Tree-Encoded Conditional Random Fields for Image Synthesis.
Tree-Encoded Conditional Random Fields for Image Synthesis.
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DOI：
10.1007/978-3-319-19992-4_58
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发表时间：
2015
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影响因子：
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通讯作者：
Prince JL
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文献类型：
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作者：
Jog A;Carass A;Pham DL;Prince JL
磁共振成像（MRI）是临床和研究领域中用于神经成像的主要方式。MRI作为一种模态的巨大多功能性可能导致图像对比度、分辨率、噪声和伪影方面的巨大变化。可变性也可以表现为丢失或损坏的成像数据。最近已经提出了图像合成来均匀化和/或增强现有成像数据的质量，以便使它们更适合作为用于处理的一致输入。我们框架的图像合成问题作为一个推理问题的3-D连续值条件随机场（CRF）。我们将条件分布建模为高斯分布，定义二次关联和相互作用势，编码在回归树的叶子中。通过最大化训练数据的伪似然来学习这些二次势的参数。最后的综合是通过对这个模型的推理完成的。我们应用这种方法来合成T2加权图像从T1加权图像，显示出改善的合成质量相比，目前的图像合成方法。我们还合成了液体衰减反转恢复（FLAIR）图像，显示出与从真实的FLAIR获得的图像相似的分割。此外，我们生成的超分辨率FLAIR显示出改进的分割。
Magnetic resonance imaging (MRI) is the dominant modality for neuroimaging in clinical and research domains. The tremendous versatility of MRI as a modality can lead to large variability in terms of image contrast, resolution, noise, and artifacts. Variability can also manifest itself as missing or corrupt imaging data. Image synthesis has been recently proposed to homogenize and/or enhance the quality of existing imaging data in order to make them more suitable as consistent inputs for processing. We frame the image synthesis problem as an inference problem on a 3-D continuous-valued conditional random field (CRF). We model the conditional distribution as a Gaussian by defining quadratic association and interaction potentials encoded in leaves of a regression tree. The parameters of these quadratic potentials are learned by maximizing the pseudo-likelihood of the training data. Final synthesis is done by inference on this model. We applied this method to synthesize T2-weighted images from T1-weighted images, showing improved synthesis quality as compared to current image synthesis approaches. We also synthesized Fluid Attenuated Inversion Recovery (FLAIR) images, showing similar segmentations to those obtained from real FLAIRs. Additionally, we generated super-resolution FLAIRs showing improved segmentation.