【作者】 方晟；

【导师】 程建平；

【作者基本信息】 清华大学 ， 核科学与技术， 2010， 博士

【摘要】 成像时间长是磁共振成像技术发展的主要瓶颈之一。尽管提高磁场梯度可以有效地加快数据采集,缩短成像时间,但受制于磁场梯度切换所引起的生理效应,这一途径的加速效果已经接近极限。近年来出现的并行成像技术则通过多通道的并行数据采集,来减少单个通道的数据采集量。由于不依赖于磁场梯度的提高,并行成像技术可以在不产生人体的生理效应的情况下,提高了成像速度。故而,其在磁共振成像中获得了广泛的应用。因此,研究高倍加速并行成像技术对于缩短磁共振成像时间具有普遍而重要的意义。高倍加速并行成像技术的难点在于:其重建过程是一个病态求逆问题,存在噪声放大现象,会严重破坏重建图像的质量。为了解决这一问题,本文针对不同的成像需要提出了两种正则化方法。一方面,为了既保留Tikhonov正则化方法计算简便的优势,又减轻其对图像的模糊效应,本论文将小波域多参数正则化方法应用于并行成像,并提出了基于信号-噪声能量比例的参数估计方法,实现了多个参数的自适应估计。另一方面,为了获得更高质量的重建图像,论文提出了基于图像继承性的非线性正则化方法,其对重建图像中细节信息的保护要明显优于现有正则化方法。该方法既能将多种滤波方法转换为正则化方法,也允许根据图像的特征直接构造正则化泛函,提供了设计正则化的新框架。并行成像技术的重建还受到线圈灵敏度估算精度的影响。本论文提出了非线性估算方法来提高线圈灵敏度的估算精度。该方法缓解了当前线性估算方法中存在的分辨率下降和吉布斯振铃问题,更好地保留了灵敏度的空间定位信息,提高了灵敏度估算的精度,降低了并行成像技术的重建误差。对于磁共振成像序列而言,成像速度和成像质量相互制约。高倍加速并行成像技术不仅可以用于提高成像速度,还可以在少量牺牲成像速度的情况下,在一定程度上,提高成像质量。为了验证本文所提出成像方法的普适性,其被应用于平面回波成像、心脏成像和量化温度成像。实验结果表明,本文中的高倍加速并行成像技术可以有效地提高成像的速度(或质量),具有较广范的适用范围。更多还原

【Abstract】 The long scan time is a bottle neck for the development of magnetic resonanceimaging (MRI). The improvement of the gradient system can directly accelerate everysingle data acquisition and reduce the scan time. However, this method has nearlyreached its limits for scan time reduction, due to the physiological problem induced bythe magnetic gradient. Parallel imaging, on the other hand, shortens the scan time byreducing the amount of the acquired data. It does not rely on the magnetic gradientand has no physiological problems, which makes parallel imaging available for almostall MRI applications. Therefore, it is of great significance to research and develop thehighly accelerated parallel imaging technique for fast MRI.The di?culty of achieving highly accelerated parallel imaging lies in that its re-construction is an ill-posed inverse problem. For this reason, the reconstructed imagesu?ers from severely amplified noise when the acceleration is high. To solve the prob-lem, this thesis proposes two regularization methods. To maintain the computationale?ciency of Tikhonov regularization while reducing its blurring e?ect, we appliedthe wavelet-based multivariate linear regularization to parallel imaging reconstruction.Furthermore, we proposes a parameter estimation method based on power signal-noiseratio, which enables the automatic and adaptive parameter estimation of multivariateregularization. To achieve a high quality reconstructed image, we proposes the non-linear coherence regularization, which significantly outperforms current regularizationmethods in preserving image details. Besides, the coherence regularization can notonly be used to transform various image filtering methods into regularization, but alsobe used to design regularization directly based on image characteristics. Therefore, itprovides a new framework for designing regularization.The estimation of receiver coil sensitivity is also critical for parallel imaging re-construction. This thesis proposes a nonlinear method to improve the accuracy of coilsensitivity estimation. This method addresses the problems of resolution loss and Gibbs ringing in the standard linear method, so that the spatial information of coil sensitivityis better preserved. With this sensitivity information, the parallel imaging reconstruc-tion exhibits fewer error than the standard method.There exists an intrinsic trade-o? between image quality and imaging speed inMRI. Under some situations, parallel imaging technique can not only boost the imag-ing speed, but also improve the image quality without obviously sacrificing the imag-ing speed. To demonstrate the versatility of the proposed method, we applied it to echoplanar imaging, cardiac imaging and temperature mapping. The experimental resultsdemonstrated that the proposed method could e?ciently improve the spatial or tem-poral resolution of MR imaging, which would potentially be available in a variety ofapplications.更多还原