基于惩罚最大似然优化模型的各向异性约束磁共振成像方法

传统磁共振(MR)傅里叶成像方法由于傅里叶不确定性,k空间扩展编码采样长度能提高图像空间分辨率,但是以降低图像信噪比为代价.提出基于最大似然优化模型的各向异性约束MR成像新方法,将离散傅里叶变换模型改进为惩罚约束函数的最优值搜索问题.利用医学结构的先验信息,将正则化惩罚运算细化至平滑区域、边界邻域、边界和边界的方向.实验结果表明,该方法不但能扩展k空间高频数据采样长度同时有效降低高斯噪声,而且能克服现有相关约束成像方法的二次模糊和Gibbs环状伪影.

Anisotropically constrained MR imaging based on penalized maximum likelihood optimality model

Fourier imaging in MRI application has the dilemma that extended k-space sampling to improve image resolution also degrade signal-to-noise ratio (SNR) because of Fourier uncertainty. This paper proposes a new method that anisotropically constrained image reconstruction based on penalized maximum likelihood optimality model, which is an optimization problem instead of DFT approach. Anisotropic regularization for enforcing anatomical prior information is proposed, where directional regularization operators apply to the smooth area, neighbouring edge area and edges respectively. Experimental results show that the proposed method enables extended k-space sampling while suppress Gaussian noise, and reduces the reblurring problem and the Gibbs ringing artifacts of existing constrained reconstruction methods.