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Patch Based Synthesis of Whole Head MR Images: Application To EPI Distortion Correction

机译:基于补丁的全头MR图像合成:在EPI失真校正中的应用

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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 T_2-w whole head (including brain, skull, eyes etc.) images from T_1 - w images for the purpose of distortion correction of diffusion weighted MR images. The geometric distortion in diffusion MR images due to inhomogeneous B_0 magnetic field are often corrected by non-linearly registering the corresponding b = 0 image with zero diffusion gradient to an undistorted T_2 - w image. We show that our synthetic T_2-w images can be used as a template in absence of a real T_2-w image. Our patch based method requires multiple atlases with T_1 and T_2 to be registered to a given target T_1. Then for every patch on the target, multiple similar looking matching patches are found on the atlas T_1 images and corresponding patches on the atlas T_2 images are combined to generate a synthetic T_2 of the target. We experimented on image data obtained from 44 patients with traumatic brain injury (TBI), and showed that our synthesized T_2 images produce more accurate distortion correction than a state-of-the-art registration based image synthesis method.
机译:基于组织的物理特性,不同的磁共振成像脉冲序列用于生成图像对比度,从而提供有关它们的不同且通常是互补的信息。因此,多个图像对比度可用于医学图像的多峰分析。通常,医学图像处理算法针对特定的图像对比度进行了优化。如果没有所需的对比度,则对比度合成(或模态合成)方法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都会生成合成的大脑图像,而全头磁共振(MR)图像也可用于许多应用程序。我们提出了一种基于图集的补丁匹配算法,从T_1-w图像合成T_2-w整个头部(包括大脑,头骨,眼睛等)图像,以对扩散加权MR图像进行畸变校正。通常通过将具有零扩散梯度的对应b = 0图像非线性配准到未畸变的T_2-w图像来校正由于不均匀的B_0磁场而导致的扩散MR图像中的几何畸变。我们表明,我们的合成T_2-w图像可以在没有实际T_2-w图像的情况下用作模板。我们基于补丁的方法需要将多个带有T_1和T_2的地图集注册到给定的目标T_1。然后,对于目标上的每个补丁,在图集T_1图像上找到多个外观相似的匹配补丁,并将图集T_2图像上的对应补丁组合在一起以生成目标的合成T_2。我们对44例颅脑外伤患者的图像数据进行了实验,结果表明,与基于最新配准的图像合成方法相比,我们合成的T_2图像可产生更准确的失真校正。

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