Temporal Dynamic Functional Analysis
- 技術應用
- Standalone software or modulewithin existing image analysis software systemToolto facilitate industry collaboration to support larger radiomics infrastructureand comprehensive database combined with medical and genomic data
- 詳細技術說明
- Perfusion and diffusion analysis softwarefor CT and MRI
- *Abstract
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TemporalDynamic (Functional) Analysis (TDA) is the new standard in advanced functionalimaging analysis for the application of radiotherapy in cancer treatment.
Theuse of dynamic contrast-enhanced (DCE) imaging in combination withpharmacokinetic modeling of contrast agents into the blood flow is rapidlybecoming an important tool in assessing tissue response to targetedinterventions, as well as efficacy of drug delivery mechanisms. Differentparameter models for contrast material exchange have been developed to describeperfusion, vascular permeability and density. As such, a fast, big dataanalytics platform—TDA—has been developed to combine fundamental tracerkinetics modeling and their resulting microcirculatory properties toeffectively advance functional imaging for a variety of applications.
TDAautomates the segmentation and voxel-based analysis that is essential for everypatient, since manual contouring of the region of interest (e.g. tumor) istime-consuming, but also prone to inter- and intra-observer errors.Furthermore, difficulties exist in image registration of serial functional datain order to derive treatment response metrics because a tumor’s morphology islikely to change (eg, shrink or change non-isotropically) and move over time. Conventionalauto-segmentation methods typically use signal thresholding, which has provendifficult for segmenting vessels and perfused tissue.
TDAuses the temporal density of 4D DCE data, which allows for a conceptuallydifferent approach to analyzing perfused tissue voxels. Instead of evaluatingeach volume in the cine acquisition individually, it exploits the dynamiccontrast behavior of voxels to characterize areas and levels of blood flow andperfusion to automatically classify structures or regions of interest based ontheir functionality. This, in turn,helps to improve the accuracy and robustness of the calculated kineticperfusion and tissue permeability parameters.
TDAleverages a rapid GPU-based framework for automatic analysis of DCE CT and DCEMRI data with the most widely tested accuracy and robustness possible usingcontrollable flow phantom data as well as clinical data in the brain and liver.
- *Principal Investigation
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Dr. Catherine Coolens, University HealthNetwork
- *Publications
- Coolens C et al Automated Voxel-BasedAnalysis of Volumetric Dynamic Contrast-Enhanced CT Data Improves Measurementof Serial Changes in Tumor Vascular Biomarkers. Int J Radiat Oncol Biol Phys.2015 Jan 1;91(1):48-57
- 國家/地區
- 美國
