We propose a new method for generating synthetic CT images from modified Dixon (mDixon) MR data. The synthetic CT is used for attenuation correction (AC) when reconstructing PET data on abdomen and pelvis. While MR does not intrinsically contain any information about photon attenuation, AC is needed in PET/MR systems in order to be quantitatively accurate and to meet qualification standards required for use in many multi-center trials. Existing MR-based synthetic CT generation methods either use advanced MR sequences that have long acquisition time and limited clinical availability or use matching of the MR images from a newly scanned subject to images in a library of MR-CT pairs which has difficulty in accounting for the diversity of human anatomy especially in patients that have pathologies. To address these deficiencies, we present a five-phase interlinked method that uses mDixon MR acquisition and advanced machine learning methods for synthetic CT generation. Both transfer fuzzy clustering and active learning-based classification (TFC-ALC) are used. The significance of our efforts is fourfold: 1) TFC-ALC is capable of better synthetic CT generation than methods currently in use on the challenging abdomen using only common Dixon-based scanning. 2) TFC partitions MR voxels initially into the four groups regarding fat, bone, air, and soft tissue via transfer learning; ALC can learn insightful classifiers, using as few but informative labeled examples as possible to precisely distinguish bone, air, and soft tissue. Combining them, the TFC-ALC method successfully overcomes the inherent imperfection and potential uncertainty regarding the co-registration between CT and MR images. 3) Compared with existing methods, TFC-ALC features not only preferable synthetic CT generation but also improved parameter robustness, which facilitates its clinical practicability. Applying the proposed approach on mDixon-MR data from ten subjects, the average score of the mean absolute prediction deviation (MAPD) was 89.78±8.76 which is significantly better than the 133.17±9.67 obtained using the all-water (AW) method (p=4.11E-9) and the 104.97±10.03 obtained using the four-cluster-partitioning (FCP, i.e., external-air, internal-air, fat, and soft tissue) method (p=0.002). 4) Experiments in the PET SUV errors of these approaches show that TFC-ALC achieves the highest SUV accuracy and can generally reduce the SUV errors to 5% or less. These experimental results distinctively demonstrate the effectiveness of our proposed TFC-ALC method for the synthetic CT generation on abdomen and pelvis using only the commonly-available Dixon pulse sequence.

中文翻译：

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基于mDixon的合成CT生成，结合基于转移模糊聚类和基于主动学习的分类对腹部和骨盆进行PET衰减校正。

我们提出了一种从修改后的Dixon（mDixon）MR数据生成合成CT图像的新方法。当在腹部和骨盆上重建PET数据时，合成CT用于衰减校正（AC）。尽管MR本质上不包含有关光子衰减的任何信息，但是PET / MR系统中需要AC，以便定量准确并满足许多多中心试验中使用的鉴定标准。现有的基于MR的合成CT生成方法或者使用具有较长获取时间和有限的临床可用性的高级MR序列，或者使用将新扫描对象的MR图像与MR-CT对库中的图像进行匹配人体解剖结构的多样性，尤其是在具有病理学的患者中。为了解决这些缺陷，我们提出了一种五阶段互连方法，该方法使用mDixon MR采集和先进的机器学习方法来生成合成CT。使用转移模糊聚类和基于主动学习的分类（TFC-ALC）。我们努力的意义有四个方面：1）TFC-ALC能够比仅使用普通的基于Dixon的扫描方法对挑战性腹部当前使用的方法更好地生成合成CT。2）TFC通过转移学习将MR体素最初分为关于脂肪，骨骼，空气和软组织的四个组；ALC可以学习有洞察力的分类器，使用尽可能少但信息丰富的标记示例来准确地区分骨骼，空气和软组织。结合起来，TFC-ALC方法成功克服了CT和MR图像之间共配准的固有缺陷和潜在不确定性。3）与现有方法相比，TFC-ALC不仅具有更佳的合成CT生成能力，而且还具有更强的参数鲁棒性，从而具有临床实用性。将拟议的方法应用于来自十个受试者的mDixon-MR数据，平均绝对预测偏差（MAPD）的平均得分为89.78±8.76，明显好于使用全水（AW）方法获得的133.17±9.67（p ＝ 4.11E-9）和使用四聚类划分（FCP，即外部空气，内部空气，脂肪和软组织）方法获得的104.97±10.03（p = 0.002）。4）在这些方法的PET SUV错误中进行的实验表明，TFC-ALC可以达到最高的SUV精度，并且通常可以将SUV错误降低到5％或更少。这些实验结果鲜明地证明了我们提出的TFC-ALC方法仅使用通常可用的Dixon脉冲序列在腹部和骨盆上生成CT的有效性。