Adluru Ganesh, Gur Yaniv, Chen Liyong, Feinberg David, Anderson Jeffrey, DiBella Edward V R
UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah 84108.
IBM Almaden Research Center, San Jose, California 95120.
Med Phys. 2015 Aug;42(8):4734-44. doi: 10.1118/1.4926777.
To improve rank constrained reconstructions for undersampled multi-image MRI acquisitions.
Motivated by the recent developments in low-rank matrix completion theory and its applicability to rapid dynamic MRI, a new reordering-based rank constrained reconstruction of undersampled multi-image data that uses prior image information is proposed. Instead of directly minimizing the nuclear norm of a matrix of estimated images, the nuclear norm of reordered matrix values is minimized. The reordering is based on the prior image estimates. The method is tested on brain diffusion imaging data and dynamic contrast enhanced myocardial perfusion data.
Good quality images from data undersampled by a factor of three for diffusion imaging and by a factor of 3.5 for dynamic cardiac perfusion imaging with respiratory motion were obtained. Reordering gave visually improved image quality over standard nuclear norm minimization reconstructions. Root mean squared errors with respect to ground truth images were improved by ∼18% and ∼16% with reordering for diffusion and perfusion applications, respectively.
The reordered low-rank constraint is a way to inject prior image information that offers improvements over a standard low-rank constraint for undersampled multi-image MRI reconstructions.
改进欠采样多图像MRI采集的秩约束重建。
受低秩矩阵补全理论的最新进展及其在快速动态MRI中的适用性启发，提出了一种基于重新排序的利用先验图像信息的欠采样多图像数据秩约束重建方法。该方法不是直接最小化估计图像矩阵的核范数，而是最小化重新排序后的矩阵值的核范数。重新排序基于先验图像估计。该方法在脑扩散成像数据和动态对比增强心肌灌注数据上进行了测试。
对于扩散成像，从欠采样三倍的数据中获得了高质量图像；对于有呼吸运动的动态心脏灌注成像，从欠采样3.5倍的数据中获得了高质量图像。与标准核范数最小化重建相比，重新排序在视觉上改善了图像质量。对于扩散和灌注应用，重新排序分别使相对于真实图像的均方根误差提高了约18%和约16%。
重新排序的低秩约束是一种注入先验图像信息的方法，与标准低秩约束相比，它在欠采样多图像MRI重建中能带来改进。
