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CREATIS opens several Master internships of 5-6 months to address new questions in the emerging field of X-ray spectral imaging.
Background X-ray “color” or “spectral” computed tomography (CT) is a new imaging modality that is raising increasing interest in radiology. Thanks to the emergence of new detectors that can discriminate X-ray photons depending on their energy [1], it is possible to reconstruct the constituents of the human body such as bone, water, fat or concentration in contrast agents [2]. Although recent works have shown the feasibility of spectral CT systems, there are still many open questions such as the best way to decompose the projections of the object into a material basis [3].
Keywords X-Ray Imaging, material decomposition, inverse problem, regularization.
Work Plan The main goal of these internships is to implement a decomposition method able to decompose the energy-dependent images into material-dependent images, which is a non linear inverse problem. Solving this problem requires to take into account the light propagation within the sample as well as the energy reponse of the detector, i.e. modelling the forward problem. The inverse problem being ill-posed, a regularization scheme is necessary to recover a stable decomposition in the presence of noisy measurments.
Depending on the background’s candidate, he will focus on whether theoretical aspects of the inverse problem or the application of state-of-the-art methods to real data. Three different work plans can be considered (see pdf files below).
Start/Duration Starting anytime between now and April for about 6 months
Salary 550€ net monthly
Skills The student must have a strong background in medical imaging, image processing, and inverse problems. Knowledge in radiation physics would be appreciated but is not required. Programming skills: Matlab, C, C++.
How to apply?
Send as soon as possible your CV and academic records to Nicolas Ducros (nicolas.ducros@creatis.insa-lyon.fr) and Simon Rit (simon.rit@creatis.insa-lyon.fr)
Reference
[1] K. Taguchi et al., “Vision 20/20: Single photon counting x-ray detectors in medical imaging,” Medical Physics, 40, 100901, 2013.
[2] H. Gao et al., “Multi-energy CT based on a prior rank, intensity and sparsity model (PRISM)”, Inverse Problems, 27, 115012, 2011.
[3] Y. Long et al., “Multi-Material Decomposition Using Statistical Image Reconstruction for Spectral CT”, IEEE TMI, 33(8), 1614, 2014.