Keywords: Machine Learning/Artificial Intelligence, Analysis/Processing, Stomach, Motility, AI
Motivation: Evaluation of gastric motility patterns and disorders is mostly dependent on external interventions (gastric manometry) or methods involving radioactive radiation (scintigraphy).
Goal(s): The goal was to establish a non-invasive and fast MRI protocol to overcome these limitations.
Approach: Using FLASH 2 real-time MR imaging, three sagittal slices with high frame rates of the antrum were acquired simultaneously, segmented using a trained artificial intelligence, a human-in-the-loop protocol and evaluated regarding frequency, velocity, and amplitude.
Results: Integration into standard DICOM software OsiriX, allowed an easy learnable workflow and fast acquisition of the peristaltic status of the antrum, offering various insights in physiology and pathophysiology.
Impact: FLASH 2 real-time MRI has been shown to be suitable for visualizing gastric motility of the stomach in fed state. Together with specifically trained artificial intelligence this could speed up the process to evaluate the peristaltic status of the stomach.
How to access this content:
For one year after publication, abstracts and videos are only open to registrants of this annual meeting. Registrants should use their existing login information. Non-registrant access can be purchased via the ISMRM E-Library.
After one year, current ISMRM & ISMRT members get free access to both the abstracts and videos. Non-members and non-registrants must purchase access via the ISMRM E-Library.
After two years, the meeting proceedings (abstracts) are opened to the public and require no login information. Videos remain behind password for access by members, registrants and E-Library customers.
Keywords