Abstract
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.
Highlights
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) renography is a promising technique for functional assessment of the kidney because it avoids the need for ionising radiation
Our implementation is novel in the sense that it uses windowed-Dynamic mode decomposition (DMD) on dynamic image sequences for the first time to compensate for motion artefacts by producing lowrank images as W-DMD component-1
Utilising the pipeline of W-DMD and Reconstruction from DMD modes (R-DMD) we introduce WR-DMD for the first time to carry out movement correction in medical image sequences in a manner that is both extremely efficient and completely data driven
Summary
Dynamic contrast-enhanced magnetic resonance imaging (DCE-MRI) renography is a promising technique for functional assessment of the kidney because it avoids the need for ionising radiation. The expert manually delineates the kidney region of interest (ROI), forming the ‘template’ Using this template, the registration of the kidneys is conducted across the DCE-MRI sequence using contrast invariant similarity matching. DMD has gained significant applications in various fields [2,3,16], including for detecting spoof samples from facial authentication video data sets [33] and for detecting spoofed finger-vein images [31] The advantage of this method is its ability to identify regions of dominant motion in an image sequence in a completely data-driven manner without relying on any prior assumptions about the patterns of behaviour within the data. It is potentially well-suited to analyse a wide variation of blood flow and filtration patterns seen in renography pathology
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