ArticleJournal of the Korean Society of Radiology2026
Additional Dose Reduction Potential of Vendor-Agnostic Deep Learning Model: A Phantom Study.
Article in Journal of the Korean Society of Radiology, 2026. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.
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Abstract
Purpose: To evaluate the additional dose reduction potential (DRP) of a vendor-agnostic deep learning model (DLM, ClariCT.AI) applied to iterative reconstruction (IR) methods from two CT vendors. Materials and Methods: CT images of a multi-sized image quality phantom (Mercury v4.0) were obtained using two CT scanners from different vendors at six dose levels: 6%, 12%, 25%, 50%, 100%, and 200% of the reference dose (computerized tomography dose index volume; CTDIvol = 12 mGy). Images were reconstructed using three IR methods (ADMIRE, iDose Results: Mean DRPs were 93.2% for ADMIRE, 90.0% for iDose Conclusion: Applying a vendor-agnostic DLM to IR images enabled substantial radiation dose reduction while preserving image quality across different CT systems.
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