Zhang Jucheng, Chu Yonghua, Ding Wenhong, Kang Liyi, Xia Ling, Jaiswal Sanjay, Wang Zhikang, Chen Zhifeng
Department of Clinical Engineering, 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
Department of Radiology, 2nd Affiliated Hospital, School of Medicine, Zhejiang University, Hangzhou, Zhejiang, China.
BMC Med Imaging. 2019 Apr 3;19(1):27. doi: 10.1186/s12880-019-0327-3.
One of the major limitations of MRI is its slow acquisition speed. To accelerate data acquisition, partially parallel imaging (PPI) methods have been widely used in clinical applications such as sensitivity encoding (SENSE) and generalized autocalibrating partially parallel acquisitions (GRAPPA). SENSE is a popular image-domain partially parallel imaging method, which suffers from residual aliasing artifacts when the reduction factor goes higher. Undersampling the k-space data and then reconstruct images with artificial sparsity is an efficient way to accelerate data acquisition. By exploiting artificial sparsity with a high-pass filter, an improved SENSE method is proposed in this work, termed high-pass filtered SENSE (HF-SENSE).
First, a high-pass filter was applied to the raw k-space data, the result of which was used as the inputs of sensitivity estimation and undersampling process. Second, the adaptive array coil combination method was adopted to calculate sensitivity maps on a block-by-block basis. Third, Tikhonov regularized SENSE was then used to reconstruct magnetic resonance images. Fourth, the reconstructed images were transformed into k-space data, which was filtered with the corresponding inverse filter.
Both simulation and in vivo experiments demonstrate that HF-SENSE method significantly reduces noise level of the reconstructed images compared with SENSE. Furthermore, it is found that HF-SENSE can achieve lower normalized root-mean-square error value than SENSE.
The proposed method explores artificial sparsity with a high-pass filter. Experiments demonstrate that the proposed HF-SENSE method can improve the image quality of SENSE reconstruction. The high-pass filter parameters can be predefined. With this image reconstruction method, high acceleration factors can be achieved, which will improve the clinical applicability of SENSE. This retrospective study (HF-SENSE: an improved partially parallel imaging using a high-pass filter) was approved by Institute Review Board of 2nd Affiliated Hospital of Zhejiang University (ethical approval number 2018-314). Participant for all images have informed consent that he knew the risks and agreed to participate in the research.
磁共振成像（MRI）的主要局限性之一是其采集速度较慢。为了加速数据采集，部分并行成像（PPI）方法已广泛应用于临床，如灵敏度编码（SENSE）和广义自校准部分并行采集（GRAPPA）。SENSE是一种常用的图像域部分并行成像方法，当加速因子较高时会出现残余混叠伪影。对k空间数据进行欠采样，然后利用人工稀疏性重建图像是加速数据采集的有效方法。通过使用高通滤波器利用人工稀疏性，本文提出了一种改进的SENSE方法，称为高通滤波SENSE（HF-SENSE）。
首先，对原始k空间数据应用高通滤波器，其结果用作灵敏度估计和欠采样过程的输入。其次，采用自适应阵列线圈组合方法逐块计算灵敏度图。第三，然后使用蒂霍诺夫正则化SENSE重建磁共振图像。第四，将重建图像转换为k空间数据，并用相应的逆滤波器进行滤波。
模拟和体内实验均表明，与SENSE相比，HF-SENSE方法显著降低了重建图像的噪声水平。此外，发现HF-SENSE可以获得比SENSE更低的归一化均方根误差值。
所提出的方法利用高通滤波器探索人工稀疏性。实验表明，所提出的HF-SENSE方法可以提高SENSE重建的图像质量。高通滤波器参数可以预先定义。使用这种图像重建方法，可以实现高加速因子，这将提高SENSE的临床适用性。本回顾性研究（HF-SENSE：一种使用高通滤波器的改进部分并行成像）已获得浙江大学医学院附属第二医院机构审查委员会的批准（伦理批准号2018-3）。所有图像受试者均已签署知情同意书，表明其了解风险并同意参与研究。
