Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm.

Journal: Journal of thoracic imaging

Published Date: May 1, 2025

Abstract

PURPOSE: To evaluate the accuracy of ultra-low dose (ULD) chest computed tomography (CT), with a radiation exposure equivalent to a 2-view chest x-ray, for pulmonary nodule detection using deep learning image reconstruction (DLIR).

Authors

Wesley Bocquet

Department of Radiology.
Roger Bouzerar

Medical Image Processing Unit, Amiens University Hospital, Amiens, France.
Géraldine François

Department of Pneumology and Transplantation, Amiens University Hospital, Amiens, France.
Antoine Leleu

Department of Radiology.
Cédric Renard

Department of Radiology, Amiens University Hospital, Amiens Cedex, France.

Keywords

Adult Aged Aged, 80 and over Algorithms Cross-Sectional Studies Deep Learning Female Humans Lung Lung Neoplasms Male Middle Aged Multiple Pulmonary Nodules Prospective Studies Radiation Dosage Radiographic Image Interpretation, Computer-Assisted Radiography, Thoracic Sensitivity and Specificity Solitary Pulmonary Nodule Tomography, X-Ray Computed

External Resources

View on PubMed Access via DOI PubMed (39267547)

Detection of Pulmonary Nodules on Ultra-low Dose Chest Computed Tomography With Deep-learning Image Reconstruction Algorithm.

Abstract

Authors

Keywords

External Resources

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