An inverse problem is called dynamic if the object changes during the data acquisition process. This occurs e.g. in medical applications when fast moving organs like the lungs or the heart are imaged. Most regularization methods are based on the assumption that the object is static during the measuring procedure. Hence, their application in the dynamic case often leads to serious motion artefacts in the reconstruction. Therefore, an algorithm has to take into account the temporal changes of the investigated object. In this paper, a reconstruction method that compensates for the motion of the object is derived for dynamic linear inverse problems. The algorithm is validated at numerical examples from computerized tomography.
%0 Journal Article
%1 hahn2014efficient
%A Hahn, B. N.
%D 2014
%J Inverse Problems
%K from:tobiasholicki from:carolastahl
%N 3
%P 035008
%R 10.1088/0266-5611/30/3/035008
%T Efficient algorithms for linear dynamic inverse problems with known motion
%U http://dx.doi.org/10.1088/0266-5611/30/3/035008
%V 30
%X An inverse problem is called dynamic if the object changes during the data acquisition process. This occurs e.g. in medical applications when fast moving organs like the lungs or the heart are imaged. Most regularization methods are based on the assumption that the object is static during the measuring procedure. Hence, their application in the dynamic case often leads to serious motion artefacts in the reconstruction. Therefore, an algorithm has to take into account the temporal changes of the investigated object. In this paper, a reconstruction method that compensates for the motion of the object is derived for dynamic linear inverse problems. The algorithm is validated at numerical examples from computerized tomography.
@article{hahn2014efficient,
abstract = {An inverse problem is called dynamic if the object changes during the data acquisition process. This occurs e.g. in medical applications when fast moving organs like the lungs or the heart are imaged. Most regularization methods are based on the assumption that the object is static during the measuring procedure. Hence, their application in the dynamic case often leads to serious motion artefacts in the reconstruction. Therefore, an algorithm has to take into account the temporal changes of the investigated object. In this paper, a reconstruction method that compensates for the motion of the object is derived for dynamic linear inverse problems. The algorithm is validated at numerical examples from computerized tomography.},
added-at = {2021-07-22T09:51:38.000+0200},
author = {Hahn, B. N.},
biburl = {https://puma.ub.uni-stuttgart.de/bibtex/2de22b9e9b54c871468b6c418626766a5/mathematik},
doi = {10.1088/0266-5611/30/3/035008},
interhash = {c5b9630c2e2ea8a8feb85f876c159a81},
intrahash = {de22b9e9b54c871468b6c418626766a5},
journal = {Inverse Problems},
keywords = {from:tobiasholicki from:carolastahl},
number = 3,
pages = 035008,
timestamp = {2021-07-22T07:51:38.000+0200},
title = {Efficient algorithms for linear dynamic inverse problems with known motion},
url = {http://dx.doi.org/10.1088/0266-5611/30/3/035008},
volume = 30,
year = 2014
}
