# Presentation: 227023

## Imaging Workspace

***An Overview***

- Eliot L. Siegel, MD
- Imaging Workspace Lead SME
**January 23, 2008**

## Introduction

**Imaging has been separate island with its own standards (DICOM)**
- DICOM allows high level of interoperability among clinical imaging modalities such as CT and MRI
- Patient centered and very limited for data mining and research
- Completely separate from world of XML, service oriented architecture, Grid Computing
- Different vendors algorithms provide different results with no standards for annotation and image mark-up

## Goals of the Imaging Workspace

**The In Vivo Imaging Workspace was added to the caBIG program in April of 2005:**
**1. To advance imaging informatics for treatment of patients with cancer.**
**2.  Leverage the caBIG technology to share images in a variety of settings.**
**3. Strive towards a standardized way to evaluate tumor change.**
**4. Facilitate secure and easy sharing of images within the cancer community.**

## National Cancer Imaging Archive

- The NCIA network-accessible "in vivo image repository" provides image archives to assist development and validation of imaging software tools
- Multiple image libraries currently stored on archive including LIDC
- Not only repository, but software for NCIA is free and open source
- Federated system so single inquiry can produce responses from multiple repositories
- Includes visualization tool

## What are the Imaging Workspace “Products”?

## eXtensible Imaging Platform (XIP) Allows Easy Sharing of Image Enhancement/Analysis/Visualization Algorithms

**XIP Application**

**(Can be replaced with any  DICOM WG23-compatible Host)**

- XIP Host Adapter

- XIP Modules
- Host Independent

**WG23**

**XIP Host**

**WG23**

**WG23**

- Web-based Application

- Medical Imaging Workstation

- Standalone Application

- Distribute

- Distribute

- DICOM, HL7, & otherservices per IHE

**caGRID Services via****Imaging Middleware**

- XIP Application Builder

- XIP Class Library
- Auto Conversion Tool

- Host-Specific Plug-in Libs

**WG23**

- Distribute

**ITK**

**VTK**

**XIP LIB**

**. . .**

- A free and open source platform that facilitates the sharing not of images and other patient data but of image display, processing, and analysis algorithms themselves.

## Imaging Middleware (including GridCAD and Virtual PACS)

- Middleware provides connection between DICOM and Grid computing
- The GRID has tremendous potential to promote interoperability, improve security, and support more efficient sharing of image data and software algorithms
- Middleware projects such as CAD (computer aided detection) for lung nodules on CT scans demonstrate the power and potential of GRID computing

## Annotations and Imaging Markup (AIM)

- The first project of its kind that we’re aware of to propose/create a “standard” means of adding information/knowledge to an image in a clinical environment in which there is currently chaos in order to create a future in which image content can be easily and automatically searched.

## Algorithm Validation Tools (AVT)

- BaselineMax Diameter 36.2mmVolume 6.1cm3

- Baseline +20 weeksMax Diameter 32.6mmVolume 9.48cm3  55% increase

## DICOM Ontology

- A single common reference information model for DICOM
- Unify and make explicit all the key entities and relations in DICOM in a human-usable but machine-processable format.
- Represent the *existing DICOM model*, whether it be implicit or explicit, as an ontology.

## Query Formulation

- Query Formulation Engine will translate user queries that are formulated using the QueryTool UI into an ontology-based query graph.

- Create an Imaging Query Formulation tool
- Automates the creation of ontology-based queries to image resources

## Imaging Workspace and DSIC

**Imaging has similar challenges to other workspaces with regards to data sharing and intellectual capital**
- Culture of sharing information
- IRB approval
- IT local policies for security/privacy including HIPAA challenges
- Unique face challenge for diagnostic images
- Can images of the face be de-identified?
- Dan Steinberg lecture at first face to face threw down the gauntlet

## 6

- 1

- 2

- 3

- 4

- 5

## Major Implications: Types of Studies

**CT studies**
- Maxillofacial sinuses
- Cerebral vasculature
- Brain
- Face
- Head
**MRI studies**

## Use Cases for De-Identified Files

**3D + multi-planar reconstruction (MPR)**
- Clinical diagnosis
- Clinical trials
- Research repositories
- Therapeutic planning
- Teaching
- Teaching files
- Clinical decision support

## The Study

**Are 3D facial reconstructed images the same as full face photographic images?**
**IRB approved, prospective**
**Informed consents were obtained for all digital photos and 3D images**

## The Study

**179 participants enrolled**
- 29 digital photos and 3D images of face
- 150 digital photos of face only
**105 image reviewers**

## Slide 18

## Accuracy Results

**Overall accuracy 60.4%**
- Correct photo present accuracy 72.9%
- No correct photo accuracy 47.9% (p < 0.001)
**Association with age **
- Younger image reviewers were less accurate
**No association **
- gender
- ethnicity
- Occupation

## Discussion

**Results are surprisingly poor**
- Not accustomed to the 3D facial reconstructed images
- No facial hair, scalp hair, eyebrows
- Gravity on facial features
- No distinguishing hair, eye, skin color
- No time limit
- Varying body habitus, sex, age, ethnicity
- Matching photographs are along side of the 3D images
- Same background
**Face perception - complex and not well understood**

## Future Work

- MRI Defacer (www.nbirn.net)

## In The Near Future: Practical Applications of Workspace Tools

**Open Source Imaging Viewer**

**Clinical**** ****Trials Tool Integration**

**Other Adoption Activities**