Retrospective Blind MR Image Recovery with Parametrized Motion Models

Tim J. Parbs; Anita Mӧller; Alfred Mertins

doi:10.1007/978-3-658-25326-4_30

Retrospective Blind MR Image Recovery with Parametrized Motion Models

Tim J. Parbs^*, Anita Mӧller, Alfred Mertins

^*Corresponding author for this work

Institute of Signal Processing

Abstract

In this paper, we present an alternating retrospective MRI reconstruction framework based on a parametrized motion model. An image recovery algorithm promoting sparsity is used in tandem with a numeric parameter search to iteratively reconstruct a sharp image. Additionally, we introduce a multiresolution strategy to restrict the numeric complexity. This algorithm is then tested in conjunction with a simple motion model on simulated data and provides robust and fast reconstruction of sharp images from severely corrupted k-spaces.

Original language	English
Title of host publication	Bildverarbeitung für die Medizin 2019
Editors	Heinz Handels, Thomas M. Deserno, Andreas Maier, Klaus Hermann Maier-Hein, Christoph Palm, Thomas Tolxdorff
Number of pages	6
Publisher	Springer Vieweg, Wiesbaden
Publication date	07.02.2019
Pages	140-145
ISBN (Print)	978-3-658-25325-7
ISBN (Electronic)	978-3-658-25326-4
DOIs	https://doi.org/10.1007/978-3-658-25326-4_30
Publication status	Published - 07.02.2019
Event	Workshop on Bildverarbeitung fur die Medizin 2019 - Lübeck, Germany Duration: 17.03.2019 → 19.03.2019 Conference number: 224899

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10.1007/978-3-658-25326-4_30

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title = "Retrospective Blind MR Image Recovery with Parametrized Motion Models",

abstract = "In this paper, we present an alternating retrospective MRI reconstruction framework based on a parametrized motion model. An image recovery algorithm promoting sparsity is used in tandem with a numeric parameter search to iteratively reconstruct a sharp image. Additionally, we introduce a multiresolution strategy to restrict the numeric complexity. This algorithm is then tested in conjunction with a simple motion model on simulated data and provides robust and fast reconstruction of sharp images from severely corrupted k-spaces.",

author = "Parbs, {Tim J.} and Anita Mӧller and Alfred Mertins",

note = "Funding Information: Acknowledgement. This work has been supported by the German Research Foundation under Grant No. ME 1170/11-1. Publisher Copyright: {\textcopyright} 2019, Springer Berlin Heidelberg. All rights reserved. Copyright: Copyright 2019 Elsevier B.V., All rights reserved.; Workshop on Bildverarbeitung fur die Medizin 2019 ; Conference date: 17-03-2019 Through 19-03-2019",

year = "2019",

month = feb,

day = "7",

doi = "10.1007/978-3-658-25326-4_30",

language = "English",

isbn = "978-3-658-25325-7",

series = "Informatik aktuell",

publisher = "Springer Vieweg, Wiesbaden",

pages = "140--145",

editor = "Handels, {Heinz } and {M. Deserno}, {Thomas } and Maier, {Andreas } and {Hermann Maier-Hein}, {Klaus } and Palm, {Christoph } and Tolxdorff, {Thomas }",

booktitle = "Bildverarbeitung f{\"u}r die Medizin 2019",

}

Parbs, TJ, Mӧller, A & Mertins, A 2019, Retrospective Blind MR Image Recovery with Parametrized Motion Models. in H Handels, T M. Deserno, A Maier, K Hermann Maier-Hein, C Palm & T Tolxdorff (eds), Bildverarbeitung für die Medizin 2019 . Informatik aktuell, Springer Vieweg, Wiesbaden, pp. 140-145, Workshop on Bildverarbeitung fur die Medizin 2019 , Lübeck, Schleswig-Holstein, Germany, 17.03.19. https://doi.org/10.1007/978-3-658-25326-4_30

Retrospective Blind MR Image Recovery with Parametrized Motion Models. / Parbs, Tim J.; Mӧller, Anita; Mertins, Alfred.
Bildverarbeitung für die Medizin 2019 . ed. / Heinz Handels; Thomas M. Deserno; Andreas Maier; Klaus Hermann Maier-Hein; Christoph Palm; Thomas Tolxdorff. Springer Vieweg, Wiesbaden, 2019. p. 140-145 (Informatik aktuell).

Research output: Chapters in Books/Reports/Conference Proceedings › Conference contribution › peer-review

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AU - Mӧller, Anita

AU - Mertins, Alfred

N1 - Conference code: 224899

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N2 - In this paper, we present an alternating retrospective MRI reconstruction framework based on a parametrized motion model. An image recovery algorithm promoting sparsity is used in tandem with a numeric parameter search to iteratively reconstruct a sharp image. Additionally, we introduce a multiresolution strategy to restrict the numeric complexity. This algorithm is then tested in conjunction with a simple motion model on simulated data and provides robust and fast reconstruction of sharp images from severely corrupted k-spaces.

AB - In this paper, we present an alternating retrospective MRI reconstruction framework based on a parametrized motion model. An image recovery algorithm promoting sparsity is used in tandem with a numeric parameter search to iteratively reconstruct a sharp image. Additionally, we introduce a multiresolution strategy to restrict the numeric complexity. This algorithm is then tested in conjunction with a simple motion model on simulated data and provides robust and fast reconstruction of sharp images from severely corrupted k-spaces.

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BT - Bildverarbeitung für die Medizin 2019

A2 - Handels, Heinz

A2 - M. Deserno, Thomas

A2 - Maier, Andreas

A2 - Hermann Maier-Hein, Klaus

A2 - Palm, Christoph

A2 - Tolxdorff, Thomas

PB - Springer Vieweg, Wiesbaden

T2 - Workshop on Bildverarbeitung fur die Medizin 2019

Y2 - 17 March 2019 through 19 March 2019

ER -

Retrospective Blind MR Image Recovery with Parametrized Motion Models

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