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Reliability of Artificial Intelligence-Based Cone Beam Computed Tomography Integration with Digital Dental Images
Published on: February 23, 2024
A joint registration and extrapolation method for cone beam computed tomography volume of interest imaging
Daniel Punzet1,2, Robert Frysch1,2, Tim Pfeiffer1,2
1Institute of Medical Technology, Otto-von-Guericke-University Magdeburg, Magdeburg, Germany.
None:
The high patient dose applied during acquisition of C-arm based cone beam computed tomography (CBCT) scans limits the applicability of this imaging technique. Acquisition of only a region-of-interest can decrease the dose significantly. However, reconstruction from such truncated projection data can lead to severe artifacts in the tomographic image. For typical usages of C-arm based CBCT however, there often exists a prior computed tomography or CBCT scan of the patient. The proposed method enables the incorporation of such priors into the reconstruction process by jointly registering and extrapolating the prior to the limited data acquisition scenario by maximizing consistency conditions across thusly extended projections. Results show improved image quality over simple heuristic extrapolation methods typically used in practice. The proposed method can be regarded as a novel hybrid registration and extrapolation algorithm similar to established digitally reconstructed radiographs-based registration methods, but unlike these, working on mutually disjoint regions of the projection data.
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