IBM estimates that 30% of the entire data in the world is medical information. Medical images occupy a significant portion of medical records with approximately 100 million scans in US and growing every year. In addition, the data size from each scan steadfastly increases as the image resolution improves. These BigData are not structured and due to lack of standardized imaging protocols, they are highly heterogeneous with different spatial resolutions, contrasts, slice orientations, etc. In this project, we will deelop a technology to structure and search medical imaging information, which will make the past data available for education and evidence-based clinical decision-making. In this grant, we will focus on brain MRI, which comprises the largest portion of MRI data. The target community will be physicians who make decisions and the patients will be the ultimate beneficiaries. Currently, radiological image data are stored in clinical database called PACS. The image data in PACS are not structured. Consequently, once the diagnosis of a patient is completed, most of the data in PACS are currently discarded in the archive. Radiologists rely on their experience and education to reach medical decisions. This is a typical problem in medical practice that calls for objective evidence-based medicine. There are many ongoing attempts to structure the text fields of PACS, which include natural language processing of free-text radiological reports, clinical information, and diagnosis. In our approach, we propose to structure the image data, not text fields, to support direct search of images. Namely, physicians will submit an image of a new patient and search past images with similar anatomical phenotypes. Then, the clinical reports of the retrieved data will be compiled for a statistical report of the diagnosis and prognosis. We believe this image structuration is the key to "unlock the vast amounts of information currently stored" in PACS and use them for education and modern evidence-based medical decisions.
The specific aims are;Objective 1: To develop and test the accuracy of high-throughput image structuration technologies Objective 2: To develop and test the image search engine Objective 3: Capacity Building Requirement: To develop prototype cloud system for data structuration / search services for research and educational purposes

Agency
National Institute of Health (NIH)
Institute
National Institute of Biomedical Imaging and Bioengineering (NIBIB)
Type
Research Project (R01)
Project #
5R01EB017638-02
Application #
8664845
Study Section
Special Emphasis Panel (ZRG1-BST-N (52))
Program Officer
Pai, Vinay Manjunath
Project Start
2013-06-01
Project End
2016-05-31
Budget Start
2014-06-01
Budget End
2015-05-31
Support Year
2
Fiscal Year
2014
Total Cost
$214,609
Indirect Cost
$80,630
Name
Johns Hopkins University
Department
Engineering (All Types)
Type
Schools of Engineering
DUNS #
001910777
City
Baltimore
State
MD
Country
United States
Zip Code
21218
Djamanakova, Aigerim; Tang, Xiaoying; Li, Xin et al. (2014) Tools for multiple granularity analysis of brain MRI data for individualized image analysis. Neuroimage 101:168-76