MR image synthesis by contrast learning on neighborhood ensembles.
MR image synthesis by contrast learning on neighborhood ensembles.
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DOI：
10.1016/j.media.2015.05.002
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发表时间：
2015-08
影响因子：
10.9
通讯作者：
Prince JL
中科院分区：
文献类型：
--
作者：
Jog A;Carass A;Roy S;Pham DL;Prince JL
磁共振图像的自动处理是神经科学研究的重要组成部分。然而，即使是最好和最广泛使用的医学图像处理方法，当它们的输入图像以不同的脉冲序列采集时，也不会产生一致的结果。虽然已经引入了强度标准化和图像合成方法来解决这个问题，但它们的性能仍然依赖于用于获取图像的脉冲序列的知识和一致性。本文提出了一种首先估计被摄体图像脉冲序列参数的图像合成方法。然后，将所估计的参数与图集或训练图像的集合一起使用，以生成具有与对象图像相同对比度的新图集图像。该附加图像提供了合成图谱中包含的任何其他目标脉冲序列图像的理想来源。具体地，训练从新的图谱图像到目标图谱图像的非线性回归强度映射，然后将其应用于对象图像以产生图谱内的特定目标脉冲序列。使用该框架可以实现强度标准化和缺失组织对比的合成。在模拟数据和真实数据上对该方法进行了评估，结果表明该方法在强度标准化和合成方面都优于其他已有的方法。
Automatic processing of magnetic resonance images is a vital part of neuroscience research. Yet even the best and most widely used medical image processing methods will not produce consistent results when their input images are acquired with different pulse sequences. Although intensity standardization and image synthesis methods have been introduced to address this problem, their performance remains dependent on knowledge and consistency of the pulse sequences used to acquire the images. In this paper, an image synthesis approach that first estimates the pulse sequence parameters of the subject image is presented. The estimated parameters are then used with a collection of atlas or training images to generate a new atlas image having the same contrast as the subject image. This additional image provides an ideal source from which to synthesize any other target pulse sequence image contained in the atlas. In particular, a nonlinear regression intensity mapping is trained from the new atlas image to the target atlas image and then applied to the subject image to yield the particular target pulse sequence within the atlas. Both intensity standardization and synthesis of missing tissue contrasts can be achieved using this framework. The approach was evaluated on both simulated and real data, and shown to be superior in both intensity standardization and synthesis to other established methods.