Image quality improvement in bowtie-filter-equipped cone-beam CT using a dual-domain neural network.

Journal: Medical physics

Published Date: Sep 5, 2023

Abstract

BACKGROUND: The bowtie-filter in cone-beam CT (CBCT) causes spatially nonuniform x-ray beam often leading to eclipse artifacts in the reconstructed image. The artifacts are further confounded by the patient scatter, which is therefore patient-dependent as well as system-specific.

Authors

Sungho Yun

Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
Uijin Jeong

Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
Donghyeon Lee

Department of Nuclear and Quantum Engineering, Korea Advanced Institute of Science and Technology (KAIST), Daejeon, South Korea.
Hyeongseok Kim

KAIST Institute for Artificial Intelligence, Korea Advanced Institute of Science and Technology, Daejeon, Korea.
Seungryong Cho

Keywords

Algorithms Artifacts Cone-Beam Computed Tomography Humans Image Processing, Computer-Assisted Neural Networks, Computer Phantoms, Imaging Quality Improvement X-Rays

External Resources

View on PubMed Access via DOI PubMed (37669510)

Image quality improvement in bowtie-filter-equipped cone-beam CT using a dual-domain neural network.

Abstract

Authors

Keywords

External Resources

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