UCAIR, Department of Radiology, University of Utah, Salt Lake City, Utah 84108, USA.
J Magn Reson Imaging. 2010 Nov;32(5):1217-27. doi: 10.1002/jmri.22358.
To develop and test a nonlocal means-based reconstruction algorithm for undersampled 3D dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of tumors.
We propose a reconstruction technique that is based on the recently proposed nonlocal means (NLM) filter which can relax trade-offs in spatial and temporal resolutions in dynamic imaging. Unlike the original application of NLM for image denoising, the MR reconstruction framework here can offer high-quality images from undersampled k-space data. The method is based on enforcing similarity constraints in terms of neighborhoods of pixels rather than individual pixels. The method was applied on undersampled 3D DCE imaging of breast and brain tumor datasets and the results were compared to sliding window reconstructions and to a compressed sensing method using total variation constraints on the images.
Undersampling factors of up to five were obtained with the proposed approach while preserving the spatial and temporal characteristics. The NLM reconstruction method offered improved performance over the sliding window and the total variation constrained reconstruction techniques.
The reconstruction framework here can give high-quality images from undersampled DCE MRI data and has the potential to improve the quality of DCE tumor imaging.
开发并测试一种基于非局部均值的重建算法，用于对肿瘤的欠采样三维动态对比增强磁共振成像（DCE MRI）进行重建。
我们提出了一种重建技术，该技术基于最近提出的非局部均值（NLM）滤波器，该滤波器可以在动态成像中放宽空间和时间分辨率的权衡。与 NLM 最初用于图像去噪的应用不同，这里的磁共振重建框架可以从欠采样的 k 空间数据中提供高质量的图像。该方法基于在像素邻域而不是单个像素上施加相似性约束。该方法应用于乳腺和脑肿瘤数据集的欠采样三维 DCE 成像，将结果与滑动窗口重建以及使用图像全变差约束的压缩感知方法进行了比较。
该方法可以在保持空间和时间特征的情况下，获得高达五倍的欠采样因子。NLM 重建方法在性能上优于滑动窗口和全变差约束重建技术。
这里的重建框架可以从欠采样的 DCE MRI 数据中获得高质量的图像，并有潜力改善 DCE 肿瘤成像的质量。
