Reconstruction of 3D Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using Nonlocal Means
Reconstruction of 3D Dynamic Contrast-Enhanced Magnetic Resonance Imaging Using Nonlocal Means
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DOI：
10.1002/jmri.22358
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发表时间：
2010-11-01
影响因子：
4.4
通讯作者：
DiBella, Edward V. R.
中科院分区：
文献类型：
--
作者：
Adluru, Ganesh;Tasdizen, Tolga;DiBella, Edward V. R.
目的：开发和测试一种基于非局部均值的肿瘤欠采样三维动态对比增强（DCE）磁共振成像（MRI）重建算法。材料和方法：我们提出了一种基于最近提出的非局部均值（NLM）滤波器的重建技术，该技术可以放松动态成像中空间和时间分辨率的权衡。与NLM用于图像去噪的原始应用不同，这里的MR重建框架可以从欠采样k空间数据中提供高质量的图像。该方法基于对像素的邻域而不是单个像素的相似性约束。将该方法应用于乳腺和脑肿瘤数据集的欠采样三维DCE成像，并将结果与滑动窗口重建和使用图像总变异约束的压缩感知方法进行了比较。结果：在保留时空特征的情况下，该方法可获得多达5个欠采样因子。NLM重建方法比滑动窗口和全变分约束重建技术具有更好的性能。结论：该重构框架能从欠采样的DCE MRI数据中获得高质量的图像，具有提高DCE肿瘤成像质量的潜力。
Purpose: To develop and test a nonlocal means-based reconstruction algorithm for undersampled 3D dynamic contrast-enhanced (DCE) magnetic resonance imaging (MRI) of tumors.Materials and Methods: 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.Results: 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.Conclusion: 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.