Noise Based Imaging for Osteoarticular diseases – Noise

Having a high contrast between the different tissues of a joint has always been the holy grail of musculoskeletal imaging. Using such a modality would promote a better understanding of the osteoarticular diseases and a follow-up of the different therapeutic strategies. In a clinical environment, this modality could revolutionize the diagnosis based on the first signs of illness. Unfortunately, for an accurate diagnosis, this is not yet the case and patients are often imaged using several techniques (CT, MRI, Ultrasound). In this project, I propose to combine signal processing methods, a paradoxical idea, and simple X-Ray tomographic systems to generate a complete picture of joints.

There is an on-going effort to reduce the level of noise as low as possible thanks to different strategies (filters, detectors, sources of light ...). I propose here to use a counter-intuitive idea: add a static noise during the acquisition, to retrieve information that would have been lost otherwise. We will employ regular X-ray experimental configurations and simply add a spatially random modulator that will create a noisy image. Using such a simple experimental set-up combined with advanced signal processing methods it is possible to retrieve new information about samples such as their refraction, scattering, or mechanical properties. The goal of the project is to perform Noise Based Imaging using an ordinary X-ray source rather than a synchrotron.

We will work on osteoarticular diseases which are the most prevalent chronic pains and long-term disabilities with hundreds of millions of people affected worldwide. Studies will be carried out at several spatial resolutions to provide a multi-scale understanding of the cartilage degradation mechanisms and its regeneration processes as well as an earlier and more accurate diagnosis.