Brinegar Cornelius, Zhang Haosen, Wu Yi-Jen L, Foley Lesley M, Hitchens T, Ye Qing, Pocci Darren, Lam Fan, Ho Chien, Liang Zhi-Pei
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 1406 West Green Street, Urbana, IL 61801, USA.
Annu Int Conf IEEE Eng Med Biol Soc. 2009;2009:4383-6. doi: 10.1109/IEMBS.2009.5333482.
Cardiac MRI performed while the patient is breathing is typically achieved using non-real-time techniques such as ECG triggering with respiratory gating; however, modern dynamic imaging techniques are beginning to enable this type of imaging in real-time. One of these dynamic imaging techniques is based on forming a Partially Separable Function (PSF) model of the data, but the model fitting process is known to be sensitive even when truncated SVD regularization is used. As a result, physiologically meaningless artifacts can appear in the dynamic images when the total number of measurements is limited. To address this issue, the dynamic imaging problem is formulated as a generalized Tikhonov regularization problem with the PSF model as a component of the forward data model, and a penalty function is used to introduce spatial-spectral prior information. This new method both reduces data acquisition requirements and improves stability relative to the original PSF based method when applied to cardiac MRI.
在患者呼吸时进行心脏磁共振成像(MRI)通常使用非实时技术来实现,例如带有呼吸门控的心电图触发;然而,现代动态成像技术正开始能够实时进行此类成像。其中一种动态成像技术基于形成数据的部分可分离函数(PSF)模型,但已知即使使用截断奇异值分解(SVD)正则化,模型拟合过程也很敏感。因此,当测量总数有限时,动态图像中可能会出现生理上无意义的伪影。为了解决这个问题,将动态成像问题表述为一个广义的蒂霍诺夫正则化问题,将PSF模型作为前向数据模型的一个组成部分,并使用惩罚函数引入空间光谱先验信息。当应用于心脏MRI时,这种新方法相对于基于原始PSF的方法既减少了数据采集要求,又提高了稳定性。