Deep learning-based fully automated Z-axis coverage range definition from scout scans to eliminate overscanning in chest CT imaging.

ABSTRACT:

Background

Despite the prevalence of chest CT in the clinic, concerns about unoptimized protocols delivering high radiation doses to patients still remain. This study aimed to assess the additional radiation dose associated with overscanning in chest CT and to develop an automated deep learning-assisted scan range selection technique to reduce radiation dose to patients.

Results

A significant overscanning range (31 ± 24) mm was observed in clinical setting for over 95% of the cases. The average Dice coefficient for lung segmentation was 0.96 and 0.97 for anterior-posterior (AP) and lateral projections, respectively. By considering the exact lung coverage as the ground truth, and AP and lateral projections as input, The DL-based approach resulted in errors of 0.08 ± 1.46 and 

SUBMITTER: Salimi Y 

PROVIDER: S-EPMC8572075 | biostudies-literature | 2021 Nov

REPOSITORIES: biostudies-literature