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A novel method for measuring bowel motility and velocity with dynamic magnetic resonance imaging in two and three dimensions.

David WillisDonnie CameronBahman KasmaiVassilios S VassiliouPaul N MalcolmGabriella Baio
Published in: NMR in biomedicine (2021)
Increasingly, dynamic magnetic resonance imaging (MRI) has potential as a noninvasive and accessible tool for diagnosing and monitoring gastrointestinal motility in healthy and diseased bowel. However, current MRI methods of measuring bowel motility have limitations: requiring bowel preparation or long acquisition times; providing mainly surrogate measures of motion; and estimating bowel-wall movement in just two dimensions. In this proof-of-concept study we apply a method that provides a quantitative measure of motion within the bowel, in both two and three dimensions, using existing, vendor-implemented MRI pulse sequences with minimal bowel preparation. This method uses a minimised cost function to fit linear vectors in the spatial and temporal domains. It is sensitised to the spatial scale of the bowel and aims to address issues relating to the low signal-to-noise in high-temporal resolution dynamic MRI scans, previously compensated for by performing thick-slice (10-mm) two-dimensional (2D) coronal scans. We applied both 2D and three-dimensional (3D) scanning protocols in two healthy volunteers. For 2D scanning, analysis yielded bi-modal velocity peaks, with a mean antegrade motion of 5.5 mm/s and an additional peak at ~9 mm/s corresponding to longitudinal peristalsis, as supported by intraoperative data from the literature. Furthermore, 3D scans indicated a mean forward motion of 4.7 mm/s, and degrees of antegrade and retrograde motion were also established. These measures show promise for the noninvasive assessment of bowel motility, and have the potential to be tuned to particular regions of interest and behaviours within the bowel.
Keyphrases
  • magnetic resonance imaging
  • contrast enhanced
  • computed tomography
  • diffusion weighted imaging
  • biofilm formation
  • high speed
  • air pollution
  • machine learning
  • artificial intelligence
  • pseudomonas aeruginosa