Abstract
At present, registration-based quantification of bowel motility from dynamic MRI is limited to breath-hold studies. Here we validate a dual-registration technique robust to respiratory motion for the assessment of small bowel and colonic motility. Small bowel datasets were acquired in breath-hold and free-breathing in 20 healthy individuals. A pre-processing step using an iterative registration of the low rank component of the data was applied to remove respiratory motion from the free breathing data. Motility was then quantified with an existing optic-flow (OF) based registration technique to form a dual-stage approach, termed Dual Registration of Abdominal Motion (DRAM). The benefit of respiratory motion correction was assessed by (1) assessing the fidelity of automatically propagated segmental regions of interest (ROIs) in the small bowel and colon and (2) comparing parametric motility maps to a breath-hold ground truth. DRAM demonstrated an improved ability to propagate ROIs through free-breathing small bowel and colonic motility data, with median error decreased by 90% and 55%, respectively. Comparison between global parametric maps showed high concordance between breath-hold data and free-breathing DRAM. Quantification of segmental and global motility in dynamic MR data is more accurate and robust to respiration when using the DRAM approach.
Highlights
Artefacts and spatial misalignments caused by respiratory motion represent a major challenge to medical image acquisition and analysis of time series data (Rohlfing et al 2004, McClelland et al 2013)
Several solutions have been introduced in the case of dynamic contrast enhanced (DCE) data with non-rigid deformations and with specific considerations made for the changes in intensity (Melbourne et al 2011, Filipovic et al 2011, Wollny et al 2012)
We introduced Robust Data Decomposition Registration (RDDR), a novel technique using Robust Principal Component Analysis (RPCA) to separate the intensity changes from the motion during DCE acquisition (Hamy et al 2014)
Summary
Artefacts and spatial misalignments caused by respiratory motion represent a major challenge to medical image acquisition and analysis of time series data (Rohlfing et al 2004, McClelland et al 2013). Prospective motion correction schemes account for motion directly during acquisition and are predominately used with tracking devices in neuroimaging, or diaphragmatic navigators in cardiac imaging These techniques require the use of tracking data during acquisition (Maclaren et al 2013) and commonly correct only for rigid motion. We introduced Robust Data Decomposition Registration (RDDR), a novel technique using Robust Principal Component Analysis (RPCA) to separate the (sparse) intensity changes from the (low rank) motion during DCE acquisition (Hamy et al 2014). This advance is of particular interest to associated applications where respiratory motion is a limitation. We show that RDDR can be used as a preprocessing step to filter out respiratory motion, with no effect on measurements of peristalsis
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