Image Analysis and Quantitation

Key personnel: Ted Graves, Geoff Nelson, Sanjeev Datta

In order to accomplish our goals we have invested great effort in engineering software for image visualization and analysis. The application we have designed, RT_Image, is a full-featured application for the visualization and quantitation of a variety of medical images. RT_Image is written in the Interactive Data Language (IDL) and is open source, allowing developers to easily add functionality and freely distribute the software. To date, RT_Image has been applied towards segmenting clinical PET images in order to extract prognostic information and quantitating multimodality small animal imaging data, including CT, MRI, PET, and optical images. Currently this code framework is being used to develop the treatment planning interface for the small animal radiotherapy system described above. This program is designed to streamline data analysis and expedite development of new quantitative methods.

Current research projects in this area include:

• Development of novel PET segmentation methods for radiotherapy target volume definition
• Development of interfaces for volume image visualization, registration, and 3D ROI analysis
• Implementation of small animal radiotherapy treatment planning and dose calculation tools within RT_Image
  

Recent publications:

• Graves EE, Quon A, Loo BW. RT_Image: An Open Source Tool for Investigating PET in Radiation Oncology. Technology in Cancer Research and Therapy 2007, 6:111-121.
• Lee P, Weerasuriya D, Le Q, Lavori PW, Quon A, Hara W, Wakelee H, Graves E, Loo BW. Metabolic Tumor Burden Predicts for Disease Progression in Lung Cancer. International Journal of Radiation Oncology Biology Physics 2007, 69:328-333.
• La TH, Filion EJ, Turnbull BB, Chu JN, Lee P, Nguyen K, Maxim P, Loo BW, Quon A, Graves EE, Le QT. Metabolic Tumor Volume Predicts for Recurrence and Death in Head and Neck Cancer. International Journal of Radiation Oncology Biology Physics 2009; 74:1335-1341.