Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China; Shenzhen College of Advanced Technology, University of Chinese Academy of Sciences, Shenzhen, Guangdong 518055, China.
Shenzhen Institutes of Advanced Technology, Chinese Academy of Sciences, Shenzhen, Guangdong, 518055, China.
Comput Biol Med. 2021 Jul;134:104456. doi: 10.1016/j.compbiomed.2021.104456. Epub 2021 May 11.
The purpose of this study is to develop a practical stripe artifacts correction framework on three-dimensional (3-D) time-of-flight magnetic resonance angiography (TOF-MRA) obtained by multiple overlapping thin slab acquisitions (MOTSA) technology. In this work, the stripe artifacts in TOF-MRA were considered as a part of image texture. To separate the image structure and the texture, the relative total variation (RTV) was firstly employed to smooth the TOF-MRA for generating the template image with fewer image textures. Then a residual image was generated, which was the difference between the template image and the raw TOF-MRA. The residual image was served as the image texture, which contained the image details and stripe artifacts. Then, we obtained the artifact image from the residual image via a filter in a specific direction since the image artifacts appeared as stripes. The image details were then produced from the difference between the artifact image and the image texture. To produce the corrected images, we finally compensated the image details to the RTV smoothing image. The proposed method was continued until the stripe artifacts during the iteration vary as little as possible. The digital phantom and the real patients' TOF-MRA were used to test the approach. The spatial uniformity was increased from 74% to 82% and the structural similarity was improved from 86% to 98% in the digital phantom test by using the proposed algorithm. Our approach proved to be highly successful in eliminating stripe artifacts in real patient data tests while retaining image details. The proposed iterative framework on TOF-MRA stripe artifact correction is effective and appealing for enhancing the imaging performance of multi-slab 3-D acquisitions.
本研究旨在开发一种实用的三维（3-D）时间飞跃磁共振血管造影（TOF-MRA）条纹伪影校正框架，该框架基于多次重叠薄层采集（MOTSA）技术。在这项工作中，TOF-MRA 中的条纹伪影被视为图像纹理的一部分。为了分离图像结构和纹理，首先采用相对全变差（RTV）对 TOF-MRA 进行平滑处理，以生成具有较少图像纹理的模板图像。然后生成一个残差图像，该图像是模板图像和原始 TOF-MRA 之间的差值。残差图像用作图像纹理，其中包含图像细节和条纹伪影。然后，我们通过特定方向的滤波器从残差图像中获取伪影图像，因为图像伪影呈现为条纹。然后，从伪影图像和图像纹理之间的差异中生成图像细节。为了生成校正后的图像，我们最后将图像细节补偿到 RTV 平滑图像中。该方法继续进行，直到迭代过程中的条纹伪影变化尽可能小。使用数字体模和真实患者的 TOF-MRA 来测试该方法。在数字体模测试中，空间均匀性从 74%提高到 82%，结构相似性从 86%提高到 98%。我们的方法在真实患者数据测试中成功地消除了条纹伪影，同时保留了图像细节。该迭代框架对 TOF-MRA 条纹伪影校正有效且具有吸引力，可提高多层面 3-D 采集的成像性能。
