Lachner Sebastian, Zaric Olgica, Utzschneider Matthias, Minarikova Lenka, Zbýň Štefan, Hensel Bernhard, Trattnig Siegfried, Uder Michael, Nagel Armin M
Institute of Radiology, University Hospital Erlangen, Friedrich-Alexander-Universität Erlangen-Nürnberg (FAU), Erlangen, Germany.
High Field MR Center, Department of Biomedical Imaging and Image-guided Therapy, Medical University of Vienna, Vienna, Austria.
Magn Reson Imaging. 2019 Jul;60:145-156. doi: 10.1016/j.mri.2019.03.024. Epub 2019 Mar 31.
To reduce acquisition time and to improve image quality in sodium magnetic resonance imaging (Na MRI) using an iterative reconstruction algorithm for multi-channel data sets based on compressed sensing (CS) with anatomical H prior knowledge.
An iterative reconstruction for Na MRI with multi-channel receiver coils is presented. Based on CS it utilizes a second order total variation (TV), adopted by anatomical weighting factors (AnaWeTV) obtained from a high-resolution H image. A support region is included as additional regularization. Simulated and measured Na multi-channel data sets (n = 3) of the female breast acquired at 7 T with different undersampling factors (USF = 1.8/3.6/7.2/14.4) were reconstructed and compared to a conventional gridding reconstruction. The structural similarity was used to assess image quality of the reconstructed simulated data sets and to optimize the weighting factors for the CS reconstruction.
Compared with a conventional TV, the AnaWeTV reconstruction leads to an improved image quality due to preserving of known structure and reduced partial volume effects. An additional incorporated support region shows further improvements for high USFs. Since the decrease in image quality with higher USFs is less pronounced compared to a conventional gridding reconstruction, proposed algorithm is beneficial especially for higher USFs. Acquisition time can be reduced by a factor of 4 (USF = 7.2), while image quality is still similar to a nearly fully sampled (USF = 1.8) gridding reconstructed data set.
Especially for high USFs, the proposed algorithm allows improved image quality for multi-channel Na MRI data sets.
利用基于压缩感知（CS）并结合解剖学H先验知识的迭代重建算法，减少钠磁共振成像（Na MRI）的采集时间并提高图像质量。
提出一种用于多通道接收线圈的Na MRI迭代重建方法。基于CS，它利用二阶全变差（TV），并采用从高分辨率H图像获得的解剖加权因子（AnaWeTV）。包含一个支持区域作为额外的正则化。对在7T下采集的具有不同欠采样因子（USF = 1.8/3.6/7.2/14.4）的女性乳房的模拟和测量的Na多通道数据集（n = 3）进行重建，并与传统的网格化重建进行比较。使用结构相似性来评估重建模拟数据集的图像质量，并优化CS重建的加权因子。
与传统TV相比，AnaWeTV重建由于保留了已知结构并减少了部分容积效应，从而提高了图像质量。对于高USF，额外纳入的支持区域显示出进一步的改善。与传统的网格化重建相比，由于较高USF导致的图像质量下降不太明显，因此所提出的算法尤其有利于较高的USF。采集时间可以减少4倍（USF = 7.2），而图像质量仍然与几乎完全采样（USF = 1.8）的网格化重建数据集相似。
特别是对于高USF，所提出的算法能够提高多通道Na MRI数据集的图像质量。
