The Veterans Precision Oncology Data Commons: Transforming VA data into a national resource for research in precision oncology.

The Department of Veterans Affairs (VA) has a strong track record providing high-quality, evidence-based care to cancer patients. In order to accelerate discoveries that will further improve care for Veterans with cancer, the VA has partnered with the Center for Translational Data Science at the University of Chicago and the Open Commons Consortium to establish a data sharing platform, the Veterans Precision Oncology Data Commons (VPODC). The VPODC makes clinical, genomic, and imaging data from the VA available to the research community at large. In this paper, we detail our motivation for data sharing, describe the VPODC, and outline our collaboration model. By transforming VA data into a national resource for research in precision oncology, the VPODC seeks to foster innovation through collaboration and resource sharing that will ultimately lead to improved care for Veterans with cancer.

Morphological atlases of knee cartilage: shape indices to analyze cartilage degradation in osteoarthritic and non-osteoarthritic population.

3D magnetic resonance imaging of the articular cartilage allows accurate morphological assessment of the cartilage with relevance for identifying osteoarthritis (OA) status and to monitor progression and response to treatment. We propose the creation of morphological atlases of the cartilage using normal subjects segregated by age, sex, and gender. These atlases capture the variation of shape in normal subjects and are then used to classify new imaging studies as belonging to ;normal (asymptomatic of OA)' or ;abnormal (symptomatic of OA)'. The classification is performed by (i) analysis of the 3D deformation field required to move voxels to their corresponding locations in the atlas. Deformations beyond +/-2SD of normal variations constitute regions with large morphological changes; (ii) generating active shape models from the normal subject data and using the shape coefficients to classify cartilage morphology. The methodology is evaluated with an atlas of 20 normal subjects in one sub-type and testing the classification potential with 3 subjects symptomatic and 3 subjects asymptomatic of OA.

Texture measure from low resolution MR images to determine trabecular bone integrity in osteoporosis.

Bone architectural information can be derived from structural indices calculated from high resolution magnetic resonance (MR) images. However, high resolution scans are time consuming and prone to motion artifacts and hence are not routinely feasible. The purpose of this study is to determine if a correlation exists between 3D structural indices calculated from high-resolution MR images and 3D co-occurrence texture features calculated from lower resolution MR images. Regression analysis indicates a strong correlation between the structural indices and the texture features. This study highlights the potential of using surrogate texture markers extracted from readily acquired clinical MR images to quantify bone architecture, circumventing the need for high resolution MR imaging.

Quantitative metrics for evaluating parallel acquisition techniques in diffusion tensor imaging at 3 Tesla.

OBJECTIVES: Single-shot echo-planar based diffusion tensor imaging is prone to geometric and intensity distortions. Parallel imaging is a means of reducing these distortions while preserving spatial resolution. A quantitative comparison at 3 T of parallel imaging for diffusion tensor images (DTI) using k-space (generalized auto-calibrating partially parallel acquisitions; GRAPPA) and image domain (sensitivity encoding; SENSE) reconstructions at different acceleration factors, R, is reported here. MATERIALS AND METHODS: Images were evaluated using 8 human subjects with repeated scans for 2 subjects to estimate reproducibility. Mutual information (MI) was used to assess the global changes in geometric distortions. The effects of parallel imaging techniques on random noise and reconstruction artifacts were evaluated by placing 26 regions of interest and computing the standard deviation of apparent diffusion coefficient and fractional anisotropy along with the error of fitting the data to the diffusion model (residual error). RESULTS: The larger positive values in mutual information index with increasing R values confirmed the anticipated decrease in distortions. Further, the MI index of GRAPPA sequences for a given R factor was larger than the corresponding mSENSE images. The residual error was lowest in the images acquired without parallel imaging and among the parallel reconstruction methods, the R = 2 acquisitions had the least error. The standard deviation, accuracy, and reproducibility of the apparent diffusion coefficient and fractional anisotropy in homogenous tissue regions showed that GRAPPA acquired with R = 2 had the least amount of systematic and random noise and of these, significant differences with mSENSE, R = 2 were found only for the fractional anisotropy index. CONCLUSION: Evaluation of the current implementation of parallel reconstruction algorithms identified GRAPPA acquired with R = 2 as optimal for diffusion tensor imaging.