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Jan De Beenhouwer

  • Research interest:In X-ray computed tomography (CT), X-ray images of a sample are taken from hundreds to thousands of angles and subsequently used to form a 3D reconstruction of the sample, including many internal features. Conventional analytical methods for CT reconstruction result however in artifacts for non-ideal, constrained, CT acquisitions such as when only a limited angular range is available, or when only few projection images can be acquired due to time constraints and when the image formation model is simply inadequate. I focus on the development of novel reconstruction techniques that take advantage of prior knowledge (e.g. sample shape, materials, spectra) in both X-ray absorption and quantitative phase contrast tomography to solve these issues and I interconnect the developed algorithms with equally flexible image acquisition hardware, thereby taking advantage of prior knowledge of the object to be scanned and as well as of the imaging hardware itself.
  • Disciplines:Modelling and simulation, Data visualisation and imaging , Nuclear imaging
  • Research techniques:Model-based tomography, mainly in the fields of X-ray Computed Tomography (CT):• Reconstruction for non-conventional geometries• Dynamic computed tomography• Parametric reconstruction• Phase contrast imagingSingle Photon Emission Computed Tomography (SPECT) imaging:• Monte Carlo simulation methodology: Low energy photon transport in media, variance reduction techniques, parallelization, GPU computing.• Image formation and reconstruction: Collimator and detector response modelling, quantitative Monte Carlo-based reconstruction, system matrix generation, correction methods for image degradation.Application fields:• Medical and biomedical imaging• Industrial quality control and inspection• Metrology
  • Users of research expertise:Potential users are both academic groups as well as industry, with applications in material science, quality control and inspection, 3D printing and medical imaging.