title: Visualization of Deformable Image Registration Quality Using Local Image Dissimilarity.
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 "accepted" 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.
authors.forename: Matthias
authors.initials: M
authors.lastname: Schlachter
authors.forename: Tobias
authors.initials: T
authors.lastname: Fechter
authors.forename: Miro
authors.initials: M
authors.lastname: Jurisic
authors.forename: Tanja
authors.initials: T
authors.lastname: Schimek-Jasch
authors.forename: Oliver
authors.initials: O
authors.lastname: Oehlke
authors.forename: Sonja
authors.initials: S
authors.lastname: Adebahr
authors.forename: Wolfgang
authors.initials: W
authors.lastname: Birkfellner
authors.forename: Ursula
authors.initials: U
authors.lastname: Nestle
authors.forename: Katja
authors.initials: K
authors.lastname: Buhler
mesh_terms: D000465:Algorithms; D006801:Humans; D007091:Image Processing, Computer-Assisted / Q000379:methods*; D008168:Lung / Q000000981:diagnostic imaging; D008175:Lung Neoplasms / Q000000981:diagnostic imaging; D013902:Radiography, Thoracic; D061089:Radiotherapy, Image-Guided; D015203:Reproducibility of Results; D014057:Tomography, X-Ray Computed