Tomography is based on recording two-dimensional X-ray images of a patient along different directions of view. A mathematical reconstruction algorithm is applied to the set of images, resulting in a three-dimensional internal model of the patient. In traditional CT scans one collects hundreds of images and produces a high-resolution 3D model. However, such a scan delivers a hefty dose of radiation to the patient. The amount of radiation can be reduced by taking fewer images, but the n the mathematical reconstruction problem becomes much more difficult. Modern inversion algorithms can overcome these difficulties and provide doctors with 3D models good enough for the clinical task at hand. Examples are presented related to dental imaging and to bone quality assessment. The sparse-data tomography methods also apply to a wide variety of problems outside medicine, for example monitoring the ozone layer by satellite measurements of star occultation.
Keynote Samuli Siltanen
Samuli Siltanen works as Professor of Industrial Mathematics at University of Helsinki. He is an expert in computational inverse problems of medical imaging. The focus areas of his research are electrical impedance tomography and three-dimensional X-ray imaging with reduced radiation dose. Professor Siltanen has 6 years work experience in medical imaging industry (GE Healthcare), bringing a sense of the end-users’ needs to his academic work as well.