Low-rank model for dynamic MRI: joint solving and debiasing

Kód výsledku v IS VaVaI

<a href="https://www.isvavai.cz/riv?ss=detail&h=RIV%2F00216305%3A26220%2F16%3APU120521" target="_blank" >RIV/00216305:26220/16:PU120521 - isvavai.cz</a>

Výsledek na webu

<a href="http://link.springer.com/article/10.1007/s10334-016-0569-9" target="_blank" >http://link.springer.com/article/10.1007/s10334-016-0569-9</a>

DOI - Digital Object Identifier

<a href="http://dx.doi.org/10.1007/s10334-016-0569-9" target="_blank" >10.1007/s10334-016-0569-9</a>

Jazyk výsledku

angličtina

Název v původním jazyce

Low-rank model for dynamic MRI: joint solving and debiasing

Popis výsledku v původním jazyce

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.

Název v anglickém jazyce

Low-rank model for dynamic MRI: joint solving and debiasing

Popis výsledku anglicky

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.

Druh

D - Stať ve sborníku

CEP obor

JA - Elektronika a optoelektronika, elektrotechnika

OECD FORD obor

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Projekt

<a href="/cs/project/GA16-13830S" target="_blank" >GA16-13830S: Perfuzní zobrazování v magnetické rezonanci pomocí komprimovaného snímání</a><br>

Návaznosti

P - Projekt vyzkumu a vyvoje financovany z verejnych zdroju (s odkazem do CEP)

Rok uplatnění

2016

Kód důvěrnosti údajů

S - Úplné a pravdivé údaje o projektu nepodléhají ochraně podle zvláštních právních předpisů

Název statě ve sborníku

ESMRMB 2016, 33rd Annual Scientific Meeting, Vienna, AT, September 29--October 1: Abstracts, Friday

ISBN

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ISSN

1352-8661

e-ISSN

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Počet stran výsledku

2

Strana od-do

200-201

Název nakladatele

Springer

Místo vydání

Berlin

Místo konání akce

Vídeň

Datum konání akce

29. 9. 2016

Typ akce podle státní příslušnosti

WRD - Celosvětová akce

Kód UT WoS článku

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