Roy Snehashis, Chou Yi-Yu, Jog Amod, Butman John A, Pham Dzung L
Center for Neuroscience and Regenerative Medicine, Henry Jackson Foundation.
Department of Computer Science, The Johns Hopkins University.
Simul Synth Med Imaging. 2016 Oct;9968:146-156. doi: 10.1007/978-3-319-46630-9_15. Epub 2016 Sep 23.
Different magnetic resonance imaging pulse sequences are used to generate image contrasts based on physical properties of tissues, which provide different and often complementary information about them. Therefore multiple image contrasts are useful for multimodal analysis of medical images. Often, medical image processing algorithms are optimized for particular image contrasts. If a desirable contrast is unavailable, contrast synthesis (or modality synthesis) methods try to "synthesize" the unavailable constrasts from the available ones. Most of the recent image synthesis methods generate synthetic brain images, while whole head magnetic resonance (MR) images can also be useful for many applications. We propose an atlas based patch matching algorithm to synthesize -w whole head (including brain, skull, eyes etc) images from -w images for the purpose of distortion correction of diffusion weighted MR images. The geometric distortion in diffusion MR images due to in-homogeneous magnetic field are often corrected by non-linearly registering the corresponding = 0 image with zero diffusion gradient to an undistorted -w image. We show that our synthetic -w images can be used as a template in absence of a real -w image. Our patch based method requires multiple atlases with and to be registeLowRes to a given target . Then for every patch on the target, multiple similar looking matching patches are found on the atlas images and corresponding patches on the atlas images are combined to generate a synthetic of the target. We experimented on image data obtained from 44 patients with traumatic brain injury (TBI), and showed that our synthesized images produce more accurate distortion correction than a state-of-the-art registration based image synthesis method.
不同的磁共振成像脉冲序列用于根据组织的物理特性生成图像对比度,这些对比度提供了关于组织的不同且通常互补的信息。因此,多种图像对比度对于医学图像的多模态分析很有用。通常,医学图像处理算法是针对特定的图像对比度进行优化的。如果所需的对比度不可用,对比度合成(或模态合成)方法会尝试从可用的对比度中“合成”不可用的对比度。最近的大多数图像合成方法生成合成脑图像,而全脑磁共振(MR)图像在许多应用中也可能很有用。我们提出了一种基于图谱的补丁匹配算法,用于从 图像合成全脑(包括脑、颅骨、眼睛等)图像,以校正扩散加权磁共振图像的失真。由于磁场不均匀导致的扩散磁共振图像中的几何失真通常通过将具有零扩散梯度的相应 = 0 图像非线性配准到未失真的 图像来校正。我们表明,在没有真实 图像的情况下,我们的合成 图像可以用作模板。我们基于补丁的方法需要多个带有 和 的图谱,将其配准到给定的目标 。然后对于目标上的每个补丁,在图谱 图像上找到多个外观相似的匹配补丁,并将图谱 图像上的相应补丁组合起来生成目标的合成 。我们对从44名创伤性脑损伤(TBI)患者获得的图像数据进行了实验,结果表明,我们合成的 图像比一种基于配准的最新图像合成方法能产生更准确的失真校正。