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Integration of Medical Imaging Simulators in Slicer
Key Investigators
- Abián Hernández-Guedes (ULPGC - GTMA - MACbioIDi)
- Jose Carlos Ruiz-Luque (ULPGC - GTMA - MACbioIDi)
- Guillermo Valentín Socorro-Marrero (ULPGC - GTMA - MACbioIDi)
- Juan Ruiz-Alzola (ULPGC - GTMA - MACbioIDi)
- Andras Lasso (PerkLab, Queen’s University)
- Tamas Ungi (Perk Lab)
Project Description
The main objective of this project is the development of an ultrasound (US) simulation prototype based on CT images.
Since the project is aimed to obtain simulated objective data that allow testing algorithms or that could be used for educational purpose. Besides these data, simulated US images, come from a CT dataset with better quality that will facilitate the diagnosis. Eventually, this project will be integrated in an extension for Medical Imaging Simulators, this extension is intended to contain multiple modules to simulate images for medical purpose.
Objective
- Establish a plan for the design of a prototype.
- Integrate the prototype developed as a module on Slicer.
- Develop the models for acoustic features deduced from the inspection of CT images.
Approach and Plan
- Define the set of CT images according to the criteria previously determinated.
- Model the acoustic features.
- Explore the way to extract volume information from 3DSlicer data structure.
- Implement the simulator as a Slicer module.
Progress and Next Steps
- We have followed the approach plan in order to reached some objectives as the plan and prototype
- We have gather valuable information to develop some of the modules and we have a simple loadable module that can extract the information that we need.
Background and References
- Reichl, T., Passenger, J., Acosta, O., & Salvado, O. (2009, March). Ultrasound goes GPU: real-time simulation using CUDA. In SPIE Medical Imaging (Vol. 2009, p. 726116). International Society for Optics and Photonics.
- Wein, W., Brunke, S., Khamene, A., Callstrom, M. R., & Navab, N. (2008). Automatic CT-ultrasound registration for diagnostic imaging and image-guided intervention. Medical image analysis, 12(5), 577-585.