Novel Deep Learning Denoising Enhances Image Quality and Lowers Radiation Exposure in Interventional Bronchial Artery Embolization Cone Beam CT.
Journal:
Academic radiology
PMID:
37989681
Abstract
OBJECTIVES: In interventional bronchial artery embolization (BAE), periprocedural cone beam CT (CBCT) improves guiding and localization. However, a trade-off exists between 6-second runs (high radiation dose and motion artifacts, but low noise) and 3-second runs (vice versa). This study aimed to determine the efficacy of an advanced deep learning denoising (DLD) technique in mitigating the trade-offs related to radiation dose and image quality during interventional BAE CBCT.
Authors
Keywords
Adult
Aged
Artifacts
Bronchial Arteries
Cone-Beam Computed Tomography
Deep Learning
Embolization, Therapeutic
Female
Humans
Male
Middle Aged
Radiation Dosage
Radiation Exposure
Radiographic Image Interpretation, Computer-Assisted
Radiography, Interventional
Retrospective Studies
Signal-To-Noise Ratio