Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model.
Estimating CT Image From MRI Data Using Structured Random Forest and Auto-Context Model.
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DOI：
10.1109/tmi.2015.2461533
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发表时间：
2016-01
影响因子：
10.6
通讯作者：
Alzheimer's Disease Neuroimaging Initiative
中科院分区：
文献类型：
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作者：
Huynh T;Gao Y;Kang J;Wang L;Zhang P;Lian J;Shen D;Alzheimer's Disease Neuroimaging Initiative
计算机断层扫描(CT)成像是各种临床诊断和放射治疗计划的重要工具。由于CT图像的强度与正电子发射断层扫描(PET)的衰减系数直接相关，因此对于正电子发射断层扫描(PET)图像的衰减校正(AC)来说，CT图像强度是必不可少的。然而，由于CT扫描的辐射剂量相对较高，建议限制CT图像的采集。此外，在新的PET和磁共振(MR)成像扫描仪中，只有MR图像可用，不幸的是，这不能直接适用于交流。这些问题极大地推动了从同一对象的相应MR图像中可靠地估计CT图像的方法的发展。在本文中，我们提出了一种基于学习的方法来解决这个具有挑战性的问题。具体地说，我们首先将给定的MR图像分割成一组补丁。然后，对于每个斑块，我们使用结构化随机森林来直接预测作为结构化输出的CT斑块，其中还使用了新的集成模型来确保稳健预测。创新地设计了图像特征以实现多层次的敏感度，空间信息仅通过刚体对齐来集成，以帮助避免容易出错的主体间变形配准。此外，我们使用自动上下文模型来迭代地改进预测。最后，我们将所有预测的CT块组合在一起，以获得对给定MR图像的最终预测。我们在两个数据集上演示了我们的方法的有效性：人脑和前列腺图像。实验结果表明，该方法在各种场景下都能准确地预测CT图像，甚至对形状变化较大的图像也能准确预测，并且性能优于两种最先进的方法。
Computed tomography (CT) imaging is an essential tool in various clinical diagnoses and radiotherapy treatment planning. Since CT image intensities are directly related to positron emission tomography (PET) attenuation coefficients, they are indispensable for attenuation correction (AC) of the PET images. However, due to the relatively high dose of radiation exposure in CT scan, it is advised to limit the acquisition of CT images. In addition, in the new PET and magnetic resonance (MR) imaging scanner, only MR images are available, which are unfortunately not directly applicable to AC. These issues greatly motivate the development of methods for reliable estimate of CT image from its corresponding MR image of the same subject. In this paper, we propose a learning-based method to tackle this challenging problem. Specifically, we first partition a given MR image into a set of patches. Then, for each patch, we use the structured random forest to directly predict a CT patch as a structured output, where a new ensemble model is also used to ensure the robust prediction. Image features are innovatively crafted to achieve multi-level sensitivity, with spatial information integrated through only rigid-body alignment to help avoiding the error-prone inter-subject deformable registration. Moreover, we use an auto-context model to iteratively refine the prediction. Finally, we combine all of the predicted CT patches to obtain the final prediction for the given MR image. We demonstrate the efficacy of our method on two datasets: human brain and prostate images. Experimental results show that our method can accurately predict CT images in various scenarios, even for the images undergoing large shape variation, and also outperforms two state-of-the-art methods.