使用具有广义支持的部分可分离函数模型的首过灌注心脏磁共振成像。
First-pass perfusion cardiac MRI using the Partially Separable Functions model with generalized support.
作者信息
Brinegar Cornelius, Zhang Haosen, Wu Yi-Jen L, Foley Lesley M, Hitchens T, Ye Qing, Ho Chien, Liang Zhi-Pei
机构信息
Department of Electrical and Computer Engineering, University of Illinois at Urbana-Champaign, 1406 West Green Street, Urbana, IL 61801, USA.
出版信息
Annu Int Conf IEEE Eng Med Biol Soc. 2010;2010:2833-6. doi: 10.1109/IEMBS.2010.5626078.
Abstract
Dynamic imaging methods based on the Partially Separable Functions (PSF) model have been used to perform ungated cardiac MRI, and the critical parameter determining the quality of the reconstructed images is the order, L, of the PSF model. This work extends previous methods by increasing L in the cardiac region to improve the ability of the PSF model to represent complex spatiotemporal signals. The resulting higher order PSF model is fit to sparse (k, t)-space data using spatial-spectral support, spatial-eigenbasis support, and spectral sparsity constraints. This new method is demonstrated in the context of 2D first-pass perfusion MRI in a healthy rat heart.
摘要
基于部分可分离函数（PSF）模型的动态成像方法已被用于进行非门控心脏磁共振成像（MRI），而决定重建图像质量的关键参数是PSF模型的阶数L。这项工作通过增加心脏区域的L来扩展先前的方法，以提高PSF模型表示复杂时空信号的能力。使用空间谱支持、空间特征基支持和谱稀疏性约束，将由此得到的高阶PSF模型拟合到稀疏的（k，t）空间数据。这种新方法在健康大鼠心脏的二维首过灌注MRI中得到了验证。
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