We propose a practicable method for generating synthetic CT images from modified Dixon (mDixon) MR data for the challenging body section of the abdomen and extending into the pelvis. Attenuation correction is necessary to make quantitatively accurate PET but is problematic withPET/MR scanning as MR data lack the information of photon attenuation. Multiple methods were proposed to generate synthetic CT from MR images. However, due to the challenge to distinguish bone and air in MR signals, most existing methods require advanced MR sequences that entail long acquisition time and have limited availablity. To address this problem, we propose a voxel-oriented method for synthetic CT generation using both the transfer and patch learning (SCG-TPL). The overall framework of SCG-TPL includes three stages. Stage I extracts seven-dimensional texture features from mDixon MR images using the weighted convolutional sum; Stage II enlists the knowledge-leveraged transfer fuzzy c-means (KL-TFCM) clustering as well as the patch learning-oriented semi-supervised LapSVM classification to train multiple candidate four-tissue-type-identifiers (FTTIs); Stage III synthesizes CT for new patients’ mDixon images using the candidate FTTIs and voting principle. The significance of our method is threefold: (1) As the global model for patch learning, guiding by the referenced knowledge, KL-TFCM can credibly initialize MR data with overcoming the individual diversity. As the local complement, LapSVM can adaptively model each patch with low time and labor costs. (2) Jointly using the transfer KL-TFCM clustering and patch learning-oriented LapSVM classification, SCG-TPL is able to output accurate synthetic CT in the abdomen. (3) SCG-TPL synthesizes CT only using easily-obtainable mDixon MR images, which greatly facilitates its clinical practicability. Experimental studies on ten subjects’ mDixon MR data verified the superiority of our proposed method.

我们提出了一种可行的方法，该方法可从改良的Dixon（mDixon）MR数据生成具有挑战性的腹部部位并延伸到骨盆的合成CT图像。衰减校正对于制造定量准确的PET是必需的，但由于MR数据缺少光子衰减信息，因此PET / MR扫描存在问题。提出了多种方法来从MR图像生成合成CT。但是，由于要在MR信号中区分骨骼和空气存在挑战，因此大多数现有方法都需要高级MR序列，这些序列需要较长的采集时间，并且可用性有限。为了解决此问题，我们提出了一种使用转移和斑块学习（SCG-TPL）的合成CT生成的面向体素的方法。SCG-TPL的总体框架包括三个阶段。第一阶段使用加权卷积和从mDixon MR图像中提取7维纹理特征。第二阶段采用知识杠杆转移模糊c均值（KL-TFCM）聚类，以及面向补丁学习的半监督LapSVM分类，以训练多个候选四组织类型标识符（FTTI）；第三阶段使用候选FTTI和投票原理为新患者的mDixon图像合成CT。我们方法的意义有三方面：（1）作为补丁学习的全局模型，在参考知识的指导下，KL-TFCM可以可靠地初始化MR数据并克服个体差异。作为本地补充，LapSVM可以以较低的时间和人工成本对每个补丁进行自适应建模。（2）结合使用转移KL-TFCM聚类和面向补丁学习的LapSVM分类，SCG-TPL能够在腹部输出精确的合成CT。（3）SCG-TPL仅使用易于获得的mDixon MR图像合成CT，极大地促进了其临床实用性。对十个受试者的mDixon MR数据进行的实验研究证明了我们提出的方法的优越性。