Performance of a Deep Learning Reconstruction Method on Clinical Chest-Abdomen-Pelvis Scans from a Dual-Layer Detector CT System.

ABSTRACT: Objective: The objective of this study was to compare the performance and robustness of a deep learning reconstruction method against established alternatives for soft tissue CT image reconstruction. Materials and Methods: Images were generated from portal venous phase chest-abdomen-pelvis CT scans (n = 99) acquired on a dual-layer spectral detector CT using filtered back projection, iterative model reconstruction (IMR), and deep learning reconstruction (DLR) with three parameter settings, namely 'standard', 'sharper', and 'smoother'. Experienced raters performed a quantitative assessment by considering attenuation stability and image noise levels in ten representative structures across all reconstruction methods, as well as a qualitative assessment using a four-point

SUBMITTER: Schuppert C 

PROVIDER: S-EPMC12473457 | biostudies-literature | 2025 Aug

REPOSITORIES: biostudies-literature