Sustainable Tomographic Imaging with Learning and rEgularization

Modern medical practice crucially relies on biomedical imaging, of which Computerized Tomography (CT) is a central pillar. Traditional CT uses a series of X-ray projections, post-processed by computer algorithms, to produce cross sectional illustrations of the inside of the body. While the results are very effective, the main drawback of X-ray CT is the harmful effect of radiations for the human body. The present project focuses on a main path of innovation in CT: reduction of patient exposure to radiation. Specifically, we will investigate mathematical and computational themes arising from the emerging technologies of:

  • low-dose Computed Tomography (LD-CT), based on a reduced radiation dose per projection and/or a reduced number of projections
  • zero-dose CT, based on non-ionizing investigating signals such as light in Diffuse Optical Tomography (DOT). In both cases, the reduced ionizing exposure comes at the price of a much noisier/subsampled signal, so that the associated reconstruction problems are severely ill-posed and require advanced mathematical and numerical techniques for adequate treatment.