Reference-free Correction for the Nyquist Ghost in Echo-planar Imaging using Deep Learning

Xudong Chen, Yufei Zhang, H. She, Yiping P. Du
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引用次数: 1

Abstract

Echo-planar imaging suffers from Nyquist ghost (i.e., N/2 ghost) because of the imperfection of the gradient system and gradient delays. The phase mismatch between even and odd echoes can be eliminated by an extra reference scan without the phase encoding. However, due to the non-linear and time-varying local magnetic field changes or movement of the patients, the reference-based methods may have incorrect correction results. Other correction methods like parallel imaging reconstruction may suffer from the image noise amplification and signal-to-noise ratio penalty. In this study, a deep learning method is proposed to eliminate the phase error in k-space and correct the mismatch between even and odd echoes without reference scan and SNR penalty. The Fourier transform layer is introduced into the conventional U-Net structure, and the distortion-free images are directly reconstructed from the k-space EPI data. Turbo spin echo data and single-shot EPI data are tested using this network. The results show that this method has a good performance in ghost correction, and the ghost-to-signal ratio is effectively reduced compared to other state-of-the-art correction methods. The proposed deep learning method is reference-free and effective to correct Nyquist ghost in EPI, and can also combine with parallel imaging to achieve additional acceleration.
利用深度学习对回波平面成像中奈奎斯特幽灵进行无参考校正
由于梯度系统的不完善和梯度延迟,回波平面成像存在奈奎斯特鬼影(即N/2鬼影)。在不进行相位编码的情况下,通过额外的参考扫描可以消除奇偶回波之间的相位不匹配。然而,由于局部磁场的非线性和时变变化或患者的运动,基于参考的方法可能会产生不正确的校正结果。其他校正方法如并行成像重建可能会受到图像噪声放大和信噪比损失的影响。本研究提出了一种深度学习方法,在不需要参考扫描和信噪比损失的情况下消除k空间的相位误差,纠正奇偶回波不匹配。在传统的U-Net结构中引入傅里叶变换层,直接从k空间EPI数据重构无畸变图像。利用该网络测试了Turbo自旋回波数据和单次EPI数据。结果表明,该方法具有良好的鬼信号校正性能,与其他先进的校正方法相比,能有效降低鬼信号比。所提出的深度学习方法不需要参考,可以有效地校正EPI中的Nyquist鬼影,并且可以结合并行成像实现额外的加速。
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