Considering bones in the attenuation map will considerably improve the accuracy of MR-guided attenuation correction in hybrid PET/MR to enable quantitative PET imaging on hybrid PET/MR technologies.

The National Cancer Institute (NCI) Cancer Research Data Commons (CRDC) aims to establish a national cloudbased data science infrastructure. Imaging Data Commons (IDC) is a new component of CRDC supported by the Cancer Moonshot™. The goal of IDC is to enable a broad spectrum of cancer researchers, with and without imaging expertise, to easily access and explore the value of de-identified imaging data and to support integrated analyses with non-imaging data. We achieve this goal by co-locating versatile imaging collections with cloudbased computing resources and data exploration, visualization, and analysis tools. The IDC pilot was released in October 2020 and is being continuously populated with radiology and histopathology collections. IDC provides access to curated imaging collections, accompanied by documentation, a user forum, and a growing number of analysis use cases that aim to demonstrate the value of a data commons framework applied to cancer imaging research.Significance: This study introduces NCI Imaging Data Commons, a new repository of the NCI Cancer Research Data Commons, which will support cancer imaging research on the cloud.

Multimodality image registration plays a crucial role in various clinical and research applications. The aim of this study is to present an optimized MR to CT whole‐body deformable image registration algorithm and its validation using clinical studies. A 3D intermodality registration technique based on B‐spline transformation was performed using optimized parameters of the elastix package based on the Insight Toolkit (ITK) framework. Twenty‐eight (17 male and 11 female) clinical studies were used in this work. The registration was evaluated using anatomical landmarks and segmented organs. In addition to 16 anatomical landmarks, three key organs (brain, lungs, and kidneys) and the entire body volume were segmented for evaluation. Several parameters — such as the Euclidean distance between anatomical landmarks, target overlap, Dice and Jaccard coefficients, false positives and false negatives, volume similarity, distance error, and Hausdorff distance — were calculated to quantify the quality of the registration algorithm. Dice coefficients for the majority of patients (>75%) were in the 0.8–1 range for the whole body, brain, and lungs, which satisfies the criteria to achieve excellent alignment. On the other hand, for kidneys, Dice coefficients for volumes of 25% of the patients meet excellent volume agreement requirement, while the majority of patients satisfy good agreement criteria (>0.6). For all patients, the distance error was in 0–10 mm range for all segmented organs. In summary, we optimized and evaluated the accuracy of an MR to CT deformable registration algorithm. The registered images constitute a useful 3D whole‐body MR‐CT atlas suitable for the development and evaluation of novel MR‐guided attenuation correction procedures on hybrid PET‐MR systems.PACS number: 07.05.Pj