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.