Application and assessment of a robust elastic motion correction algorithm to dynamic MRI

Herrmann, K-H; Wurdinger, S; Fischer, D R; Krumbein, I; Schmitt, M; Hermosillo, G; Chaudhuri, K; Krishnan, A; Salganicoff, M; Kaiser, W A; Reichenbach, J R (2007). Application and assessment of a robust elastic motion correction algorithm to dynamic MRI. European radiology, 17(1), pp. 259-64. Berlin: Springer 10.1007/s00330-006-0224-3

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The purpose of this study was to assess the performance of a new motion correction algorithm. Twenty-five dynamic MR mammography (MRM) data sets and 25 contrast-enhanced three-dimensional peripheral MR angiographic (MRA) data sets which were affected by patient motion of varying severeness were selected retrospectively from routine examinations. Anonymized data were registered by a new experimental elastic motion correction algorithm. The algorithm works by computing a similarity measure for the two volumes that takes into account expected signal changes due to the presence of a contrast agent while penalizing other signal changes caused by patient motion. A conjugate gradient method is used to find the best possible set of motion parameters that maximizes the similarity measures across the entire volume. Images before and after correction were visually evaluated and scored by experienced radiologists with respect to reduction of motion, improvement of image quality, disappearance of existing lesions or creation of artifactual lesions. It was found that the correction improves image quality (76% for MRM and 96% for MRA) and diagnosability (60% for MRM and 96% for MRA).

Item Type:

Journal Article (Original Article)

Division/Institute:

04 Faculty of Medicine > Department of Radiology, Neuroradiology and Nuclear Medicine (DRNN) > Institute of Diagnostic, Interventional and Paediatric Radiology

UniBE Contributor:

Fischer, Dorothee Rita

ISSN:

0938-7994

ISBN:

16612550

Publisher:

Springer

Language:

English

Submitter:

Factscience Import

Date Deposited:

04 Oct 2013 14:58

Last Modified:

05 Dec 2022 14:17

Publisher DOI:

10.1007/s00330-006-0224-3

PubMed ID:

16612550

Web of Science ID:

000243396700030

BORIS DOI:

10.48350/24829

URI:

https://boris.unibe.ch/id/eprint/24829 (FactScience: 53030)

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