Geant based inverse planning for radiotherapy Low-hanging fruit Balazs Ujvari Hungary 30.11.2017.

Geant based inverse planning Motivation Computing for medical applications is a strategic domain with a wealth of possible projects that will be assessed by CERN’s medical applications decision-making structure. (Strategy p13) Geant is mainly used for studies in the radiotherapy. Geant based inverse planning

Why are these differences? They use sophisticated methods to calculate dose at given position BUT works well in homogeneous volumes The validation is done in homogeneous phantom with air-filled ionization chamber Geant based inverse planning

What can cause these differences? Since the surface of the treatment is not well known They add some extra to burn out the tumour Depending on the device, geometry and the neighbouring organs it’s about 0.5 – 1mm Healthy organs can be damaged Geant based inverse planning

Our work at University of Debrecen Dose deposit balls (shots) 4/8/14/18 mm Geant based inverse planning

Geant based inverse planning What we can do Start always from scratch Build the Geant4 volume from CT image (~70M voxel = G4Box) Analyse the geometry of tumour Make a list of reasonable 4/8/14/18 shots Run the Geant4 on local supercomputer (can use max 500 cores) 10k runs (sometimes 100k) few days – one week The results are the ROI (dose in 5-10M voxels) Collect all the shots and try to put inside the volume (100GB raw data) Find the best shots to fill the volume (and minimise the side effect) Naive AI methods, search trees As result there is a list with the central and the diameter of the shots, it can be converted to rotations Geant based inverse planning

Geant based inverse planning What they need A button in the menu Run MC Inverse Planning NOW Nice study Useful Something great Geant based inverse planning

Geant based inverse planning Plan to be useful Show that we can do it faster (1-4 hours) Run Geant4 for thousands of CT images to enhance the preselection Learning the similarities perhaps 1k or less runs (shots) would be enough Short runs for candidates to exclude the obvious wrong results Cloud/grid computing with the best candidates Better methods to find the ones, that can fill the tumour with minimal side effect, perhaps without move the results in the local computer Help the traditional methods to handle inhomogeneous body CT images, geometry of the tumours Run at first on supercomputer Think about cloud/grid solutions Check the recent AI solutions for the filling problem Nice study Useful Something great Geant based inverse planning

Plan to do something great (in 5 years) Discuss about the tendencies in cancel treatment What will be the top100 geometry in 5-10 years Shot sizes should be parameters 4 / 8 / 14 / 18 mm what kind of optimisation is it? 3.58 / 6.29 / 15.98 / 21.67 ?? Run thousands of CT with thousands of shot combinations Talk with the R&D at the companies (hardware and software) Central DB from the treatment to learn from the past Help to develop the next devices, find best treatment Estimate the cost to be realistic (cloud 10 x 16cores 2 hours 10CHF) Nice study Useful Something great Geant based inverse planning

Plan to do something great (in 5 years) Find the fastest method to fill the volume Prepare for the more detailed medical images (0.5mm → 0.2mm) 100M voxels → 1G voxels, or for half/full body 10-20G voxels Moore-law won’t help us, the CPU need for the calculation with detailed images could grow faster CT images, geometry of the tumours Work together with Geant V developer Dedicated cloud/grid development Company R&D, know the tendencies Much better search methods User friendly, integrated solutions Nice study Useful Something great Geant based inverse planning

Geant based inverse planning Thanks! Geant based inverse planning