Real Time Dynamic Magnetic Resonance Imaging Via Dictionary Learning and Combined Fourier Transform

• Main Authors: Yang Wang, Ning Cao, Yueli Cui, Qiang Zhou
• Format: Article
• Language: English
• Published: IEEE 2019-01-01
• Series: IEEE Access
• Subjects: Dynamic magnetic resonance imaging; compressed sensing; combined Fourier transform; dictionary leaning
• Online Access: https://ieeexplore.ieee.org/document/8873554/

Real time dynamic magnetic resonance imaging (dMRI) requires that the image acquisition and reconstruction are carried out simultaneously and the reconstruction speed catches up with imaging speed. In this paper, a novel compressed sensing (CS) reconstruction algorithm for real time dynamic MRI is proposed. The first frame with more k-space measurements is reconstructed precisely as the reference image. Different from previous methods who start their reconstructions from zero-filled k-space measurements, a Combined Fourier Transform (CFT) algorithm is implemented in our method, which can dynamically aggregate the k-space measurements from previous sampled frames to create a highly accurate predictive image for the current frame. We then combine the CFT algorithm with a 3D path-based dictionary leaning algorithm, which is named as DLCFT in our work for fast real time dMRI reconstruction. The proposed algorithm is compared with four state-of-the-art online and offline methods on two real and complex perfusion MR sequences and a real functional brain MR sequence. Experimental results show that the proposed algorithm outperforms these methods with faster convergence and higher reconstruction accuracy.