Emerging technologies in the field of X-ray sources and detectors open up possibilities for envisioning new breakthrough systems in 3D imaging. In conventional tomography, a large-area detector captures images of an object exposed to X-rays from a point source. The latest generations of medical scanners also incorporate spectrometric semiconductor detectors, providing a real improvement in image quality.
The proposed thesis aims to shift the paradigm by designing a system that combines numerous distributed X-ray sources with a small-sized spectrometric detector. This inverse geometry is innovative in terms of system architectures, allowing relaxation of constraints on sensor dimensions and reduction of certain artifacts.
The thesis work will revolve around the design and simulation of new systems in inverse geometry, along with the development of associated reconstruction algorithms. These algorithms, based on proximal methods and capable of integrating neural networks, must leverage the rich information provided by the spectrometric detector under conditions of sparse acquisition. The student will utilize simulation and reconstruction tools developed within the laboratory and will also have access to experimental resources for validating the developments. Working within a multidisciplinary laboratory with extensive experience in spectrometric detector and X-ray system design, the student will engage in exchanges with external teams, including radiologists, to incorporate a final need into the research.