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Pretargeted Radioimmunotherapy Based on the Inverse Electron Demand Diels-Alder Reaction

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SU-E-J-189: The Kullback-Leiber Divergence for Quantifying Changes in Radiotherapy Treatment Response.

E Schreibmann¹, I Crocker¹, H Shu¹

¹Emory University School of Medicine, Atlanta, GA.

Medical Physics

|May 19, 2017

Summary

This summary is machine-generated.

Kullback-Leiber divergence offers a robust method for automated change quantification in repeated radiotherapy imaging. This technique reliably detects tumor response, improving personalized treatment assessment.

Keywords:

Cancer Image sensors Medical image contrast Medical imaging Radiation therapy Remote sensing Statistical analysis Statistical distribution

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Area of Science:

Medical Imaging
Radiotherapy
Computational Analysis

Background:

Repeated imaging is crucial in radiotherapy for tumor detection and treatment assessment.
Change detection algorithms from remote sensing can quantify modifications in time-series data.
Automated quantification of clinical changes in repeated radiotherapy imaging is needed.

Purpose of the Study:

To propose and evaluate change detectors for automated quantification of clinical changes in repeated radiotherapy imaging.
To explore the Kullback-Leiber divergence as an indicator of tumor change and treatment response.

Main Methods:

Utilized Kullback-Leiber divergence, based on likelihood theory, to measure statistical distribution differences.
The method accommodates noise and variations in imaging acquisition parameters.
Compared Kullback-Leiber divergence with simple difference maps for change detection.

Main Results:

Kullback-Leiber divergence effectively discerned noise by considering regional voxel statistics.
Unlike difference maps, it consistently detected low-intensity and high-contrast changes.
The proposed operator marked both low and high contrast changes accurately.

Conclusions:

Statistical comparison using Kullback-Leiber divergence reliably quantifies changes in repeated radiotherapy imaging.
This method enhances automated assessment of tumor response and personalized treatment.