Deep learning-based scan range optimization can reduce radiation exposure in coronary CT angiography.

ABSTRACT:

Objectives

Cardiac computed tomography (CT) is essential in diagnosing coronary heart disease. However, a disadvantage is the associated radiation exposure to the patient which depends in part on the scan range. This study aimed to develop a deep neural network to optimize the delimitation of scan ranges in CT localizers to reduce the radiation dose.

Methods

On a retrospective training cohort of 1507 CT localizers randomly selected from calcium scoring and angiography scans and acquired between 2010 and 2017, optimized scan ranges were delimited by two radiologists in consensus. A neural network was trained to reproduce the scan ranges and was tested on two randomly selected and independent validation cohorts: an internal cohort of 233 CT localizers (January 2018-June 2020)

SUBMITTER: Demircioglu A 

PROVIDER: S-EPMC10791769 | biostudies-literature | 2024 Jan

REPOSITORIES: biostudies-literature