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

Repeated imaging is an extremely powerful tool in current radiotherapy practice since it allows advanced tumor detection and personalized treatment assessment by quantify tumor response. Change detection algorithms have been developed for remote sensing images to mathematically quantify relevant modifications occurring between datasets of the same subject acquired at different times. We propose usage of change detectors in radiotherapy for an automated quantification of clinical changes occurring in repeated imaging.

We explore usage of the Kullback-Leiber divergence as indicator of tumor change and quantification of treatment response. The Kullbach-Leiber divergence uses the likelihood theory to measures the distance between two statistical distributions and thus does not assume consistency in imaging. By it's general nature, it can accommodate the presence of noise and variations in imaging acquisition parameters that usually hinder automated identification of clinically-relevant features.

In a comparison of simple difference maps and the Kullbach-Leiber divergence operator, the difference maps were affected by noise and did not consistently detect changes of low intensity. In contrast, the proposed operator discerned noise by considering regional statistics around each voxel, and marked both regions with low and high contrast changes.

Statistical comparison through Kullback-Leiber divergence provides a reliable means to automatically quantify changes in repeated radiotherapy imaging.