ESTRO 2022 - Abstract Book

S1412

Abstract book

ESTRO 2022

Other datasets were then registered to the cCT, in order to project useful multimodal information on this mesh. EAM data was exported from the CARTO system as a mesh and a list of measurements. The EAM mesh was automatically registered to the cCT mesh using ICP registration algorithm. PET information, if available, was also integrated into the workflow through registration of the associated CT image to the cCT, and reporting of the PET values at the coordinates of each cCT mesh point. These processes allowed the representation of the information on one unique mesh. A tool was integrated to delineate the target by geodesic path picking on its surface. The resulting target was finally propagated to the whole width of the myocardium in order to generate the clinical target volume which can be directly exploited by treatment planning systems. Results Examples of one patient data representations are illustrated in figure 1. The path picking process is presented in figure 2.

Our workflow was tested on the data of 4 patients. Cardiologists using this workflow reported being more confident about their target definition than when using standard treatment planning tools. The three-dimensional representation especially was considered helpful to identify the optimal target zone. All operations were applied automatically with potential manual corrections. It represented a gain of time, compared to a slice-by-slice delineation on cCT, that was evaluated up to 50%. Conclusion The developed workflow enables to fuse multimodal information to improve the robustness of target definition in CR of VT. Preliminary experiments show very positive feedback from clinicians and a decrease of delineation time. Further studies will be needed to confirm these results on a larger cohort of patients.

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