Reference-free learning-based similarity metric for motion compensation in cone-beam CT

H Huang1, J H Siewerdsen1,2,3, W Zbijewski1

  • 1Department of Biomedical Engineering, Johns Hopkins University, Baltimore, MD, United States of America.

Summary

A new deep learning metric, DL-VIF, effectively quantifies motion artifacts in cone-beam CT (CBCT) without a reference image. This method improves motion compensation accuracy and image quality in interventional procedures.

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