@article{PB-VRVis-2016-007,
author = {Schlachter, Matthias and Fechter, Tobias and Jurisic, Miro and Schimek-Jasch, Tanja and Oehlke, Oliver and Adebahr, Sonja and Birkfellner, Wolfgang and Nestle, Ursula and B{\"u}hler, Katja},
title = {Visualization of Deformable Image Registration Quality using Local Image Dissimilarity},
year = {2016},
journaltitle = {IEEE Transactions on Medical Imaging},
doi = {10.1109/TMI.2016.2560942},
url = {https://www.vrvis.at/publications/PB-VRVis-2016-007},
issn = {0278-0062},
pages = {1-1},
volume = {PP},
abstract = {Deformable image registration (DIR) has the potential to improve modern  radiotherapy in many aspects, including volume definition, treatment  planning and image-guided adaptive radiotherapy. Studies have shown its  possible clinical benefits. However, measuring DIR accuracy is difficult  without known ground truth, but necessary before integration in the  radiotherapy workflow. Visual assessment is an important step towards  clinical acceptance. We propose a visualization framework which supports  the exploration and the assessment of DIR accuracy. It offers different  interaction and visualization features for exploration of candidate  regions to simplify the process of visual assessment. The visualization  is based on voxel-wise comparison of local image patches for which  dissimilarity measures are computed and visualized to indicate locally  the registration results. We performed an evaluation with three radiation  oncologists to demonstrate the viability of our approach. In the evaluation,  lung regions were rated by the participants with regards to their visual  accuracy and compared to the registration error measured with expert defined  landmarks. Regions rated as {\v{a}}accepted{\v{a}} had an average registration error of  1.8 mm, with the highest single landmark error being 3.3 mm. Additionally,  survey results show that the proposed visualizations support a fast and  intuitive investigation of DIR accuracy, and are suitable for finding even  small errors.},
keywords = {Image color analysis;Image registration;Lungs;Planning;Rendering (computer
  graphics);Tumors;Visualization;image registration;radiotherapy;similarity
  measure;visual assessment;volume visualization},
number = {99},
month = {apr},
}