Real-time phase-contrast flow cardiovascular magnetic resonance with low-rank modeling and parallel imaging.
BACKGROUND
Conventional phase-contrast cardiovascular magnetic resonance (PC-CMR) employs cine-based acquisitions to assess blood flow condition, in which electro-cardiogram (ECG) gating and respiration control are generally required. This often results in lower acquisition efficiency, and limited utility in the presence of cardiovascular pathology (e.g., cardiac arrhythmia). Real-time PC-CMR, without ECG gating and respiration control, is a promising alternative that could overcome limitations of the conventional approach. But real-time PC-CMR involves image reconstruction from highly undersampled (k, t)-space data, which is very challenging. In this study, we present a novel model-based imaging method to enable high-resolution real-time PC-CMR with sparse sampling.


METHODS
The proposed method captures spatiotemporal correlation among flow-compensated and flow-encoded image sequences with a novel low-rank model. The image reconstruction problem is then formulated as a low-rank matrix recovery problem. With proper temporal subspace modeling, it results in a convex optimization formulation. We further integrate this formulation with the SENSE-based parallel imaging model to handle multichannel acquisitions. The performance of the proposed method was systematically evaluated in 2D real-time PC-CMR with flow phantom experiments and in vivo experiments (with healthy subjects). Additionally, we performed a feasibility study of the proposed method on patients with cardiac arrhythmia.


RESULTS
The proposed method achieves a spatial resolution of 1.8 mm and a temporal resolution of 18 ms for 2D real-time PC-CMR with one directional flow encoding. For the flow phantom experiments, both regular and irregular flow patterns were accurately captured. For the in vivo experiments with healthy subjects, flow dynamics obtained from the proposed method correlated well with those from the cine-based acquisitions. For the experiments with the arrhythmic patients, the proposed method demonstrated excellent capability of resolving the beat-by-beat flow variations, which cannot be obtained from the conventional cine-based method.


CONCLUSIONS
The proposed method enables high-resolution real-time PC-CMR at 2D without ECG gating and respiration control. It accurately resolves beat-by-beat flow variations, which holds great promise for studying patients with irregular heartbeats.
Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China.
Aiqi
A
Sun
Athinoula A. Martinos Center for Biomedical Imaging, Massachusetts General Hospital, Chalestown, MA, USA.|Department of Radiology, Harvard Medical School, Boston, MA, USA.
Bo
B
Zhao
Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China.
Yunduo
Y
Li
Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China.
Qiong
Q
He
Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China. leerui@tsinghua.edu.cn.
Rui
R
Li
Center for Biomedical Imaging Research, Department of Biomedical Engineering, School of Medicine, Tsinghua University, Haidian District, Beijing, China.|Vascular Imaging Lab, Department of Radiology, University of Washington, Seattle, WA, USA.
Chun
C
Yuan
D000328:Adult; D000368:Aged; D000465:Algorithms; D001145:Arrhythmias, Cardiac / Q000175:diagnosis* / Q000503:physiopathology; D001783:Blood Flow Velocity; D003326:Coronary Circulation; D005240:Feasibility Studies; D005260:Female; D006801:Humans; D007090:Image Interpretation, Computer-Assisted / Q000379:methods*; D019028:Magnetic Resonance Imaging, Cine / Q000295:instrumentation / Q000379:methods*; D008297:Male; D008955:Models, Cardiovascular; D055414:Myocardial Perfusion Imaging / Q000379:methods*; D066230:Patient-Specific Modeling; D019047:Phantoms, Imaging; D011237:Predictive Value of Tests; D013997:Time Factors; D055815:Young Adult
Cardiovascular imaging; Cine; Low-rank modeling; Model-based reconstruction; Parallel imaging; Phase-contrast CMR; Real-time flow imaging