In principle the whole medical topic of radiotherapy analysis is full of visualisation. A recent sandpit discussed Monte Carlo techniques that are critical within the radiotherapy planning / beamline construction / or just radiation safety / etc…
So where is the visualisation interests as the main need is to make these processes faster and more useful. We did pitch three small projects to look at:
- Solving the 1:1:1 problem may involve visualising the results immediately after compute and not storing or transmitting this.
The 1:1:1 problem termed by radiotherapists is to model 1mm cubed, solved in 1 second with 1% accuracy for practical use.
There will be a one-day workshop where the required (back of the envelope) 250x speed up will be discussed with UK researchers and industrial participants; from the software / hardware / and remote access skills.
- Sharing data discussed ways to share both code and data: an example of data / code share that worked is on zenodo (yes have used github as well).
– Data set at: https://zenodo.org/record/16474#.WMlng_ni7Lk
– Code for reconstruction at: https://zenodo.org/record/16539#.WMlnlvni7Lk
– Even has a published tutorial: http://eprints.ma.man.ac.uk/2290/01/covered/MIMS_ep2015_26.pdfThese have official DOI tags and therefore are version controlled for both code and data so then can be used in papers by others – and have been,
- A final project is looking at fractal analysis from CT to MRI analysis and considering the problem from 3D to 2D to 1D. This will enable new planning processed to occur.
More details to come. This was organised by the Advanced Radiotherapy network https://www.advanced-radiotherapy.ac.uk/ and thank them for allowing these ideas to be pitched.
There were global other issues and “Focus Where it Matters”, was a industry quote and will reuse here; so they need not just to increase speed but also to control inaccuracy and inconsistency.