Accelerated phase‐contrast cine MRI using k‐t SPARSE‐SENSE

Abnormalities in vascular flow patterns frequently can provide useful insights into pathophysiology. For example, recent studies suggest that abnormal hepatic venous waveforms can be used to diagnose liver pathologies such as cirrhosis (1–3). Although ultrasound is typically used in these studies, phase-contrast (PC) cine MRI (4–9) is a promising modality for studying hemodynamics and can be performed in addition to conventional contrast-enhanced anatomic MR imaging. Also, unlike Doppler ultrasonography, which is sensitive to misalignment between the vessel axis and Doppler beam angle, PC cine MRI can acquire data in any orientation, regardless of the depth of the vessel or overlying bowel gas. A major disadvantage of PC cine MRI, however, is its low data acquisition efficiency, which may limit the achievable spatial and temporal resolutions within clinically acceptable breath-hold durations. In PC cine MRI, two data sets (e.g., phase reference and velocity-encoded) are acquired to subtract the background phase due to static magnetic field inhomogeneities and susceptibility. Typically, phase reference and velocity-encoded cine data sets are acquired in an interleaved fashion within the same heart beat and over multiple heart beats (7). As a result of this time-consuming acquisition approach, PC cine MRI studies are particularly in need of acceleration to achieve relatively high spatio-temporal resolution within clinically acceptable scan times. Previous studies have incorporated acceleration methods into PC cine MRI, including spiral (10), radial (11), balanced steady-state free precession (12), view sharing (13,14), parallel imaging (15–17), and other spatio-temporal undersampling approaches (18,19), with each method having advantages and disadvantages. Among these acceleration methods, the most widely used method for PC cine MRI is parallel imaging (20–22). Most clinical MR scanners equipped with coil arrays are capable of performing generalized autocalibrating partially parallel acquisitions (GRAPPA) (22) or sensitivity encoding (SENSE) (21) accelerations with acceleration rates (R) of 2–3, but the achievable spatio-temporal resolution within a clinically acceptable breath-hold duration is still limited. Compressed sensing (CS) techniques, relatively recently introduced into the field of MRI (23), represent an alternative method to accelerate PC cine MRI (24–26). PC cine MRI is a good candidate for CS, because the background is in a steady state of magnetization, the temporal variation in signal is limited to blood vessel regions, and the resulting image data are sparse after applying an appropriate transform. Previous CS studies for dynamic MRI (27–32) have proposed temporal principal component analysis (PCA) as the sparsifying transform. To date, only a few preliminary CS studies for PC cine MRI have been reported (24–26). The study by Yoon et al. (24) describes use of a nonconvex greedy algorithm for high acceleration in a simulated phantom. The study by Tao et al. (26) reports low acceleration in retrospectively undersampled carotid PC cine MRI data, using temporal fast Fourier transform (FFT) as the sparsifying transform. The study by Velikina et al. (25) describes high acceleration using second temporal difference as the sparsifying transform. These preliminary studies have been important developments, but they have not been validated in vivo. This study describes a method to accelerate PC cine MRI using a combination of k-t SPARSE (33) and SENSE (21) that exploits joint sparsity among all component coil datasets (k-t SPARSE-SENSE) (34). Although this approach can be used in all vascular systems, the motivation of our work was to study hepatic blood flow waveforms in liver diseases (1–3) using accelerated PC cine MRI. We describe an accelerated PC cine MRI pulse sequence with k-t SPARSE-SENSE and our results validating it against a reference PC cine MRI pulse sequence with GRAPPA in a flow phantom and human subjects.