Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space.
Denoising of Diffusion MRI Data via Graph Framelet Matching in x-q Space.
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DOI：
10.1109/tmi.2019.2915629
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发表时间：
2019-12
影响因子：
10.6
通讯作者：
Yap PT
中科院分区：
文献类型：
--
作者：
Chen G;Dong B;Zhang Y;Lin W;Shen D;Yap PT
弥散磁共振成像（DMRI）由于与水分子运动相关的MR信号衰减而遭受较低的信噪比（SNR）。为了提高信噪比，非局部均值（NLM）算法在降噪方面表现出了最先进的性能。然而，现有的NLM算法没有明确考虑DMRI信号可以随局部纤维取向显著变化的事实。因此，天真地应用NLM可能会模糊细微结构并加剧部分体积效应。为了克服这一限制，我们通过在非平坦域中执行邻域匹配并利用来自x空间（空间域）和q空间（波矢量域）的信息去除噪声来改进NLM。具体来说，我们首先使用图对q空间采样域进行编码。然后，我们执行图小框架变换来提取x-q空间中每个采样点的鲁棒旋转不变特征。所得到的功能是用于强大的邻域匹配，以定位经常性的信息。最后，我们通过NLM框架去除噪声。为了适应多线圈MR成像中的各种类型的噪声，我们在去噪之前对信号进行变换，使其呈高斯分布，从而以无偏的方式进行噪声去除。我们的方法能够更有效地定位具有不同方向的白色物质结构中的递归信息，避免了天真地应用NLM所造成的模糊效果。对合成的、重复获取的和婴儿DMRI数据的实验表明，我们的方法能够在有效去除噪声的同时保留细微结构。
Diffusion magnetic resonance imaging (DMRI) suffers from lower signal-to-noise-ratio (SNR) due to MR signal attenuation associated with the motion of water molecules. To improve SNR, the non-local means (NLM) algorithm has demonstrated state-of-the-art performance in noise reduction. However, existing NLM algorithms do not take into account explicitly the fact that DMRI signal can vary significantly with local fiber orientations. Applying NLM naïvely can hence blur subtle structures and aggravate partial volume effects. To overcome this limitation, we improve NLM by performing neighborhood matching in non-flat domains and removing noise with information from both x-space (spatial domain) and q-space (wavevector domain). Specifically, we first encode the q-space sampling domain using a graph. We then perform graph framelet transforms to extract robust rotation-invariant features for each sampling point in x-q space. The resulting features are employed for robust neighborhood matching to locate recurrent information. Finally, we remove noise via an NLM framework. To adapt to the various types of noise in multi-coil MR imaging, we transform the signal before denoising so that it is Gaussian-distributed, allowing noise removal to be carried out in an unbiased manner. Our method is able to more effectively locate recurrent information in white matter structures with different orientations, avoiding the blurring effects caused by naïvely applying NLM. Experiments on synthetic, repetitively-acquired, and infant DMRI data demonstrate that our method is able to preserve subtle structures while effectively removing noise.