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GPU 加速的 3D mipmap 用于实时可视化超声体数据。

GPU-accelerated 3D mipmap for real-time visualization of ultrasound volume data.

机构信息

Department of Computer and Information Engineering, Inha University, Incheon, Republic of Korea.

出版信息

Comput Biol Med. 2013 Oct;43(10):1382-9. doi: 10.1016/j.compbiomed.2013.07.014. Epub 2013 Jul 19.

Abstract

Ultrasound volume rendering is an efficient method for visualizing the shape of fetuses in obstetrics and gynecology. However, in order to obtain high-quality ultrasound volume rendering, noise removal and coordinates conversion are essential prerequisites. Ultrasound data needs to undergo a noise filtering process; otherwise, artifacts and speckle noise cause quality degradation in the final images. Several two-dimensional (2D) noise filtering methods have been used to reduce this noise. However, these 2D filtering methods ignore relevant information in-between adjacent 2D-scanned images. Although three-dimensional (3D) noise filtering methods are used, they require more processing time than 2D-based methods. In addition, the sampling position in the ultrasonic volume rendering process has to be transformed between conical ultrasound coordinates and Cartesian coordinates. We propose a 3D-mipmap-based noise reduction method that uses graphics hardware, as a typical 3D mipmap requires less time to be generated and less storage capacity. In our method, we compare the density values of the corresponding points on consecutive mipmap levels and find the noise area using the difference in the density values. We also provide a noise detector for adaptively selecting the mipmap level using the difference of two mipmap levels. Our method can visualize 3D ultrasound data in real time with 3D noise filtering.

摘要

超声体绘制是妇产科中用于可视化胎儿形状的一种有效方法。然而,为了获得高质量的超声体绘制,必须进行噪声去除和坐标转换。超声数据需要经过噪声过滤过程;否则,伪影和斑点噪声会导致最终图像质量下降。已经使用了几种二维(2D)噪声过滤方法来减少这种噪声。但是,这些 2D 滤波方法忽略了相邻 2D 扫描图像之间的相关信息。尽管使用了三维(3D)噪声滤波方法,但它们比基于 2D 的方法需要更多的处理时间。此外,在超声体绘制过程中,采样位置必须在锥形超声坐标和笛卡尔坐标之间进行转换。我们提出了一种基于 3D-mipmap 的降噪方法,该方法使用图形硬件,因为典型的 3D mipmap 生成所需的时间更短,存储空间也更小。在我们的方法中,我们比较连续 mipmap 级别上对应点的密度值,并使用密度值的差异找到噪声区域。我们还提供了一个噪声检测器,用于使用两个 mipmap 级别的差异自适应地选择 mipmap 级别。我们的方法可以使用 3D 噪声过滤实时可视化 3D 超声数据。

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