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首页> 外文期刊>Journal of Digital Imaging >Shape-Adaptive DCT for Denoising of 3D Scalar and Tensor Valued Images
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Shape-Adaptive DCT for Denoising of 3D Scalar and Tensor Valued Images

机译：用于3D标量和张量值图像去噪的形状自适应DCT

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During the last ten years or so, diffusion tensor imaging has been used in both research and clinical medical applications. To construct the diffusion tensor images, a large set of direction sensitive magnetic resonance image (MRI) acquisitions are required. These acquisitions in general have a lower signal-to-noise ratio than conventional MRI acquisitions. In this paper, we discuss computationally effective algorithms for noise removal for diffusion tensor magnetic resonance imaging (DTI) using the framework of 3-dimensional shape-adaptive discrete cosine transform. We use local polynomial approximations for the selection of homogeneous regions in the DTI data. These regions are transformed to the frequency domain by a modified discrete cosine transform. In the frequency domain, the noise is removed by thresholding. We perform numerical experiments on 3D synthetical MRI and DTI data and real 3D DTI brain data from a healthy volunteer. The experiments indicate good performance compared to current state-of-the-art methods. The proposed method is well suited for parallelization and could thus dramatically improve the computation speed of denoising schemes for large scale 3D MRI and DTI.
机译：在最近十年左右的时间里，扩散张量成像已用于研究和临床医学应用中。为了构造扩散张量图像，需要大量的方向敏感磁共振图像（MRI）采集。这些采集通常比常规的MRI采集具有更低的信噪比。在本文中，我们讨论了使用3维形状自适应离散余弦变换框架的，用于扩散张量磁共振成像（DTI）的噪声去除的计算有效算法。我们使用局部多项式近似来选择DTI数据中的同质区域。通过修改的离散余弦变换将这些区域变换到频域。在频域中，通过阈值消除噪声。我们对来自健康志愿者的3D综合MRI和DTI数据以及真实3D DTI脑数据进行了数值实验。与目前的最新方法相比，实验表明性能良好。所提出的方法非常适合并行化，因此可以极大地提高大规模3D MRI和DTI的去噪方案的计算速度。

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