Philips has introduced the world's first whole body sequential PET/MR system. We present the current status of MR-based attenuation correction (MRAC) technique. MRAC consists of MR image acquisition, segmentation, truncation compensation (TC), -value assignment, as well as correction for patient table and RF coils. These components have been described last year; this paper focuses on updates of the two most critical steps of MRAC: segmentation and TC. The segmentation algorithm attempts to distinguish 3 biological classes: air, lungs, and soft tissue. It combines an intensity-based region-growing technique with lung-model adaptation. For TC, the following three-step approach to correct for truncation in the MR-based attenuation maps has been developed and investigated: (A) Areas in the attenuation map which are possibly truncated are identified. (B) For these areas, an estimate of the outer patient contour is extracted from a registered PET image which is reconstructed without attenuation correction. (C) Truncation correction areas as extracted from the PET contours are added to attenuation map. The segmentation algorithm was applied to a number of datasets from a large pool of volunteers from multiple MR systems. The algorithm yields expected results except for susceptibility and motion artifacts. While the truncation compensation algorithm works for most cases, the robustness needs to be further improved.