title: Medical imaging informatics simulators: a tutorial.
abstract: OBJECTIVE
  A medical imaging informatics infrastructure (MIII) platform is an organized method of selecting tools and synthesizing data from HIS/RIS/PACS/ePR systems with the aim of developing an imaging-based diagnosis or treatment system. Evaluation and analysis of these systems can be made more efficient by designing and implementing imaging informatics simulators. This tutorial introduces the MIII platform and provides the definition of treatment/diagnosis systems, while primarily focusing on the development of the related simulators.
  
  
  METHODS
  A medical imaging informatics (MII) simulator in this context is defined as a system integration of many selected imaging and data components from the MIII platform and clinical treatment protocols, which can be used to simulate patient workflow and data flow starting from diagnostic procedures to the completion of treatment. In these processes, DICOM and HL-7 standards, IHE workflow profiles, and Web-based tools are emphasized. From the information collected in the database of a specific simulator, evidence-based medicine can be hypothesized to choose and integrate optimal clinical decision support components. Other relevant, selected clinical resources in addition to data and tools from the HIS/RIS/PACS and ePRs platform may also be tailored to develop the simulator. These resources can include image content indexing, 3D rendering with visualization, data grid and cloud computing, computer-aided diagnosis (CAD) methods, specialized image-assisted surgical, and radiation therapy technologies.
  
  
  RESULTS
  Five simulators will be discussed in this tutorial. The PACS-ePR simulator with image distribution is the cradle of the other simulators. It supplies the necessary PACS-based ingredients and data security for the development of four other simulators: the data grid simulator for molecular imaging, CAD-PACS, radiation therapy simulator, and image-assisted surgery simulator. The purpose and benefits of each simulator with respect to its clinical relevance are presented.
  
  
  CONCLUSIONS
  The concept, design, and development of these five simulators have been implemented in laboratory settings for education and training. Some of them have been extended to clinical applications in hospital environments.
authors.affiliation: IPILab, Department of Biomedical Engineering, Viterbi School of Engineering, University of Southern California, 734 W. Adams Blvd, Los Angeles, CA, 90089, USA, berniehkhuang@gmail.com.
authors.forename: H K
authors.initials: HK
authors.lastname: Huang
authors.forename: Ruchi
authors.initials: R
authors.lastname: Deshpande
authors.forename: Jorge
authors.initials: J
authors.lastname: Documet
authors.forename: Anh H
authors.initials: AH
authors.lastname: Le
authors.forename: Jasper
authors.initials: J
authors.lastname: Lee
authors.forename: Kevin
authors.initials: K
authors.lastname: Ma
authors.forename: Brent J
authors.initials: BJ
authors.lastname: Liu
mesh_terms: D003936:Diagnosis, Computer-Assisted / Q000295:instrumentation*; D003952:Diagnostic Imaging / Q000379:methods*; D006801:Humans; D017145:Models, Educational; D011871:Radiology / Q000193:education*; D011873:Radiology Information Systems
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