Big data for health.

This paper provides an overview of recent developments in big data in the context of biomedical and health informatics. It outlines the key characteristics of big data and how medical and health informatics, translational bioinformatics, sensor informatics, and imaging informatics will benefit from an integrated approach of piecing together different aspects of personalized information from a diverse range of data sources, both structured and unstructured, covering genomics, proteomics, metabolomics, as well as imaging, clinical diagnosis, and long-term continuous physiological sensing of an individual. It is expected that recent advances in big data will expand our knowledge for testing new hypotheses about disease management from diagnosis to prevention to personalized treatment. The rise of big data, however, also raises challenges in terms of privacy, security, data ownership, data stewardship, and governance. This paper discusses some of the existing activities and future opportunities related to big data for health, outlining some of the key underlying issues that need to be tackled.

Evaluation of carotid artery wall volume measurement using novel semiautomated analysis software.

PURPOSE: To evaluate semiautomated analysis software for measuring the total carotid arterial wall volume (TWV) as a measure of atheroma burden. MATERIALS AND METHODS: Semiautomated-software and manual analyses of TWV measured by cardiovascular magnetic resonance (CMR) were compared in two phantom models, 10 subjects with no known carotid artery disease, and eight subjects with known carotid disease. The subjects were scanned twice for reproducibility. RESULTS: In subjects with no known carotid disease, semiautomated analysis of 98% of slices showed an improved interstudy coefficient of variation (COV) compared to manual analysis of 50% of slices (4.0% vs. 6.2%, P = 0.02). The proportion of matched cross-sectional slices usable for TWV measurement was superior (99% vs. 49%, P = 0.005) and the median analysis time was shorter (31 minutes vs. 90 minutes, P < 0.001) using the semiautomated software. In subjects with known carotid disease, semiautomated (99% of slices) and manual (56% of slices) analyses had comparable interstudy COVs (4.1% vs. 3.9%, P = 0.01). However, the proportion of matched cross-sectional slices usable for TWV measurement was greater using semiautomated contouring (96% vs. 56%, P = 0.01). CONCLUSION: Carotid CMR measurement of TWV using novel semiautomated analysis software shows good reproducibility, enables greater coverage of arterial vessel wall length, and is considerably faster compared to manual contouring.

Progress towards patient-specific computational flow modeling of the left heart via combination of magnetic resonance imaging with computational fluid dynamics.

A combined computational fluid dynamics (CFD) and magnetic resonance imaging (MRI) methodology has been developed to simulate blood flow in a subject-specific left heart. The research continues from earlier experience in modeling the human left ventricle using time-varying anatomical MR scans. Breathing artifacts are reduced by means of a MR navigator echo sequence with feedback to the subject, allowing a near constant breath-hold diaphragm position. An improved interactive segmentation technique for the long- and short-axis anatomical slices is used. The computational domain is extended to include the proximal left atrium and ascending aorta as well as the left ventricle, and the mitral and aortic valve orifices are approximately represented. The CFD results show remarkable correspondence with the MR velocity data acquired for comparison purposes, as well as with previously published in vivo experiments (velocity and pressure). Coherent vortex formation is observed below the mitral valve, with a larger anterior vortex dominating the late-diastolic phases. Some quantitative discrepancies exist between the CFD and MRI flow velocities, owing to the limitations of the MR dataset in the valve region, heart rate differences in the anatomical and velocity acquisitions, and to certain phenomena that were not simulated. The CFD results compare well with measured ranges in literature.