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Talk by Dr. Nikita Morikiakov on inverse problems in medical imaging with deep learning.
Inverse problem is the type of problems in natural sciences when one has to infer from a set of observations the causal factors that produced them. In medical imaging, important examples of inverse problems would be recontruction in CT and MRI, where the volumetric representation of an object is computed from the projection and Fourier space data respectively. In a classical approach, one relies on domain specific knowledge contained in physical-analytical models to develop a reconstruction algorithm, which is often given by a certain iterative refinement procedure. Recent research in inverse problems seeks to develop a mathematically coherent foundation for combining data driven models, based on deep learning, with the analytical knowledge contained in the classical reconstruction procedures. In this talk we will give a brief overview of these developments and then focus on particular applications in Digital Breast Tomosynthesis and MRI reconstruction.