The susceptibility of MRI to metallic objects leads to void MR signal and missing information around metallic implants. In addition, body truncation occurs in MR imaging for large patients who exceed the transaxial field-of-view of the scanner. Body truncation and metal artefacts translate to incomplete MRI-derived attenuation correction (AC) maps, consequently resulting in large quantification errors in PET imaging. In this work, we propose a deep learning-based approach to predict the missing information/regions in MR images affected by metallic artefacts and/or body truncation aiming at reducing quantification errors in PET/MRI. Twenty-five whole-body (WB) co-registered PET, CT, and MR images were used for training and evaluation of the object completion approach. CT-based attenuation corrected PET images were considered as reference for the quantitative evaluation of the proposed approach. Its performance was compared to the 3-class segmentation-based AC approach (containing background air, soft-tissue and lung) obtained from MR images. The metal-induced artefacts affected 8.1 ± 1.8% of the volume of the head region when using the 3-class AC maps. This error reduced to 0.9 ± 0.5% after application of object completion on MR images. Consequently, quantification errors in PET images reduced from −57.5 ± 11% to −18.5 ± 5% in the head region after metal artefact correction. The percentage of the torso volume affected by body truncation in the 3-class AC maps reduced from 9.8 ± 1.9% to 0.6 ± 0.3% after truncation compensation. PET quantification errors in the affected regions were also reduced from −45.5 ± 10% to −9.5 ± 3% after truncation compensation. The quantitative results demonstrated promising performance of the proposed approach towards the completion of MR images corrupted by metal artefacts and/or body truncation in the context of WB PET/MR imaging.