Correcting corrupted-phases by blood perfusion, diffusion and body motion in MRI imaging.

在拍摄小流量的灌注或扩散图像时，人体运动容易引起伪影。在这项研究中，我们开发了平移，旋转和扩张-收缩身体运动的伪影校正算法。(1)平移刚体运动：利用第n个视图（相位编码）中MRI信号F （x, n）的傅里叶变换F （x, n）显示了物体沿频率轴(x)的位置，我们通过身体运动来纠正损坏的相位。我们还开发了一种不依赖于x轴的物体沿y轴运动的校正方法。(2)旋转刚体运动：对于旋转中心和旋转角度未知的阶跃旋转，首先估计旋转角度，使重构图像外围区域的能量最小。然后采用相位反演算法来减小旋转中心偏离原点引起的相位干扰。(3)伸缩变形体运动：当腹部在x轴和y轴上按比例伸缩时，我们已经证明我们可以通过减小伪影；首先从频率轴上的投影得到与(1)相同的x方向运动，然后用相位反演算法校正y轴对应的相位。这些算法也可以应用于儿童和老人的身体运动引起的伪影校正，这些儿童和老人不能静止地躺在图像上，传统MRI需要几到几十分钟的时间。为了使该算法更接近实际应用，对具有更多自由度的更灵活的运动进行建模是未来的任务。

When taking perfusion or diffusion images of small flow, artifact is easily caused by body motion. In this research, we developed artifact correction algorithms for translational, rotational, and expanding-shrinking body motions.(1) Translational rigid body motion : Using the fact that Fourier transform F (x, n) of MRI signal f (x, n) in the n-th view (phase encode) shows the object position along the frequency axis (x), we corrected the corrupted phases by a body motion. We have also developed a method which can correct independently of x-axis the body motion alosng y-axis.(2) Rotational rigid body motion : For a step rotation with unkown rot ation center and rotation angle, first we estimated the rotaion angle so that energy in outside region of the reconstructed image becomes minimum. Then we applied the phase retreaval algorithm to reduce the phase disturbance caused by the off-origin of the rotation center.(3) Expanding-shrinking deformable body motion : In case of such as abdomen expanding and shrinking proportionally in x-and y-axs, we have shown that we can reduce the artifact by ; first obtain x-directional motion from the projection onto the frequency axis as the same as (1), and then correct phases corresponding to y-axis by the phase retreaval algorithm.These algorithms can also be applied to the artifact correction caused by body motion of child and old person who cannot lay still for the image taking time of several to several ten minutes with conventional MRI.In order to make the algorithm more close to the practical use, it's a future task to model the more flexible motion with more number of freedom.