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Detail publikace
DAŇKOVÁ, M. RAJMIC, P.
Originální název
Low-rank model for dynamic MRI: joint solving and debiasing
Typ
článek ve sborníku ve WoS nebo Scopus
Jazyk
angličtina
Originální abstrakt
Reconstruction procedures from compressed-sensed MRI data are often treated as optimization problems. The most popular approach is to solve convex problems including the l1-norm. It is known that this type of regularization seeks for sparse solutions, however it gives biased estimates. Debiasing is a postprocessing procedure commonly used in many applications, especially where the optimization criterion is penalized least squares. In LASSO-type problems, debiasing is performed such that an additional least squares estimate is run while the non-sparse support is fixed. In low-rank modelling, l1-norm is applied on the singular values of a matrix. The debiasing procedure is more complicated, and especially, it turns out that it can amplify noise in the estimates. This abstract shows a method which debiases the estimates within a single procedure.
Klíčová slova
MRI, debiasing, compressed sensing
Autoři
DAŇKOVÁ, M.; RAJMIC, P.
Vydáno
29. 9. 2016
Nakladatel
Springer
Místo
Berlin
ISSN
1352-8661
Periodikum
MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE
Ročník
29
Číslo
Supplement 1
Stát
Spojené státy americké
Strany od
200
Strany do
201
Strany počet
2
URL
http://link.springer.com/article/10.1007/s10334-016-0569-9
BibTex
@inproceedings{BUT128720, author="Marie {Mangová} and Pavel {Rajmic}", title="Low-rank model for dynamic MRI: joint solving and debiasing", booktitle="ESMRMB 2016, 33rd Annual Scientific Meeting, Vienna, AT, September 29--October 1: Abstracts, Friday", year="2016", journal="MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE", volume="29", number="Supplement 1", pages="200--201", publisher="Springer", address="Berlin", doi="10.1007/s10334-016-0569-9", issn="1352-8661", url="http://link.springer.com/article/10.1007/s10334-016-0569-9" }