ArticleJournal of imaging2026
A Task-Prompt-Guided Dual-Decoder Framework with Large Language Models for Multi-Phase Contrast CT Synthesis from Non-Contrast CT.
Article in Journal of imaging, 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
Synthesizing multi-phase contrast-enhanced CT images from non-contrast CT (NCCT) may provide complementary phase-specific cues for preliminary assessment. After rigorous clinical validation, such images could provide clinical decision support or aid diagnostic triage by identifying cases that warrant further acquired contrast-enhanced CT (CECT) work-up; they are not intended to replace acquired CECT. Arterial-phase (ART) and portal-venous-phase (PV) images share anatomical structures but exhibit distinct enhancement patterns and intensity distributions, which makes simultaneous multi-phase synthesis challenging for conventional single-decoder models. We propose a task-prompt-guided dual-decoder framework that combines a shared Swin Transformer encoder, two phase-specific decoders, learnable task prompts initialized from Qwen3-8B semantic representations, two phase-specific adversarial discriminators, and an independent ART/PV domain classifier. The shared encoder extracts phase-invariant anatomical features, whereas the two decoders independently model ART- and PV-specific enhancement. The Qwen3-derived prompt vectors provide phase-aware initialization and subsequently adapt through prompt-feature interaction at the bottleneck. Experiments on a single-center dataset of 86 patients show improved whole-image and lesion-focused PSNR, SSIM, MSE, and PCC relative to Pix2pix and MedGAN. Controlled ablation experiments indicate the benefits of Qwen3-based prompt initialization, subsequent prompt adaptation, and correct prompt-decoder correspondence; a reduced baseline additionally assesses the combined removal of LLTP and ART/PV domain classification. In a downstream slice-level four-class focal liver lesion classification experiment, synthetic multiphase input improved accuracy from 71.65% with NCCT alone to 85.04%, compared with 91.34% for acquired multiphase CT. These findings provide a proof of concept for LLM-guided multi-phase CT synthesis, although external validation, clinically oriented safety assessment, and reader studies remain necessary before clinical use for decision support or diagnostic triage.
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