Blind Sparsity Based Motion Estimation and Correction Model for Arbitrary MRI Sampling Trajectories

Anita Möller; Marco Maass; Tim Jeldrik Parbs; Alfred Mertins

Blind Sparsity Based Motion Estimation and Correction Model for Arbitrary MRI Sampling Trajectories

Anita Möller, Marco Maass, Tim Jeldrik Parbs, Alfred Mertins

Institut für Signalverarbeitung

Abstract

A blind retrospective MRI motion estimation and compensation algorithm is designed for arbitrary sampling trajectories. Using the idea of natural images being sparsely representable, the algorithm is based on motion estimation between a motion corrupted image and it’s sparse representative. Therefore, rigid motion models are designed and used in gradient descent methods for image quality optimization. As the motion estimation and compensation work on arbitrary real valued sampling coordinates, the algorithm is capable for all trajectories. Image reconstruction is performed by computationally efficient gridding. The exact motion estimation results are shown for PROPELLER and radial trajectory simulation.

Originalsprache	Englisch
Publikationsstatus	Veröffentlicht - 01.05.2019
Veranstaltung	Annual Meeting of the International Society of Magnetic Resonance in Medicine 2019 - Palais des congrès de Montréal, Montréal, Kanada Dauer: 11.05.2019 → 16.01.2021

Tagung, Konferenz, Kongress

Tagung, Konferenz, Kongress	Annual Meeting of the International Society of Magnetic Resonance in Medicine 2019
Kurztitel	ISMRM 2019
Land/Gebiet	Kanada
Ort	Montréal
Zeitraum	11.05.19 → 16.01.21

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https://archive.ismrm.org/2019/4496.html

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title = "Blind Sparsity Based Motion Estimation and Correction Model for Arbitrary MRI Sampling Trajectories",

abstract = "A blind retrospective MRI motion estimation and compensation algorithm is designed for arbitrary sampling trajectories. Using the idea of natural images being sparsely representable, the algorithm is based on motion estimation between a motion corrupted image and it{\textquoteright}s sparse representative. Therefore, rigid motion models are designed and used in gradient descent methods for image quality optimization. As the motion estimation and compensation work on arbitrary real valued sampling coordinates, the algorithm is capable for all trajectories. Image reconstruction is performed by computationally efficient gridding. The exact motion estimation results are shown for PROPELLER and radial trajectory simulation.",

author = "Anita M{\"o}ller and Marco Maass and Parbs, {Tim Jeldrik} and Alfred Mertins",

year = "2019",

month = may,

day = "1",

language = "English",

note = "Annual Meeting of the International Society of Magnetic Resonance in Medicine 2019<br/>, ISMRM 2019 ; Conference date: 11-05-2019 Through 16-01-2021",

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TY - CONF

T1 - Blind Sparsity Based Motion Estimation and Correction Model for Arbitrary MRI Sampling Trajectories

AU - Möller, Anita

AU - Maass, Marco

AU - Parbs, Tim Jeldrik

AU - Mertins, Alfred

PY - 2019/5/1

Y1 - 2019/5/1

N2 - A blind retrospective MRI motion estimation and compensation algorithm is designed for arbitrary sampling trajectories. Using the idea of natural images being sparsely representable, the algorithm is based on motion estimation between a motion corrupted image and it’s sparse representative. Therefore, rigid motion models are designed and used in gradient descent methods for image quality optimization. As the motion estimation and compensation work on arbitrary real valued sampling coordinates, the algorithm is capable for all trajectories. Image reconstruction is performed by computationally efficient gridding. The exact motion estimation results are shown for PROPELLER and radial trajectory simulation.

AB - A blind retrospective MRI motion estimation and compensation algorithm is designed for arbitrary sampling trajectories. Using the idea of natural images being sparsely representable, the algorithm is based on motion estimation between a motion corrupted image and it’s sparse representative. Therefore, rigid motion models are designed and used in gradient descent methods for image quality optimization. As the motion estimation and compensation work on arbitrary real valued sampling coordinates, the algorithm is capable for all trajectories. Image reconstruction is performed by computationally efficient gridding. The exact motion estimation results are shown for PROPELLER and radial trajectory simulation.

M3 - Conference Papers

T2 - Annual Meeting of the International Society of Magnetic Resonance in Medicine 2019<br/>

Y2 - 11 May 2019 through 16 January 2021

ER -

Blind Sparsity Based Motion Estimation and Correction Model for Arbitrary MRI Sampling Trajectories

Abstract

Tagung, Konferenz, Kongress

UN SDGs

Dokumente

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