School of Electrical Engineering, Nanjing Xiaozhuang University, Nanjing 211171, China.
Comput Math Methods Med. 2021 Dec 7;2021:4645544. doi: 10.1155/2021/4645544. eCollection 2021.
Diffusion MRI (DMRI) plays an essential role in diagnosing brain disorders related to white matter abnormalities. However, it suffers from heavy noise, which restricts its quantitative analysis. The total variance (TV) regularization is an effective noise reduction technique that penalizes noise-induced variances. However, existing TV-based denoising methods only focus on the spatial domain, overlooking that DMRI data lives in a combined spatioangular domain. It eventually results in an unsatisfactory noise reduction effect. To resolve this issue, we propose to remove the noise in DMRI using graph total variance (GTV) in the spatioangular domain. Expressly, we first represent the DMRI data using a graph, which encodes the geometric information of sampling points in the spatioangular domain. We then perform effective noise reduction using the powerful GTV regularization, which penalizes the noise-induced variances on the graph. GTV effectively resolves the limitation in existing methods, which only rely on spatial information for removing the noise. Extensive experiments on synthetic and real DMRI data demonstrate that GTV can remove the noise effectively and outperforms state-of-the-art methods.
扩散磁共振成像（DMRI）在诊断与白质异常相关的脑疾病方面发挥着重要作用。然而，它受到噪声的严重影响，这限制了其定量分析。全变差（TV）正则化是一种有效的降噪技术，可惩罚噪声引起的方差。然而，现有的基于 TV 的去噪方法仅关注于空间域，忽略了 DMRI 数据存在于联合的空间角域中。这最终导致了不尽如人意的降噪效果。为了解决这个问题，我们提出在空间角域中使用图全变差（GTV）来去除 DMRI 中的噪声。具体来说，我们首先使用图来表示 DMRI 数据，该图编码了空间角域中采样点的几何信息。然后，我们使用强大的 GTV 正则化来进行有效的降噪，该正则化惩罚图上噪声引起的方差。GTV 有效地解决了现有方法仅依赖于空间信息来去除噪声的局限性。对合成和真实 DMRI 数据的广泛实验表明，GTV 可以有效地去除噪声，并优于最先进的方法。
