This proposal aims to continue the development of XNAT. XNAT is an imaging informatics platform designed to facilitate common management and productivity tasks for imaging and associated data. We will develop the next generation of XNAT technology to support the ongoing evolution of imaging research. Development will focus on modernizing and expanding the current system.
In Aim 1, we will implement new web application infrastructure that includes a new archive file management system, a new event bus to manage cross-service orchestration and a new Javascript library to simplify user interface development. We will also implement new core services, including a Docker Container service, a dynamic scripting engine, and a global XNAT federation.
In Aim 2, we will implement two innovative new capabilities that build on the services developed in Aim 1. The XNAT Publisher framework will streamline the process of data sharing by automating the creation and curation of data releases following best practices for data publication and stewardship. The XNAT Machine Learning framework will streamline the development and use of machine learning applications by integrating XNAT with the TensorFlow machine learning environment and implementing provenance and other monitoring features to help avoid the pitfalls that often plague machine learning efforts. For both Aim 1 and 2, all capabilities will be developed and evaluated in the context of real world scientific programs that are actively using the XNAT platform.
In Aim 3, we will provide extensive support to the XNAT community, including training workshops, online documentation, discussion forums, and . These activities will be targeted at both XNAT users and developers.

Public Health Relevance

Medical imaging is one of the key methods used by biomedical researchers to study human biology in health and disease. The imaging informatics platform described in this application will enable biomedical researchers to capture, analyze, and share imaging and related data. These capabilities address key bottlenecks in the pathway to discovering cures to complex diseases such as Alzheimer's disease, cancer, and heart disease.

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB009352-11
Application #
9772886
Study Section
Biodata Management and Analysis Study Section (BDMA)
Program Officer
Duan, Qi
Project Start
2009-09-01
Project End
2021-08-31
Budget Start
2019-09-01
Budget End
2020-08-31
Support Year
11
Fiscal Year
2019
Total Cost
Indirect Cost
Name
Washington University
Department
Radiation-Diagnostic/Oncology
Type
Schools of Medicine
DUNS #
068552207
City
Saint Louis
State
MO
Country
United States
Zip Code
63130
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