Editing Inverse problem (section)

===== Computed tomography =====
In [[X-ray computed tomography]] the lines on which the parameter is integrated are straight lines: the [[tomographic reconstruction]] of the parameter distribution is based on the inversion of the [[Radon transform]]. Although from a theoretical point of view many linear inverse problems are well understood, problems involving the Radon transform and its generalisations still present many theoretical challenges with questions of sufficiency of data still unresolved. Such problems include incomplete data for the x-ray transform in three dimensions and problems involving the generalisation of the x-ray transform to tensor fields. Solutions explored include [[Algebraic Reconstruction Technique]], [[filtered backprojection]], and as computing power has increased, [[iterative reconstruction]] methods such as [[SAMV (algorithm)|iterative Sparse Asymptotic Minimum Variance]].<ref name=AbeidaZhang>{{cite journal | last1=Abeida | first1=Habti | last2=Zhang | first2=Qilin | last3=Li | first3=Jian | last4=Merabtine | first4=Nadjim | title=Iterative Sparse Asymptotic Minimum Variance Based Approaches for Array Processing | journal=IEEE Transactions on Signal Processing | volume=61 | issue=4 | year=2013 | issn=1053-587X | doi=10.1109/tsp.2012.2231676 | pages=933–944 | url=https://qilin-zhang.github.io/_pages/pdfs/SAMVpaper.pdf | arxiv=1802.03070 | bibcode=2013ITSP...61..933A | s2cid=16276001 }}</ref>