A supervoxel based random forest synthesis framework for bidirectional MR/CT synthesis.
A supervoxel based random forest synthesis framework for bidirectional MR/CT synthesis.
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DOI：
10.1007/978-3-319-68127-6_4
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发表时间：
2017-09
期刊：
影响因子：
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通讯作者：
Prince JL
中科院分区：
文献类型：
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作者：
Zhao C;Carass A;Lee J;Jog A;Prince JL
合成磁共振（MR）和计算机断层扫描（CT）图像（彼此）对临床神经成像具有重要意义。MR到CT方向对于基于MRI的放射治疗计划和剂量计算至关重要，而CT到MR方向可以为图像处理任务提供真实的MRI的经济替代方案。另外，在两个方向上的合成可以增强MR/CT多模态图像配准。现有的方法都集中在合成CT从MR。在本文中，我们提出了一种基于多图集的混合方法来合成T1加权MR图像从CT和CT图像从T1加权MR图像使用一个共同的框架。该任务通过以下方式执行：（a）使用联合标签融合基于对象图像的超体素计算标签场;（B）使用随机森林分类器（RF-C）校正该结果;（c）使用马尔可夫随机场进行空间平滑;（d）使用一组RF回归器合成强度，每个标签训练一个RF回归器。使用一组六个注册的CT和MR图像对整个头部的算法进行评估。
Synthesizing magnetic resonance (MR) and computed tomography (CT) images (from each other) has important implications for clinical neuroimaging. The MR to CT direction is critical for MRI-based radiotherapy planning and dose computation, whereas the CT to MR direction can provide an economic alternative to real MRI for image processing tasks. Additionally, synthesis in both directions can enhance MR/CT multi-modal image registration. Existing approaches have focused on synthesizing CT from MR. In this paper, we propose a multi-atlas based hybrid method to synthesize T1-weighted MR images from CT and CT images from T1-weighted MR images using a common framework. The task is carried out by: (a) computing a label field based on supervoxels for the subject image using joint label fusion; (b) correcting this result using a random forest classifier (RF-C); (c) spatial smoothing using a Markov random field; (d) synthesizing intensities using a set of RF regressors, one trained for each label. The algorithm is evaluated using a set of six registered CT and MR image pairs of the whole head.