Integration of XNAT/PACS, DICOM, and Research Software for Automated Multi-modal Image Analysis

Yurui Gao; Scott S Burns; Carolyn B Lauzon; Andrew E Fong; Terry A James; Joel F Lubar; Robert W Thatcher; David A Twillie; Michael D Wirt; Marc A Zola; Bret W Logan; Adam W Anderson; Bennett A Landman

doi:10.1117/12.2007621

Integration of XNAT/PACS, DICOM, and Research Software for Automated Multi-modal Image Analysis

Proc SPIE Int Soc Opt Eng. 2013 Mar 29:8674:10.1117/12.2007621. doi: 10.1117/12.2007621.

Authors

Affiliations

¹ Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 37235 ; Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA 37235.
² Vanderbilt Kennedy Center, Vanderbilt University, Nashville, TN, USA 37235.
³ Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA 37235 ; Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA 37235.
⁴ Blanchfield Army Community Hospital, Fort Campbell, KY, USA 42223.
⁵ Southeastern Biofeedback Institute, Inc., Knoxville, TN, USA 37996.
⁶ Applied Neuroscience, Inc., St. Petersburg, FL, USA 33708.
⁷ Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 37235 ; Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA 37235 ; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA 37235.
⁸ Biomedical Engineering, Vanderbilt University, Nashville, TN, USA 37235 ; Institute of Imaging Science, Vanderbilt University, Nashville, TN, USA 37235 ; Electrical Engineering and Computer Science, Vanderbilt University, Nashville, TN, USA 37235 ; Radiology and Radiological Sciences, Vanderbilt University, Nashville, TN, USA 37235.

Abstract

Traumatic brain injury (TBI) is an increasingly important public health concern. While there are several promising avenues of intervention, clinical assessments are relatively coarse and comparative quantitative analysis is an emerging field. Imaging data provide potentially useful information for evaluating TBI across functional, structural, and microstructural phenotypes. Integration and management of disparate data types are major obstacles. In a multi-institution collaboration, we are collecting electroencephalogy (EEG), structural MRI, diffusion tensor MRI (DTI), and single photon emission computed tomography (SPECT) from a large cohort of US Army service members exposed to mild or moderate TBI who are undergoing experimental treatment. We have constructed a robust informatics backbone for this project centered on the DICOM standard and eXtensible Neuroimaging Archive Toolkit (XNAT) server. Herein, we discuss (1) optimization of data transmission, validation and storage, (2) quality assurance and workflow management, and (3) integration of high performance computing with research software.

Keywords: XNAT; automatic multi-modal image analysis; traumatic brain injury.

Abstract

Grants and funding