自适应重建（Adaptive Reconstruction，AR）算法被广泛应用于磁共振图像的多通道合并问题上． AR算法不需要直接采集各个线圈的灵敏度信息，而是通过通道间信号及噪声相关矩阵，估算出各个通道的灵敏度，从而保证了合并的幅值图像具有较高的信噪比（Signal-to-Noise Ratio，SNR）．然而，由于AR算法没有针对相位图像的合并问题进行优化，导致重建出的相位图像具有不确定性．另外，受各通道之间相位偏移及低信噪比相位图像的影响，重建结果可能包含伪影．该文提出了一种改进型AR算法，估算并移除了各通道之间的相位偏移，同时对多通道数据的相位进行质量评估及通道重排，用以进行后续自适应重建．仿体及在体实验表明，该方法可以有效提升AR算法稳定性、消除重建图像中存在的伪影，同时保持合并后幅值图像及相位图像的高信噪比．
Adaptive reconstruction (AR) has been widely used to combine multi-channel MRI images. AR can estimate coil sensitivity through signal and noise correlation matrices between different channels, and therefore achieve optimized signal-to-noise ratio (SNR) for the reconstructed images. However, AR is usually not optimized for phase images, which may lead to uncertainties when used to reconstruct phase images. In addition, the reconstructed phase images may contain artifacts when there are coil-dependent phase offsets and noise-corrupted phase images. In this study, an improved adaptive reconstruction (iAR) method was developed, which can eliminate coil-dependent phase offsets, and rearrange multi-channel data after a data quality judgment. Phantom and in vivo experiments demonstrated that the proposed method had improved robustness over the original AR method, and it can eliminate the artifacts contained in the reconstructed phase images while maintaining high SNR.
[1] Kecskemeti S, Johnson K, Wu Y, et al. High resolution three-dimensional cine phase contrast MRI of small intracranial aneurysms using a stack of stars k-space trajectory[J]. J Magn Reson Imaging, 2012, 35(3):518-527.
[2] Markl M, Frydrychowicz A, Kozerke S, et al. 4D flow MRI[J]. J Magn Reson Imaging, 2012, 36(5):1015-1036.
[3] De Senneville B D, Roujol S, Jaïs P, et al. Feasibility of fast MR-thermometry during cardiac radiofrequency ablation[J]. NMR Biomed, 2012, 25(4):556-562.
[4] Vogl T J, Huebner F, Naguib N N, et al. MR-based thermometry of laser induced thermotherapy:temperature accuracy and temporal resolution in vitro at 0.2 and 1.5 T magnetic field strengths[J]. Lasers Surg Med, 2012, 44(3):257-265.
[5] Haacke E M, Liu S, Buch S, et al. Quantitative susceptibility mapping:current status and future directions[J]. Magn Reson Imaging, 2015, 33(1):1-25.
[6] Liu J, Liu T, de Rochefort L, et al. Morphology enabled dipole inversion for quantitative susceptibility mapping using structural consistency between the magnitude image and the susceptibility map[J]. Neuroimage, 2012, 59(3):2560-2568.
[7] Li W, Wu B, Batrachenko A, et al. Differential developmental trajectories of magnetic susceptibility in human brain gray and white matter over the lifespan[J]. Hum Brain Mapp, 2014, 35(6):2698-2713.
[8] Liu T, Spincemaille P, de Rochefort L, et al. Calculation of susceptibility through multiple orientation sampling (COSMOS):a method for conditioning the inverse problem from measured magnetic field map to susceptibility source image in MRI[J]. Magn Reson Med, 2009, 61(1):196-204.
[9] Li W, Wu B, Liu C. Quantitative susceptibility mapping of human brain reflects spatial variation in tissue composition[J]. Neuroimage, 2011, 55(4):1645-1656.
[10] Weng Peng-fei(翁鹏飞), Dong Fang(董芳), Wang Qian-feng(王前锋), et al. Three-dimensional multi-echo quantitative susceptibility mapping with flow compensation[三维多回波流动补偿定量磁化率成像(QSM)方法研究] [J]. Chinese J Magn Reson(波谱学杂志), 2015, 32(3):450-461.
[11] Wang A-li(王阿莉), Lin Jian-zhong(林建忠), Liu Wei-jun(刘伟俊), et al. Quantitative susceptibility mapping(定量磁化率成像重建方法及其应用)[J]. Chinese J Magn Reson(波谱学杂志), 2014, 31(1):133-154.
[12] Roemer P B, Edelstein W A, Hayes C E, et al. The NMR phased array[J]. Magn Reson Med, 1990, 16(2):192-225.
[13] Pruessmann K P, Weiger M, Scheidegger M B, et al. SENSE:sensitivity encoding for fast MRI[J]. Magn Reson Med, 1999, 42(5):952-962.
[14] Griswold M A, Jakob P M, Heidemann R M, et al. Generalized autocalibrating partially parallel acquisitions (GRAPPA)[J]. Magn Reson Med, 2002, 47(6):1202-1210.
[15] Walsh D O, Gmitro A F, Marcellin M W. Adaptive reconstruction of phased array MR imagery[J]. Magn Reson Med, 2000, 43(5):682-690.
[16] Bernstein M A, Grgic M, Brosnan T J, et al. Reconstructions of phase contrast, phased array multicoil data[J]. Magn Reson Med, 1994, 32(3):330-334.
[17] Sodickson D K, Manning W J. Simultaneous acquisition of spatial harmonics (SMASH):fast imaging with radiofrequency coil arrays[J]. Magn Reson Med, 1997, 38(4):591-603.
[18] Vegh V, O'Brien K, Barth M, et al. Selective channel combination of MRI signal phase[J]. Magn Reson Med, 2016, 76(5):1469-1477.
[19] Ghiglia D C, Pritt M D. Two-Dimensional Phase Unwrapping:Theory, Algorithms, and Software[M]. New York:Wiley, 1998.
[20] Bing W, Wei L, Arnaud G, et al. Whole brain susceptibility mapping using compressed sensing[J]. Magn Reson Med, 2012, 67(1):137-147.
[21] Li W, Avram A V, Wu B, et al. Integrated laplacian-based phase unwrapping and background phase removal for quantitative susceptibility mapping[J]. NMR Biomed, 2014, 27(2):219-227.
[22] Parker D L, Payne A, Todd N, et al. Phase reconstruction from multiple coil data using a virtual reference coil[J]. Magn Reson Med, 2014, 72(2):563-569.
