Multimodal Imaging Capacity for Prediction of Evolution of Knee Osteoarthritis
Osteoarthritis is a major cause of pain, disability and loss of independence among the elderly. With life expectancy progressing, growing old while maintaining an active lifestyle without pain has become one of the important challenges of aging. Knee osteoarthritis is a major public health issue which is responsible of pain and of difficulty in performing daily living activities.
At present, the definition of knee osteoarthritis is based on a combination of symptoms and joint radiographic criteria. Although this definition is useful for epidemiological studies, it does not address the problems of prediction and prognosis of knee osteoarthritis. Better predictors of progression of structural damage via osteoarthritis imaging could therefore help to identify patients whose disease has the potential to move towards degradation and joint space narrowing. Predictive factors of osteoarthritis progression are not yet well known. However, in this unfavorable evolution, a growing role gives importance to the subchondral bone.
The aim of the present thesis is to determine predictive factors of progression of osteoarthritis at the knee by a multimodal characterization of the subchondral bone using MRI (Magnetic Resonance Imaging), direct high resolution digitization radiographs and bone texture analysis. At the end of the project, an innovative imaging device, combining a semi-automatic software for texture analysis, control detection and image registration would be supplied. This will enable on the one hand a more accurate and reproducible way to measure the joint space width of the affected compartment and on the other hand, an assistance to better detect patients at risk of progression of their osteoarthritis at the knee.
This thesis aims to: First, using textural analysis of subchondral X-ray images, provide textural descriptors linked to osteoarthritis. Second, develop a tool for the measurement of the joint space narrowing for the detection of potential patients with knee osteoarthritis. Finally, establish and validate links between 2D texture features and 3D physical parameters of osteoarthritis.
The candidate will work closely with clinicians, image processors, industrials, etc. The candidate should have skills in image processing, applied mathematics, and programming.
The thesis position is to be filled in autumn 2017, for 3 years at the I3MTO laboratory (Imagerie Multimodale Multiéchelle et Modélisation du Tissu Osseux - EA 4708) of the University of Orleans (France).
Image processing, Computer programming, texture analysis, 2D/3D analysis, segmentation, applied mathematics, digital radiography.
Send a detailed CV and a cover letter to:
Rachid JENNANE (email : Rachid.Jennane@univ-orleans.fr, tel : +33 2 38 41 99 43)
(c) GdR 720 ISIS - CNRS - 2011-2015.