Design, implementation and operation of a multimodality research imaging informatics repository

Toan D Nguyen,Parnesh Raniga,Gary F Egan,David G Barnes

doi:10.1186/2047-2501-3-s1-s6

Abstract

BackgroundBiomedical imaging research increasingly involves acquiring, managing and processing large amounts of distributed imaging data. Integrated systems that combine data, meta-data and workflows are crucial for realising the opportunities presented by advances in imaging facilities.MethodsThis paper describes the design, implementation and operation of a multi-modality research imaging data management system that manages imaging data obtained from biomedical imaging scanners operated at Monash Biomedical Imaging (MBI), Monash University in Melbourne, Australia. In addition to Digital Imaging and Communications in Medicine (DICOM) images, raw data and non-DICOM biomedical data can be archived and distributed by the system. Imaging data are annotated with meta-data according to a study-centric data model and, therefore, scientific users can find, download and process data easily.ResultsThe research imaging data management system ensures long-term usability, integrity inter-operability and integration of large imaging data. Research users can securely browse and download stored images and data, and upload processed data via subject-oriented informatics frameworks including the Distributed and Reflective Informatics System (DaRIS), and the Extensible Neuroimaging Archive Toolkit (XNAT).

Highlights

Biomedical imaging research increasingly involves acquiring, managing and processing large amounts of distributed imaging data
In this paper we describe the implementation of the informatics systems and data flows at the Monash Biomedical Imaging (MBI) facility at Monash University
In order to the access to the systems, hide differences between data models and enforce some meta-data entry, we have developed python classes that map onto the PSSD model for interacting with Distributed and Reflective Informatics System (DaRIS) and extensible neuroimaging archive toolkit (XNAT)

Summary

Introduction

Biomedical imaging research increasingly involves acquiring, managing and processing large amounts of distributed imaging data. Large multi-modal imaging studies can result in terabyte (TB) size data collections [6], most research studies generate data in the megabyte (MB) to gigabyte (GB) range per subject. Many of today’s high profile biomedical imaging studies have hundreds to thousands of participants [7,8,9]. Many of these studies are longitudinal in nature and collect imaging data at multiple time points per subject. This multiplier effect results in a large collection of data that must be recorded per subject. Along with the imaging data, non-imaging and meta-data may collected and should be stored and directly associated with the image data, especially if the data will be mined and/or shared [10]

Methods

Results

Discussion

Conclusion