Publication result detail

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

DAŇKOVÁ, M.; RAJMIC, P.

Original Title

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

English Title

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

Type

Paper in proceedings (conference paper)

Original Abstract

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.

English abstract

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.

Keywords

MRI, debiasing, compressed sensing

Key words in English

MRI, debiasing, compressed sensing

Authors

DAŇKOVÁ, M.; RAJMIC, P.

RIV year

2017

Released

29.09.2016

Publisher

Springer

Location

Berlin

Book

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

ISBN

1352-8661

Periodical

MAGNETIC RESONANCE MATERIALS IN PHYSICS BIOLOGY AND MEDICINE

Volume

29

Number

Supplement 1

State

United States of America

Pages from

200

Pages to

201

Pages count

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"
}