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Magnetic resonance imaging-guided attenuation correction in whole-body PET/MRI using a sorted atlas approach

机译：使用分类图谱方法在全身PET / MRI中进行磁共振成像引导的衰减校正

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Quantitative whole-body PET/MR imaging is challenged by the lack of accurate and robust strategies for attenuation correction. In this work, a new pseudo-CT generation approach, referred to as sorted atlas pseudo-CT (SAP), is proposed for accurate extraction of bones and estimation of lung attenuation properties. This approach improves the Gaussian process regression (GPR) kernel proposed by Hofmann et al, which relies on the information provided by a co-registered atlas (CT and MRI) using a GPR kernel to predict the distribution of attenuation coefficients. Our approach uses two separate GPR kernels for lung and non-lung tissues. For non-lung tissues, the co-registered atlas dataset was sorted on the basis of local normalized cross-correlation similarity to the target MR image to select the most similar image in the atlas for each voxel. For lung tissue, the lung volume was incorporated in the GPR kernel taking advantage of the correlation between lung volume and corresponding attenuation properties to predict the attenuation coefficients of the lung. In the presence of pathological tissues in the lungs, the lesions are segmented on PET images corrected for attenuation using MRI-derived three-class attenuation map followed by assignment of soft-tissue attenuation coefficient. The proposed algorithm was compared to other techniques reported in the literature including Hofmann's approach and the three-class attenuation correction technique implemented on the Philips Ingenuity TF PET/MR where CT-based attenuation correction served as reference. Fourteen patients with head and neck cancer undergoing PET/CT and PET/MR examinations were used for quantitative analysis. SUV measurements were performed on 12 normal uptake regions as well as high uptake malignant regions. Moreover, a number of similarity measures were used to evaluate the accuracy of extracted bones. The Dice similarity metric revealed that the extracted bone improved from 0.58 +/- 0.09 to 0.65 +/- 0.07 when using the SAP technique compared to Hofmann's approach. This enabled to reduce the SUVmean bias in bony structures for the SAP approach to -1.7 +/- 4.8% as compared to -7.3 +/- 6.0% and -27.4 +/- 10.1% when using Hofmann's approach and the three-class attenuation map, respectively. Likewise, the three-class attenuation map produces a relative absolute error of 21.7 +/- 11.8% in the lungs. This was reduced on average to 15.8 +/- 8.6% and 8.0 +/- 3.8% when using Hofmann's and SAP techniques, respectively. The SAP technique resulted in better overall PET quantification accuracy than both Hofmann's and the three-class approaches owing to the more accurate extraction of bones and better prediction of lung attenuation coefficients. Further improvement of the technique and reduction of the computational time are still required. (C) 2016 Elsevier B.V. All rights reserved.
机译：缺乏定量，可靠的衰减校正策略对全身PET / MR成像提出了挑战。在这项工作中，提出了一种新的伪CT生成方法，称为分类图集伪CT（SAP），用于精确提取骨骼和评估肺衰减特性。这种方法改进了Hofmann等人提出的高斯过程回归（GPR）内核，该内核依赖于使用GPR内核共同注册的图集（CT和MRI）提供的信息来预测衰减系数的分布。我们的方法对肺和非肺组织使用两个单独的GPR谷粒。对于非肺组织，根据与目标MR图像的局部归一化互相关相似度对共同注册的图集数据集进行排序，以为每个体素选择图集中最相似的图像。对于肺组织，利用肺体积和相应衰减特性之间的相关性将肺体积合并到GPR内核中，以预测肺的衰减系数。在肺中存在病理组织的情况下，将病变在PET图像上进行分割，并使用MRI衍生的三类衰减图对衰减进行校正，然后分配软组织衰减系数。将该算法与文献中报道的其他技术进行了比较，包括霍夫曼方法和在Philips Ingenuity TF PET / MR上实现的三级衰减校正技术，其中基于CT的衰减校正作为参考。接受PET / CT和PET / MR检查的14例头颈癌患者被用于定量分析。在12个正常摄取区域以及高摄取恶性区域上进行了SUV测量。此外，许多相似性度量用于评估提取骨骼的准确性。 Dice相似性度量标准显示，与Hofmann方法相比，使用SAP技术时，提取的骨骼从0.58 +/- 0.09改善到0.65 +/- 0.07。与使用霍夫曼方法和三级衰减法时的-7.3 +/- 6.0％和-27.4 +/- 10.1％相比，SAP方法的骨结构中的SUVmean偏差可降低至-1.7 +/- 4.8％。地图。同样，三级衰减图在肺中产生的相对绝对误差为21.7 +/- 11.8％。使用霍夫曼技术和SAP技术时，平均平均降低到15.8 +/- 8.6％和8.0 +/- 3.8％。 SAP技术比霍夫曼方法和三类方法都具有更好的总体PET定量准确度，这是因为骨骼的提取更加准确，并且肺衰减系数的预测更好。仍然需要技术的进一步改进和减少计算时间。 （C）2016 Elsevier B.V.保留所有权利。

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