Stomach Geometry Reconstruction Using Serosal Transmitting Coils and Magnetic Source Localization

Objective: Bioelectric slow waves (SWs) are a key regulator of gastrointestinal motility, and disordered SW activity has been linked to motility disorders. There is currently a lack of practical options for the acquisition of the 3D stomach geometry during research studies when medical imaging is challenging. Accurately recording the geometry of the stomach and co-registering electrode and sensor positions would provide context for in-vivo studies and aid the development of non-invasive methods of gastric SW assessment. Methods: A stomach geometry reconstruction method based on the localization of transmitting coils placed on the gastric serosa was developed. The positions and orientations of the coils, which represented boundary points and surface-normal vectors, were estimated using a magnetic source localization algorithm. Coil localization results were then used to generate surface models. The reconstruction method was evaluated against four 3D-printed anatomically realistic human stomach models and applied in a proof of concept in-vivo pig study. Results: Over ten repeated reconstructions, average Hausdorff distance and average surface-normal vector error values were 4.7 +/- 0.2 mm and 18.7 +/- 0.7 degrees for the whole stomach, and 3.6 +/- 0.2 mm and 14.6 +/- 0.6 degrees for the corpus. Furthermore, mean intra-array localization error was 1.4 +/- 1.1 mm for the benchtop experiment and 1.7 +/- 1.6 mm in-vivo. Conclusion and Significance: Results demonstrated that the proposed reconstruction method is accurate and feasible. The stomach models generated by this method, when co-registered with electrode and sensor positions, could enable the investigation and validation of novel inverse analysis techniques.

Automated summary

Bioelectric slow waves regulate gastrointestinal motility, and disordered activity is linked to motility disorders. A method for reconstructing 3D stomach geometry from coil localization was developed and validated using anatomically realistic models and in-vivo pig studies. The proposed method could enable investigation of novel analysis techniques.