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Abstract
Objective: Improvement of the planning stage of image-guided surgery requires a better anticipation of the surgical procedure and its anatomical and functional environment. This anticipation should be provided by acquisition of multimodal medical images of the patient and by a better understanding of surgical procedures. In this paper, we propose improvements to the planning and performance of multimodal image-guided neurosurgery through the use of information models related to neurosurgical procedures.
Materials and Methods: A new generic model of surgical procedures is introduced in the context of multimodal image-guided craniotomies. The basic principle of the model is to break down the surgical procedure into a sequence of steps denning the surgical script. In the model, a step is defined by an action. The model assigns to each surgical step a list of image entities extracted from multimodal preoperative images (i.e., anatomical and/or functional images) which are relevant to the performance of that particular step. A semantic validation of the model was performed by instantiating the model entities for 29 surgical procedures.
Results: The resulting generic model is described by a UML class diagram and a textual description. The validation showed the relevance of the model, confirming the main underlying assumptions. It also provided some leads to improve the model.
Conclusion: While further validation is needed, the initial benefits of this approach can already be outlined. It should add real value to the different levels of image-guided surgery, from preprocessing to planning, as well as during surgery. Models of surgical procedures can manage image data according to the surgical script, which should lead to better anticipation of surgery through the development of simulation tools. Furthermore, the models may improve the performance of surgery using microscope-based neuronavigation systems by making it possible to adapt both visualization and interaction features of multimodal preoperative images according to the model.
