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DVV: a taxonomy for mixed reality visualization in image guided surgery.

Marta Kersten-Oertel1, Pierre Jannin, D Louis Collins

  • 1McConell Brain Imaging Center at the Montreal Neurological Institute (MNI), 3801 University St, Montre´al, QC H3A 2B4, Canada. marta.kersten@mail.mcgill.ca

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Summary

A new taxonomy, Data, Visualization processing, View (DVV), helps evaluate mixed reality systems for operating rooms. This framework aids in developing practical surgical visualization tools for better clinical integration.

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Area of Science:

  • Medical Imaging
  • Computer-Aided Surgery
  • Human-Computer Interaction

Background:

  • Mixed reality (MR) visualizations are promising for image-guided surgery (IGS) but face challenges in operating room (OR) adoption.
  • Current MR IGS systems often lack end-user focus and real-world OR evaluation, hindering clinical integration.

Purpose of the Study:

  • To introduce the Data, Visualization processing, View (DVV) taxonomy as a validation framework for MR IGS systems.
  • To establish a common language for discussing and developing MR IGS components, promoting OR integration.

Main Methods:

  • Development of the DVV taxonomy defining key components of MR IGS systems.
  • Evaluation of the DVV taxonomy's fit and completeness.
  • Classification of 17 state-of-the-art MR IGS research papers using the DVV taxonomy.

Main Results:

  • The DVV taxonomy provides a structured approach to assess MR IGS systems.
  • Classification revealed that few existing MR IGS systems have validated components or undergo thorough evaluation.
  • The taxonomy aids in identifying gaps in development and validation processes.

Conclusions:

  • The DVV taxonomy is a valuable tool for validating and guiding the development of MR IGS systems.
  • Standardized validation criteria are crucial for successful translation of MR technology into the OR.
  • Further research should focus on evaluating MR IGS systems using the DVV framework to improve clinical utility.