k‐Space deep learning for reference‐free EPI ghost correction

• Published in: Magnetic resonance in medicine 2019-12, Vol.82 (6), p.2299-2313
• Main Authors: Lee, Juyoung, Han, Yoseob, Ryu, Jae‐Kyun, Park, Jang‐Yeon, Ye, Jong Chul
• Format: Article
• Language: English
• Subjects: Acceleration; Artificial neural networks; Channels; Computing time; deep convolutional framelet; Deep learning; deep learning k‐space learning; Echoes; EPI; Hankel matrices; Image quality; Image reconstruction; In vivo methods and tests; Interpolation; Kernels; Machine learning; Magnetic fields; Magnetic resonance imaging; MRI; Neural networks; Nyquist ghost artifact; Redundancy
• ISSN: 0740-3194; 1522-2594
• DOI: 10.1002/mrm.27896

Purpose Nyquist ghost artifacts in echo planar imaging (EPI) are originated from phase mismatch between the even and odd echoes. However, conventional correction methods using reference scans often produce erroneous results especially in high‐field MRI due to the nonlinear and time‐varying local magnetic field changes. Recently, it was shown that the problem of ghost correction can be reformulated as k‐space interpolation problem that can be solved using structured low‐rank Hankel matrix approaches. Another recent work showed that data driven Hankel matrix decomposition can be reformulated to exhibit similar structures as deep convolutional neural network. By synergistically combining these findings, we propose a k‐space deep learning approach that immediately corrects the phase mismatch without a reference scan in both accelerated and non‐accelerated EPI acquisitions. Theory and Methods To take advantage of the even and odd‐phase directional redundancy, the k‐space data are divided into 2 channels configured with even and odd phase encodings. The redundancies between coils are also exploited by stacking the multi‐coil k‐space data into additional input channels. Then, our k‐space ghost correction network is trained to learn the interpolation kernel to estimate the missing virtual k‐space data. For the accelerated EPI data, the same neural network is trained to directly estimate the interpolation kernels for missing k‐space data from both ghost and subsampling. Results Reconstruction results using 3T and 7T in vivo data showed that the proposed method outperformed the image quality compared to the existing methods, and the computing time is much faster. Conclusions The proposed k‐space deep learning for EPI ghost correction is highly robust and fast, and can be combined with acceleration, so that it can be used as a promising correction tool for high‐field MRI without changing the current acquisition protocol.