aceInfluence of dominant sequence artifacts on generative AI for virtual contrast-enhancement in breast MRI
Virtual contrast-enhanced (vCE) breast MRI is a deep learning approach for generating contrast-like images from non-contrast acquisitions. As diffusion-weighted imaging (DWI) has emerged as a major contributor to enhancement synthesis, understanding the influence of DWI-related image artifacts is important for the robustness and potential clinical implementation of AI-based contrast-free imaging approaches. We evaluated the robustness of a pix2pix-based vCE synthesis model to artifact-contaminated DWI during tra…
In 61 test lesions, the lesion-level error of AI-generated virtual contrast breast MRI rose with the share of slices showing severe signal-loss artifacts (ρ = 0.478 in 12 lesions), so poor-quality input scans can distort lesion depiction.
Correlation analyses were exploratory with no multiplicity correction, the key signal-loss finding rested on only 12 lesions, and reader agreement was low.
Evidence
- Peer-reviewedBMC Medical Imaging2026-11-03
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Truvace Impact Record TRV-2026-2369, v1: “Influence of dominant sequence artifacts on generative AI for virtual contrast-enhancement in breast MRI.” Truvace, 2026-11-04. /record/TRV-2026-2369 (accessed at citation time). sha256 8eb4322b3d91e1f1…
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