ± 0.0005) and cortical (ND: 0.0148 ±0.0023, HFD: 0.0224 ±0.0054) regions which were higher than vascular (ND: 0.0087 ±0.0013, HFD: 0.0132 ±0.0050) regions. In particular, the k_pl value in the medullar region exhibited a significant difference between the two diet groups. In summary, the feasibility of using modified SPICE for dynamic hyperpolarized ¹³ C MRSI was demonstrated via simulations and in vivo experiments. The consistency of spectral bases over time and the accuracy of the estimated k_pl values validate the proposed acquisition scheme, which comprises only a single training dataset. The proposed method improved the spatial resolution of dynamic hyperpolarized ¹³ C MRSI, which could be used for k_pl estimation using high signal-to-noise ratio spectral bases.

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

PURPOSE

To develop data acquisition and image reconstruction methods to enable high-resolution (1) H MR spectroscopic imaging (MRSI) of the brain, using the recently proposed subspace-based spectroscopic imaging framework called SPICE (SPectroscopic Imaging by exploiting spatiospectral CorrElation).

THEORY AND METHODS

SPICE is characterized by the use of a subspace model for both data acquisition and image reconstruction. For data acquisition, we propose a novel spatiospectral encoding scheme that provides hybrid data sets for determining the subspace structure and for image reconstruction using the subspace model. More specifically, we use a hybrid chemical shift imaging /echo-planar spectroscopic imaging sequence for two-dimensional (2D) MRSI and a dual-density, dual-speed echo-planar spectroscopic imaging sequence for three-dimensional (3D) MRSI. For image reconstruction, we propose a method that can determine the subspace structure and the high-resolution spatiospectral reconstruction from the hybrid data sets generated by the proposed sequences, incorporating field inhomogeneity correction and edge-preserving regularization.

RESULTS

Phantom and in vivo brain experiments were performed to evaluate the performance of the proposed method. For 2D MRSI experiments, SPICE is able to produce high-SNR spatiospectral distributions with an approximately 3 mm nominal in-plane resolution from a 10-min acquisition. For 3D MRSI experiments, SPICE is able to achieve an approximately 3 mm in-plane and 4 mm through-plane resolution in about 25 min.

CONCLUSION

Special data acquisition and reconstruction methods have been developed for high-resolution (1) H-MRSI of the brain using SPICE. Using these methods, SPICE is able to produce spatiospectral distributions of (1) H metabolites in the brain with high spatial resolution, while maintaining a good SNR. These capabilities should prove useful for practical applications of SPICE. Magn Reson Med 76:1059-1070, 2016. © 2015 Wiley Periodicals, Inc.