Movement correction in DCE-MRI through windowed and reconstruction dynamic mode decompositionReport as inadecuate

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Machine Vision and Applications

, Volume 28, Issue 3–4, pp 393–407

First Online: 06 April 2017Received: 26 May 2016Revised: 02 January 2017Accepted: 14 March 2017DOI: 10.1007-s00138-017-0835-5

Cite this article as: Tirunagari, S., Poh, N., Wells, K. et al. Machine Vision and Applications 2017 28: 393. doi:10.1007-s00138-017-0835-5


Images of the kidneys using dynamic contrast-enhanced magnetic resonance renography DCE-MRR contains unwanted complex organ motion due to respiration. This gives rise to motion artefacts that hinder the clinical assessment of kidney function. However, due to the rapid change in contrast agent within the DCE-MR image sequence, commonly used intensity-based image registration techniques are likely to fail. While semi-automated approaches involving human experts are a possible alternative, they pose significant drawbacks including inter-observer variability, and the bottleneck introduced through manual inspection of the multiplicity of images produced during a DCE-MRR study. To address this issue, we present a novel automated, registration-free movement correction approach based on windowed and reconstruction variants of dynamic mode decomposition WR-DMD. Our proposed method is validated on ten different healthy volunteers’ kidney DCE-MRI data sets. The results, using block-matching-block evaluation on the image sequence produced by WR-DMD, show the elimination of \99\%\ of mean motion magnitude when compared to the original data sets, thereby demonstrating the viability of automatic movement correction using WR-DMD.

KeywordsDMD W-DMD R-DMD WR-DMD DCE-MRI Movement correction Electronic supplementary materialThe online version of this article doi:10.1007-s00138-017-0835-5 contains supplementary material, which is available to authorized users.

Santosh Tirunagari and Norman Poh have benefited from the Medical Research Council MRC funded project ‘Modelling the Progression of Chronic Kidney Disease’ under the grant number R-M023281-1.

Author: Santosh Tirunagari - Norman Poh - Kevin Wells - Miroslaw Bober - Isky Gorden - David Windridge



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