Jiang Yuhao, Huo Donglai, Wilson David L
Department of Engineering and Physics, University of Central Oklahoma, Edmond, OK 73034, USA.
Magn Reson Imaging. 2007 Jun;25(5):712-21. doi: 10.1016/j.mri.2006.10.019. Epub 2007 Feb 26.
Many reconstruction algorithms are being proposed for parallel magnetic resonance imaging (MRI), which uses multiple coils and subsampled k-space data, and a quantitative method for comparison of algorithms is sorely needed. On such images, we compared three methods for quantitative image quality evaluation: human detection, computer detection model and a computer perceptual difference model (PDM). One-quarter sampling and three different reconstruction methods were investigated: a regularization method developed by Ying et al., a simplified regularization method and an iterative method proposed by Pruessmann et al. Images obtained from a full complement of k-space data were also included as reference images. Detection studies were performed using a simulated dark tumor added on MR images of fresh bovine liver. Human detection depended strongly on reconstruction methods used, with the two regularization methods achieving better performance than the iterative method. Images were also evaluated using detection by a channelized Hotelling observer model and by PDM scores. Both predicted the same trends as observed from human detection. We are encouraged that PDM gives trends similar to that for human detection studies. Its ease of use and applicability to a variety of MRI situations make it attractive for evaluating image quality in a variety of MR studies.
目前针对使用多个线圈和欠采样k空间数据的并行磁共振成像（MRI）提出了许多重建算法，因此迫切需要一种算法比较的定量方法。在此类图像上，我们比较了三种定量图像质量评估方法：人工检测、计算机检测模型和计算机感知差异模型（PDM）。研究了四分之一采样和三种不同的重建方法：Ying等人开发的正则化方法、简化正则化方法以及Pruessmann等人提出的迭代方法。从完整的k空间数据获得的图像也作为参考图像纳入研究。检测研究使用添加在新鲜牛肝MR图像上的模拟黑色肿瘤进行。人工检测很大程度上取决于所使用的重建方法，两种正则化方法的性能优于迭代方法。还使用通道化霍特林观察者模型检测和PDM分数对图像进行了评估。两者都预测出了与人工检测相同的趋势。我们感到鼓舞的是，PDM给出的趋势与人工检测研究相似。其易用性和适用于各种MRI情况的特点使其在各种MR研究中评估图像质量时颇具吸引力。
