Jun 3 – 5, 2022
Parador El Saler, Valencia, Spain
Europe/Zurich timezone

Fast prototyping of medical imaging detectors using AI methods

Jun 5, 2022, 4:45 PM
Parador El Saler, Valencia, Spain

Parador El Saler, Valencia, Spain


Indranil Pan


Computationally expensive physics simulations involving Monte Carlo runs form the backbone of designing improved medical imaging detectors. We show initial results, from an EU-sponsored grant, leveraging uncertainty quantification (UQ) techniques to drastically reduce simulation time with negligible loss in fidelity. We outline the use of embedding such UQ techniques within a machine learning surrogate-based optimiser to achieve optimised detector configurations quickly.
As part of our software democratisation efforts, we showcase our Quaisr platform which can be used by non-specialist users to deploy and run such AI-driven simulation workflows in the cloud. We discuss other features unique to Quaisr which help engineers, data scientists, operators, and experimentalists to integrate simulations, machine learning, and real-world data; these features accelerate decision-making under uncertainty, rapid prototyping, collaboration, and standardisation.

Primary authors

Dr Samuel Palmer (Quaisr) Dr Assen Batchvarov (Quaisr) Dr Roman Klapaukh (Quaisr) Dr Lachlan Mason (Quaisr) Prof. Richard Craster (Imperial College London, Quaisr) Prof. Omar Matar (Imperial College London, Quaisr) Indranil Pan

Presentation materials