Evidence map›Paper›PMID 42645964›Full record

ArticleJournal of imaging2026

A Task-Prompt-Guided Dual-Decoder Framework with Large Language Models for Multi-Phase Contrast CT Synthesis from Non-Contrast CT.

Liang Lyu, Hao Sun, Jiaqing Liu, Fang Wang, Lanfen Lin, Yen-Wei Chen

Abstract read
In one paragraph

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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1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

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3 · Its place in the literature

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4 · The record

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5 · Who and what money

Authors and funding

6 authors.

Liang LyuGraduate School of Information Science and Engineering, Ritsumeikan University, Ibaraki 567-8570, Osaka, Japan.
Hao SunGraduate School of Information Science and Engineering, Ritsumeikan University, Ibaraki 567-8570, Osaka, Japan.ORCID 0000-0001-8094-1991
Jiaqing LiuGraduate School of Information Science and Engineering, Ritsumeikan University, Ibaraki 567-8570, Osaka, Japan.
Fang WangDepartment of Radiology, SRRSH of School of Medicine, Zhejiang University, Hangzhou 310016, China.
Lanfen LinCollege of Artificial Intelligence, Zhejiang University, Hangzhou 310058, China.ORCID 0000-0003-4098-588X
Yen-Wei ChenGraduate School of Information Science and Engineering, Ritsumeikan University, Ibaraki 567-8570, Osaka, Japan.ORCID 0000-0002-5952-0188

Funding

Ministry of Education, Culture, Sports, Science and Technology 20KK0234; 21H03470; 20K21821
6 · The paper itself

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.

Indexed as

dual-decoder networklarge language modelmedical image synthesismulti-phase contrast CT synthesisnon-contrast CTtask prompt

Identifiers

PMID42645964
PMCPMC13514630

What OpenQuestion holds

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.