This scenario is built under real-time tracking conditions.
Particle detectors are used in lab conditions or in a pre-known geometrical condition to be able to resolv geometrical information from the environment.
Under certain conditions have a knowledge the device, the radiation source, the detector and the relative position of the patient, or device under test, can improve the extraction of the sensor information.
This leads to work under non laboratory or well know conditions, this research line tries to:
- Provide reconstruction algorithms to capture 3D coordinates of all the required devices.
- Provide benefits in the reconstruction by moving the capture devices, and include real time 3D infrmation to the reconstruction.
- Improbe by using Machine Learning the reconstruction. This can be done training models that latter can provide an heuristic robust pattern for reconstruction
- Include this information to provide enhanced or extra information to the detector (density, compton corrections)ls
Works and tools
This is a large colaboration started from 2012, and started from a patent (currently licensed), to start to develop enhanced method, the main work with the colaboration of the University Politécnica de Valencia, latter colaborators are Instituto de Biomecánica de Valencia, and Hospital Politécnico La Fe.
From the begining the main tasks where to provide robust methods to permorm image registration, image registration in placing two diferent imaging devices in the same coordinates. The main idea is perform latter calculations with this information.
Information can arise from, depth cameras, which requiries procesing in large GPU devices a large number of points, visible cameras, which requieres to place the system in diferent locations to provide precise 3D. Last but not least, Machine Learnign can be applied to solve models for specific diseases.