Motion artifacts can degrade image quality in dental cone-beam CT and complicate diagnosis. In some cases, an exam retake is necessary, resulting in additional radiation exposure for the patient, without any guarantee of improved image quality. Therefore, motion compensation methods play a crucial role. Many methods are time-consuming since they require several reconstructions. We propose a very efficient method that requires only two partial-angle reconstructions. It assumes that the patient remains still during the acquisition, except for a short interval. In this situation, two motion-free partial-angle reconstructions, one before and one after patient motion, can be reconstructed. Motion compensation is achieved by registering forward projections of the two volumes. To enhance the robustness of the registration step, we simulate an extended angular range covered by the two partial volumes using a conditioned U-Net trained on a target-specific dataset. Qualitative analysis shows that we can significantly reduce the appearance of motion artifacts even in the case of challenging motion patterns.

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Extended Partial Angle Based Motion Compensation for Dental CBCT

  • Cristina Sarti,
  • Mikhail Mikerov,
  • Claudio Landi

摘要

Motion artifacts can degrade image quality in dental cone-beam CT and complicate diagnosis. In some cases, an exam retake is necessary, resulting in additional radiation exposure for the patient, without any guarantee of improved image quality. Therefore, motion compensation methods play a crucial role. Many methods are time-consuming since they require several reconstructions. We propose a very efficient method that requires only two partial-angle reconstructions. It assumes that the patient remains still during the acquisition, except for a short interval. In this situation, two motion-free partial-angle reconstructions, one before and one after patient motion, can be reconstructed. Motion compensation is achieved by registering forward projections of the two volumes. To enhance the robustness of the registration step, we simulate an extended angular range covered by the two partial volumes using a conditioned U-Net trained on a target-specific dataset. Qualitative analysis shows that we can significantly reduce the appearance of motion artifacts even in the case of challenging motion patterns.