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Home > SCI Research: Inverse Problems
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Inverse Problems
- Inverse EEG Applications
- Inverse MEG Applications
- Focusing Inversion
- Finite Elements in the Forward EEG Problem
- Forward Simulations
A common aspect of most imaging modalities is the need to perform reconstruction based on remote measurements from a number of sensors - such reconstructions require the solution to an "inverse problem". The goal of the reconstruction can be structural information, such as the anatomy that comes from classic medical imaging with, for example, magnetic resonance or X-rays. The aim of the inverse solution can also be functional information such as electrical activity or conductivity.
Research within the SCI Institute has included numerous types of inverse problems in imaging, with emphasis on problems in functional imaging. Examples include reconstruction of electrical sources within the heart or brain and extraction of molecular diffusion information from magnetic resonance images. Computationally, such problems frequently involve complex numerical algorithms and large systems of equations. Inverse problems are also typically ill-posed in the sense that small changes in the input data can lead to unbounded fluctuations in the solutions. Often, the major challenge of an inverse problem lies in incorporating a priori information into the solution in efficient and realistic ways. The SCI Institute has developed a number of novel means of including such information to solve a wide variety of inverse problems.
