aceImpact of deep learning image reconstruction on CT radiomics reproducibility in patients with pulmonary nodules
Objective Ensuring CT feature reproducibility across imaging protocols and reconstruction algorithms is essential for the generalizability of radiomics applications. This study aimed to evaluate the reproducibility of CT radiomic features in pulmonary nodules using deep learning image reconstruction (DLIR) compared with conventional reconstruction techniques under different radiation doses. Materials and methods We included 94 patients between March and June 2023. Standard- and low-dose CT images were scanned to…
Deep learning image reconstruction improved the reproducibility of radiomic features in pulmonary nodules, making it particularly suitable for low-dose CT protocols.
Feature robustness tends to decrease at higher deep learning reconstruction strengths, and nodule characteristics significantly influence variability.
The inter-dose reproducibility difference between DLIR-H, ASIR-V-50%, and FBP was not statistically significant, and robustness declines at higher DLIR strengths.
Evidence
- Peer-reviewedInsights into Imaging2026-10-13
How should this claim be treated?
Truvace Impact Record TRV-2026-2043, v1: “Impact of deep learning image reconstruction on CT radiomics reproducibility in patients with pulmonary nodules.” Truvace, 2026-10-20. /record/TRV-2026-2043 (accessed at citation time). sha256 80b732c2cab8aa1f…
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