Abstract: Parallel imaging techniques can help overcome issues such as radiofrequency energy deposition, image inhomogeneity, and reduce scan time in ultra-high field Magnetic Resonance Imaging (MRI), while also lowering motion artifacts and accelerating data acquisition speed. To enhance feature extraction from MRI complex-valued data and reduce aliasing artifacts caused by undersampling in parallel imaging, a Residual Complex convolution scan-specific Robust Artificial-neural-networks for K-space Interpolation (RCRAKI) was proposed. The raw undersampled MRI scan data was taken as input by the algorithm, and the advantages of both linear and nonlinear reconstruction methods combined with a residual structure were leveraged. In the residual connection part, convolution was used to create a linear reconstruction baseline, while multiple layers of complex convolution were utilized in the main path to compensate for baseline defects, ultimately resulting in Magnetic Resonance (MR)images being reconstructed with fewer artifacts. Experiments were conducted on data acquired from a 7T ultra-high field MRI device developed by the Hefei Comprehensive National Science Center Energy Research Institute. The comparison results with the residual scan-specific Robust Artificial-neural-networks for K-space Interpolation (rRAKI) under a sampling rate of 40 Auto-Calibration Signals (ACS) and an acceleration factor of 8 for mouse imaging across different anatomical planes show that: in the sagittal plane, the Normalized Root Mean Square Error (NRMSE) of the proposed algorithm decreases by 59.74%, the Structural SIMilarity (SSIM) increases by 0.45%, and the Peak Signal-to-Noise Ratio (PSNR) increases by 13.04%; in the transverse plane, the NRMSE decreases by 7.97%, SSIM improves slightly by 0.001%, and PSNR increases by 1.09%; in the coronal plane, the NRMSE decreases by 35.03%, PSNR increases by 5.60%, and SSIM increases by 0.98%. Experimental results demonstrate that RCRAKI, combining complex convolution and residual structures, performs well across different anatomical planes of MRI data. The method can reduce the impact of noise amplification at high acceleration factors and reconstruct MR images with clearer details.

Key words: ultra-high-field magnetic resonance, parallel imaging, under-sampled images, complex convolution, deep learning

摘要： 并行成像技术可以帮助解决超高场强磁共振成像(MRI)中的射频能量沉积、图像均匀性问题，缩短扫描时间，降低运动伪影，并提升数据采集速度。为了提高对MRI复值数据的特征提取能力，减小并行成像欠采样所引起的卷褶伪影，提出基于K空间插值的残差复卷积鲁棒人工神经网络(RCRAKI)。所提算法将原始欠采样磁共振扫描数据作为输入，利用残差结构结合线性与非线性重建方法的优势，在残差连接部分利用卷积创建线性重建基线，主路径利用多层复卷积补偿基线缺陷，最终重建出伪影更少的磁共振(MR)图像。在合肥综合性国家科学中心能源研究院自主研发的7T超高场磁共振设备采集的数据上进行实验，与基于K空间插值的残差鲁棒人工神经网络方法(rRAKI)在自动校准信号(ACS)数为40、加速比为8的采样率下进行小鼠不同解剖切面成像质量对比结果表明：在矢状面下，所提算法的标准均方根误差(NRMSE)指标下降了59.74%，结构相似度(SSIM)指标提升了0.45%，峰值信噪比(PSNR)指标提升了13.04%；在横断面下，所提算法的NRMSE指标降低了7.97%，SSIM指标略有改善(提高0.001%)，PSNR指标提升了1.09%；在冠状面下，所提算法的NRMSE指标下降了35. 03%，PSNR指标提升了5.60%，SSIM指标提升了0.98%。实验结果表明，RCRAKI，在不同解剖切面的磁共振数据上表现出良好的性能，在高加速比采样率下能够减小噪声放大的影响，并重建出细节更加清晰的MR图像。

关键词: 超高场磁共振, 并行成像, 欠采样图像, 复卷积, 深度学习